1
|
Chen Y, Wang L, Wang Y, Fang Y, Shen W, Si Y, Zheng X, Zeng S. Integrative Analysis of Histone Acetylation Regulated CYP4F12 in Esophageal Cancer Development. Drug Metab Dispos 2024; 52:813-823. [PMID: 38811154 DOI: 10.1124/dmd.124.001674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/27/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Current therapeutic strategies for esophageal cancer (EC) patients have yielded limited improvements in survival rates. Recent research has highlighted the influence of drug metabolism enzymes on both drug response and EC development. Our study aims to identify specific drug metabolism enzymes regulated by histone acetylation and to elucidate its molecular and clinical features. CYP4F12 exhibited a notable upregulation subsequent to trichostatin A treatment as evidenced by RNA sequencing analysis conducted on the KYSE-150 cell line. The change in gene expression was associated with increased acetylation level of histone 3 K18 and K27 in the promoter. The regulation was dependent on p300. In silicon analysis of both The Cancer Genome Atlas esophageal carcinoma and GSE53624 dataset suggested a critical role of CYP4F12 in EC development, because CYP4F12 was downregulated in tumor tissues and predicted better disease-free survival. Gene ontology analysis has uncovered a robust correlation between CYP4F12 and processes related to cell migration, as well as its involvement in cytosine-mediated immune activities. Further investigation into the relationship between immune cells and CYP4F12 expression has indicated an increased level of B cell infiltration in samples with high CYP4F12 expression. CYP4F12 was also negatively correlated with the expression of inhibitory checkpoints. An accurate predictive nomogram model was established combining with clinical factors and CYP4F12 expression. In conclusion, CYP4F12 was crucial in EC development, and targeting CYP4F12 may improve the therapeutic efficacy of current treatment in EC patients. SIGNIFICANCE STATEMENT: CYP4F12 expression was downregulated in esophageal cancer (EC) patients and could be induced by trichostatin A. During EC development, CYP4F12 was linked to reduced cell migration and increased infiltration of B cells. CYP4F12 also is a biomarker as prognostic predictors and therapeutic guide in EC patients.
Collapse
Affiliation(s)
- Yanhong Chen
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Li Wang
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Yuchen Wang
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Yanyan Fang
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Wenyang Shen
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Yingxue Si
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Xiaoli Zheng
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| | - Su Zeng
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Cancer Center of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (Y.C., Y.W., Y.F., S.Z.); and Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China (L.W., W.S., Y.S., X.Z.)
| |
Collapse
|
2
|
Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
Collapse
Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
| |
Collapse
|
3
|
Mao W, Zhou T, Zhang F, Qian M, Xie J, Li Z, Shu Y, Li Y, Xu H. Pan-cancer single-cell landscape of drug-metabolizing enzyme genes. Pharmacogenet Genomics 2024:01213011-990000000-00063. [PMID: 38814173 DOI: 10.1097/fpc.0000000000000538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
OBJECTIVE Varied expression of drug-metabolizing enzymes (DME) genes dictates the intensity and duration of drug response in cancer treatment. This study aimed to investigate the transcriptional profile of DMEs in tumor microenvironment (TME) at single-cell level and their impact on individual responses to anticancer therapy. METHODS Over 1.3 million cells from 481 normal/tumor samples across 9 solid cancer types were integrated to profile changes in the expression of DME genes. A ridge regression model based on the PRISM database was constructed to predict the influence of DME gene expression on drug sensitivity. RESULTS Distinct expression patterns of DME genes were revealed at single-cell resolution across different cancer types. Several DME genes were highly enriched in epithelial cells (e.g. GPX2, TST and CYP3A5) or different TME components (e.g. CYP4F3 in monocytes). Particularly, GPX2 and TST were differentially expressed in epithelial cells from tumor samples compared to those from normal samples. Utilizing the PRISM database, we found that elevated expression of GPX2, CYP3A5 and reduced expression of TST was linked to enhanced sensitivity of particular chemo-drugs (e.g. gemcitabine, daunorubicin, dasatinib, vincristine, paclitaxel and oxaliplatin). CONCLUSION Our findings underscore the varied expression pattern of DME genes in cancer cells and TME components, highlighting their potential as biomarkers for selecting appropriate chemotherapy agents.
Collapse
Affiliation(s)
- Wei Mao
- Department of Laboratory Medicine/Research Centre of Clinical Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan
| | - Tao Zhou
- Department of Laboratory Medicine/Research Centre of Clinical Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan
| | - Feng Zhang
- Center for Precision Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang
| | - Maoxiang Qian
- Institute of Pediatrics and Department of Hematology and Oncology, National Children's Medical Center, Children's Hospital of Fudan University, Shanghai
| | - Jianqiang Xie
- Department of Medicine and Surgery, Sichan Second Veterans Hospital
| | - Zhengyan Li
- Department of Radiology, West China Hospital, Sichuan University
| | - Yang Shu
- Gastric Cancer Center, West China Hospital, Sichuan University
| | - Yuan Li
- Institute of Digestive Surgery, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Heng Xu
- Department of Laboratory Medicine/Research Centre of Clinical Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan
| |
Collapse
|
4
|
Karampuri A, Kundur S, Perugu S. Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling. Comput Biol Med 2024; 174:108433. [PMID: 38642491 DOI: 10.1016/j.compbiomed.2024.108433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/22/2024]
Abstract
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.
Collapse
Affiliation(s)
- Anush Karampuri
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Sunitha Kundur
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India.
| |
Collapse
|
5
|
Hong Y, Xu H, Liu Y, Zhu S, Tian C, Chen G, Zhu F, Tao L. DDID: a comprehensive resource for visualization and analysis of diet-drug interactions. Brief Bioinform 2024; 25:bbae212. [PMID: 38711369 DOI: 10.1093/bib/bbae212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/01/2024] [Accepted: 04/21/2024] [Indexed: 05/08/2024] Open
Abstract
Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.
Collapse
Affiliation(s)
- Yanfeng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuhong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Sisi Zhu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Chao Tian
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Gongxing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Affiliated Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| |
Collapse
|
6
|
Zhou Y, Chen Z, Yang M, Chen F, Yin J, Zhang Y, Zhou X, Sun X, Ni Z, Chen L, Lv Q, Zhu F, Liu S. FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. Brief Bioinform 2024; 25:bbae223. [PMID: 38742521 PMCID: PMC11091744 DOI: 10.1093/bib/bbae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.
Collapse
Affiliation(s)
- Yuan Zhou
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mengjie Yang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Fengyun Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xuheng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ziheng Ni
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lu Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Qun Lv
- Department of Respiratory, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shuiping Liu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| |
Collapse
|
7
|
Jimonet P, Druart C, Blanquet-Diot S, Boucinha L, Kourula S, Le Vacon F, Maubant S, Rabot S, Van de Wiele T, Schuren F, Thomas V, Walther B, Zimmermann M. Gut Microbiome Integration in Drug Discovery and Development of Small Molecules. Drug Metab Dispos 2024; 52:274-287. [PMID: 38307852 DOI: 10.1124/dmd.123.001605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/04/2024] Open
Abstract
Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.
Collapse
Affiliation(s)
- Patrick Jimonet
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Céline Druart
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stéphanie Blanquet-Diot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Lilia Boucinha
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Stephanie Kourula
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Françoise Le Vacon
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Maubant
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Sylvie Rabot
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Tom Van de Wiele
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Frank Schuren
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Vincent Thomas
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Bernard Walther
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| | - Michael Zimmermann
- Medicen Paris Région, Paris, France (P.J.); Pharmabiotic Research Institute, Narbonne, France (C.D.); UMR 454 MEDIS, Université Clermont Auvergne, Clermont-Ferrand, France (S.B.D.); Global Bioinformatics, Evotec ID, Lyon, France (L.B.); Preclinical Sciences & Translational Safety, JNJ Innovative Medicine, Beerse, Belgium (S.K.); Biofortis, Saint-Herblain, France (F.L.V.); Translational Pharmacology Department, Oncodesign Services, Dijon, France (S.M.); Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France (S.R.); Center of Microbial Ecology and Technology, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium (T.V.W.); TNO, Leiden, The Netherlands (F.S.); Lallemand Health Solutions, Blagnac, France (V.T.); Servier, Saclay, France (B.W.); and Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (M.Z.)
| |
Collapse
|
8
|
Chen B, Pan Z, Mou M, Zhou Y, Fu W. Is fragment-based graph a better graph-based molecular representation for drug design? A comparison study of graph-based models. Comput Biol Med 2024; 169:107811. [PMID: 38168647 DOI: 10.1016/j.compbiomed.2023.107811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/23/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024]
Abstract
Graph Neural Networks (GNNs) have gained significant traction in various sectors of AI-driven drug design. Over recent years, the integration of fragmentation concepts into GNNs has emerged as a potent strategy to augment the efficacy of molecular generative models. Nonetheless, challenges such as symmetry breaking and potential misrepresentation of intricate cycles and undefined functional groups raise questions about the superiority of fragment-based graph representation over traditional methods. In our research, we undertook a rigorous evaluation, contrasting the predictive prowess of eight models-developed using deep learning algorithms-across 12 benchmark datasets that span a range of properties. These models encompass established methods like GCN, AttentiveFP, and D-MPNN, as well as innovative fragment-based representation techniques. Our results indicate that fragment-based methodologies, notably PharmHGT, significantly improve model performance and interpretability, particularly in scenarios characterized by limited data availability. However, in situations with extensive training, fragment-based molecular graph representations may not necessarily eclipse traditional methods. In summation, we posit that the integration of fragmentation, as an avant-garde technique in drug design, harbors considerable promise for the future of AI-enhanced drug design.
Collapse
Affiliation(s)
- Baiyu Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Wei Fu
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China.
| |
Collapse
|
9
|
Yin J, Chen Z, You N, Li F, Zhang H, Xue J, Ma H, Zhao Q, Yu L, Zeng S, Zhu F. VARIDT 3.0: the phenotypic and regulatory variability of drug transporter. Nucleic Acids Res 2024; 52:D1490-D1502. [PMID: 37819041 PMCID: PMC10767864 DOI: 10.1093/nar/gkad818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.
Collapse
Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Nanxin You
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hui Ma
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Qingwei Zhao
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
10
|
Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
Collapse
Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
11
|
Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
Collapse
Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
12
|
Shen L, Sun X, Chen Z, Guo Y, Shen Z, Song Y, Xin W, Ding H, Ma X, Xu W, Zhou W, Che J, Tan L, Chen L, Chen S, Dong X, Fang L, Zhu F. ADCdb: the database of antibody-drug conjugates. Nucleic Acids Res 2024; 52:D1097-D1109. [PMID: 37831118 PMCID: PMC10768060 DOI: 10.1093/nar/gkad831] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.
Collapse
Affiliation(s)
- Liteng Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yu Guo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zheyuan Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yi Song
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wenxiu Xin
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Xinyue Ma
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Weiben Xu
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wanying Zhou
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Jinxin Che
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lili Tan
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Liangsheng Chen
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Siqi Chen
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Luo Fang
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
13
|
Zhang Y, Liu X, Li F, Yin J, Yang H, Li X, Liu X, Chai X, Niu T, Zeng S, Jia Q, Zhu F. INTEDE 2.0: the metabolic roadmap of drugs. Nucleic Acids Res 2024; 52:D1355-D1364. [PMID: 37930837 PMCID: PMC10767827 DOI: 10.1093/nar/gkad1013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 11/08/2023] Open
Abstract
The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.
Collapse
Affiliation(s)
- Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xingang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China
| | - Hao Yang
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xuedong Li
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xinyu Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Xu Chai
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Tianle Niu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Su Zeng
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Qingzhong Jia
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
| | - Feng Zhu
- School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China
- College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
14
|
Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
Collapse
Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
| |
Collapse
|
15
|
Fu T, Zeng S, Zheng Q, Zhu F. The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity. Drug Metab Dispos 2023; 51:1316-1323. [PMID: 37295948 DOI: 10.1124/dmd.123.001275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/27/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.
Collapse
Affiliation(s)
- Tingting Fu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Su Zeng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Qingchuan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China (F.Z.); School of Pharmaceutical Sciences, Jilin University, Changchun, China (T.F., Q.Z.); College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China (S.Z., F.Z.); and Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China (F.Z.)
| |
Collapse
|
16
|
Liang S, Zhao Y, Jin J, Qiao J, Wang D, Wang Y, Wei L. Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. Comput Biol Med 2023; 164:107238. [PMID: 37515874 DOI: 10.1016/j.compbiomed.2023.107238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/16/2023] [Accepted: 07/07/2023] [Indexed: 07/31/2023]
Abstract
Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.
Collapse
Affiliation(s)
- Sirui Liang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yanxi Zhao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ding Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
| |
Collapse
|
17
|
Liu X, Liu J, Fu B, Chen R, Jiang J, Chen H, Li R, Xing L, Yuan L, Chen X, Zhang J, Li H, Guo S, Guo F, Guo J, Liu Y, Qi Y, Yu B, Xu F, Li D, Liu Z. DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine. J Chem Inf Model 2023; 63:4948-4959. [PMID: 37486750 PMCID: PMC10428213 DOI: 10.1021/acs.jcim.3c00365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Indexed: 07/25/2023]
Abstract
Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.
Collapse
Affiliation(s)
- Xinyue Liu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
| | - Jinying Liu
- College
of Traditional Chinese Medicine, Chengde
Medical University, Chengde 067000, China
| | - Bangze Fu
- School
of Biomedicine, Beijing City University, Beijing 100094, China
| | - Ruzhen Chen
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
| | - Jianzhou Jiang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
- School
of Life Sciences, Hebei University, Baoding 071002, China
| | - He Chen
- School
of Life Sciences, Hebei University, Baoding 071002, China
| | - Runa Li
- School
of Biomedicine, Beijing City University, Beijing 100094, China
| | - Lin Xing
- School
of Biomedicine, Beijing City University, Beijing 100094, China
| | - Liying Yuan
- School
of Life Sciences, Hebei University, Baoding 071002, China
| | - Xuetai Chen
- School
of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Jing Zhang
- School
of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Honglei Li
- Beijing
Cloudna Technology Company, Limited, Beijing 100029, China
| | - Shuzhen Guo
- School
of Traditional Chinese Medicine, Beijing
University of Chinese Medicine, Beijing 100029, China
| | - Feifei Guo
- Institute
of Chinese Materia Medica, China Academy
of Chinese Medical Sciences, Beijing 100700, China
| | - Jiachen Guo
- School
of Life Sciences, Hebei University, Baoding 071002, China
| | - Yuan Liu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
| | - Yaning Qi
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
| | - Biyue Yu
- School
of Life Sciences, Hebei University, Baoding 071002, China
| | - Feng Xu
- School
of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Dong Li
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
| | - Zhongyang Liu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, Beijing 102206, China
- School
of Life Sciences, Hebei University, Baoding 071002, China
| |
Collapse
|
18
|
Liu X, Yan W, Wang S, Lu M, Yang H, Chai X, Shi H, Zhang Y, Jia Q. Discovery of selective HDAC6 inhibitors based on a multi-layer virtual screening strategy. Comput Biol Med 2023; 160:107036. [PMID: 37196455 DOI: 10.1016/j.compbiomed.2023.107036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/30/2023] [Accepted: 05/11/2023] [Indexed: 05/19/2023]
Abstract
The abnormal enhancement of histone deacetylase 6 (HDAC6) has been demonstrated to be closely related to the occurrence and development of various malignant tumors, attracting extensive attention as a promising target for cancer therapy. Currently, only limited selective HDAC6 inhibitors have entered clinical trials, making the rapid discovery of selective HDAC6 inhibitors with safety profiles particularly urgent. In this study, a multi-layer virtual screening workflow was established, and the representative compounds screened were biologically evaluated in combination with enzyme inhibitory and anti-tumor cell proliferation experiments. The experimental results showed that the screened compounds L-25, L-32, L-45 and L-81 exhibited nanomolar inhibitory activity against HDAC6, and exerted a certain degree of anti-proliferative activities against tumor cells, especially the cytotoxicity of L-45 to A375 (IC50 = 11.23 ± 1.27 μM) and the cytotoxicity of L-81 against HCT-116 (IC50 = 12.25 ± 1.13 μM). Additionally, the molecular mechanisms underlying the subtype selective inhibitory activities of the selected compounds were further elucidated using computational approaches, and the hotspot residues on HDAC6 contributing to the ligands' binding were identified. In summary, this study established a multi-layer screening scheme to quickly and effectively screen out hit compounds with enzyme inhibitory activity and anti-tumor cell proliferation, providing novel scaffolds for the subsequent anti-tumor drug design based on HDAC6 target.
Collapse
Affiliation(s)
- Xingang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; The Key Laboratory of Neural and Vascular Biology, Ministry of Education, Hebei Medical University, Shijiazhuang, 050017, China; Key Laboratory of Innovative Drug Research and Evaluation of Hebei Province, Shijiazhuang, 050017, China
| | - Wenying Yan
- Department of Clinical Pharmacy, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, China
| | - Songsong Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; The Key Laboratory of Neural and Vascular Biology, Ministry of Education, Hebei Medical University, Shijiazhuang, 050017, China; Key Laboratory of Innovative Drug Research and Evaluation of Hebei Province, Shijiazhuang, 050017, China
| | - Ming Lu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; Department of Pharmacy, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Hao Yang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xu Chai
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - He Shi
- The Fourth Hospital of Shijiazhuang, Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang, 050000, China.
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; The Key Laboratory of Neural and Vascular Biology, Ministry of Education, Hebei Medical University, Shijiazhuang, 050017, China; Key Laboratory of Innovative Drug Research and Evaluation of Hebei Province, Shijiazhuang, 050017, China.
| | - Qingzhong Jia
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; The Key Laboratory of Neural and Vascular Biology, Ministry of Education, Hebei Medical University, Shijiazhuang, 050017, China; Key Laboratory of Innovative Drug Research and Evaluation of Hebei Province, Shijiazhuang, 050017, China.
| |
Collapse
|
19
|
Hu W, Zhang W, Zhou Y, Luo Y, Sun X, Xu H, Shi S, Li T, Xu Y, Yang Q, Qiu Y, Zhu F, Dai H. MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction. J Chem Inf Model 2023; 63:1626-1636. [PMID: 36802582 DOI: 10.1021/acs.jcim.2c01656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.
Collapse
Affiliation(s)
- Wei Hu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Huimin Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Teng Li
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yichao Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qianqian Yang
- Department of Pharmacy, Affiliated Hangzhou First Peoples Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Haibin Dai
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
| |
Collapse
|
20
|
He H, Duo H, Hao Y, Zhang X, Zhou X, Zeng Y, Li Y, Li B. Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications. Comput Biol Med 2023; 155:106671. [PMID: 36805225 DOI: 10.1016/j.compbiomed.2023.106671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023]
Abstract
De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.
Collapse
Affiliation(s)
- Hao He
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yujie Zeng
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Yinghong Li
- The Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| |
Collapse
|
21
|
Yan TC, Yue ZX, Xu HQ, Liu YH, Hong YF, Chen GX, Tao L, Xie T. A systematic review of state-of-the-art strategies for machine learning-based protein function prediction. Comput Biol Med 2023; 154:106446. [PMID: 36680931 DOI: 10.1016/j.compbiomed.2022.106446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
Collapse
Affiliation(s)
- Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| |
Collapse
|
22
|
Fu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, Zhang H, Xu H, Zhu F. Label-free proteome quantification and evaluation. Brief Bioinform 2023; 24:6833644. [PMID: 36403090 DOI: 10.1093/bib/bbac477] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 11/21/2022] Open
Abstract
The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.
Collapse
Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanxiang Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| |
Collapse
|
23
|
Cheemanapalli S, Palaniappan C, Mahesh Y, Iyyappan Y, Yarrappagaari S, Kanagaraj S. In vitro and in silico perspectives to explain anticancer activity of a novel syringic acid analog ((4-(1H-1, 3-benzodiazol-2-yl)-2, 6-dimethoxy phenol)) through apoptosis activation and NFkB inhibition in K562 leukemia cells. Comput Biol Med 2023; 152:106349. [PMID: 36470147 DOI: 10.1016/j.compbiomed.2022.106349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 11/13/2022] [Accepted: 11/22/2022] [Indexed: 11/24/2022]
Abstract
Syringic acid (SA) is an active carcinogenesis inhibitor; however, the low bioavailability and unstable functional groups hinder its activity. Here, a chemically synthesized novel SA analog (SA10) is evaluated for its anticancer activity using in-vitro and in-silico studies. K562 cell line study revealed that SA10 had shown a higher rate of inhibition (IC50 = 50.40 μg/mL) than its parental compound, SA (IC50 = 96.92 μg/mL), at 50 μM concentration. The inhibition ratio was also been evaluated by checking the expression level of NFkB and Bcl-2 and showing that SA10 has two-fold increase in the inhibitory mechanism than SA. This result demonstrates that SA10 acts as an NFkB inhibitor and an apoptosis inducer. Further, molecular docking and simulation have been performed to get insights into the possible inhibitory mechanism of SA and SA10 on NFkB at the atomistic level. The molecular docking results exemplify that both SA and SA10 bind to the active site of NFkB, thereby interfering with the association between DNA and NFkB. SA10 exhibits a more robust binding affinity than SA and is firmly docked well into the interior of the NFkB, as confirmed by MM-PBSA calculations. In a nutshell, the Benzimidazole scaffold containing SA10 has shown more NFkB inhibitory activity in K562 cells than SA, which could be helpful as an ideal therapeutic NFkB inhibitor for treating cancers.
Collapse
Affiliation(s)
- Srinivasulu Cheemanapalli
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India; Regional Ayurveda Research Institute (CCRAS, Govt. of India), Itanagar, Arunachal Pradesh, India
| | - Chandrasekaran Palaniappan
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India; Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Yeshwanth Mahesh
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Yuvaraj Iyyappan
- National Institute for Plant Biotechnology, ICAR, New Delhi, India
| | - Suresh Yarrappagaari
- Division of Ethnopharmacology, Department of Biotechnology, School of Herbal Studies and Natural Sciences, Dravidian University, Kuppam, Andhra Pradesh, India
| | - Sekar Kanagaraj
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India.
| |
Collapse
|
24
|
Yue ZX, Yan TC, Xu HQ, Liu YH, Hong YF, Chen GX, Xie T, Tao L. A systematic review on the state-of-the-art strategies for protein representation. Comput Biol Med 2023; 152:106440. [PMID: 36543002 DOI: 10.1016/j.compbiomed.2022.106440] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.
Collapse
Affiliation(s)
- Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| |
Collapse
|
25
|
Huang H, Zhang S, Wen X, Sadee W, Wang D, Yang S, Li L. Transcription Factors and ncRNAs Associated with CYP3A Expression in Human Liver and Small Intestine Assessed with Weighted Gene Co-Expression Network Analysis. Biomedicines 2022; 10:biomedicines10123061. [PMID: 36551817 PMCID: PMC9775998 DOI: 10.3390/biomedicines10123061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/09/2022] [Accepted: 11/16/2022] [Indexed: 11/29/2022] Open
Abstract
CYP3A4, CYP3A5, and CYP3A7, which are located in a multigene locus (CYP3A), play crucial roles in drug metabolism. To understand the highly variable hepatic expression of CYP3As, regulatory network analyses have focused on transcription factors (TFs). Since long non-coding RNAs (lncRNAs) likely contribute to such networks, we assessed the regulatory effects of both TFs and lncRNAs on CYP3A expression in the human liver and small intestine, main organs of CYP3A expression. Using weighted gene co-expression network analysis (WGCNA) of GTEx v8 RNA expression data and multiple stepwise regression analysis, we constructed TF-lncRNA-CYP3A co-expression networks. Multiple lncRNAs and TFs displayed robust associations with CYP3A expression that differed between liver and small intestines (LINC02499, HNF4A-AS1, AC027682.6, LOC102724153, and RP11-503C24.6), indicating that lncRNAs contribute to variance in CYP3A expression in both organs. Of these, HNF4A-AS1 had been experimentally demonstrated to affect CYP3A expression. Incorporating ncRNAs into CYP3A expression regulatory network revealed additional candidate TFs associated with CYP3A expression. These results serve as a guide for experimental studies on lncRNA-TF regulation of CYP3A expression in the liver and small intestines.
Collapse
Affiliation(s)
- Huina Huang
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Siqi Zhang
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaozhen Wen
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wolfgang Sadee
- Center for Pharmacogenomics, Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Danxin Wang
- Center for Pharmacogenomics, Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Siyao Yang
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Liang Li
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Experimental Education and Administration Center, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Correspondence:
| |
Collapse
|
26
|
Yang Q, Li B, Wang P, Xie J, Feng Y, Liu Z, Zhu F. LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data. Brief Bioinform 2022; 23:6768054. [PMID: 36274234 DOI: 10.1093/bib/bbac455] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/06/2022] [Accepted: 09/24/2022] [Indexed: 12/14/2022] Open
Abstract
Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.
Collapse
Affiliation(s)
- Qingxia Yang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Jicheng Xie
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Yuhao Feng
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Ziqiang Liu
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
27
|
Sun X, Zhang Y, Li H, Zhou Y, Shi S, Chen Z, He X, Zhang H, Li F, Yin J, Mou M, Wang Y, Qiu Y, Zhu F. DRESIS: the first comprehensive landscape of drug resistance information. Nucleic Acids Res 2022; 51:D1263-D1275. [PMID: 36243960 PMCID: PMC9825618 DOI: 10.1093/nar/gkac812] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.
Collapse
Affiliation(s)
| | | | | | | | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xin He
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Zhejiang University–University of Edinburgh Institute, Zhejiang University, Haining 314499, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunzhu Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- To whom correspondence should be addressed.
| |
Collapse
|
28
|
Li F, Yin J, Lu M, Mou M, Li Z, Zeng Z, Tan Y, Wang S, Chu X, Dai H, Hou T, Zeng S, Chen Y, Zhu F. DrugMAP: molecular atlas and pharma-information of all drugs. Nucleic Acids Res 2022; 51:D1288-D1299. [PMID: 36243961 PMCID: PMC9825453 DOI: 10.1093/nar/gkac813] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/30/2022] [Accepted: 10/12/2022] [Indexed: 02/06/2023] Open
Abstract
The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.
Collapse
Affiliation(s)
| | | | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Tan
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Xinyi Chu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- Correspondence may also be addressed to Su Zeng.
| | - Yuzong Chen
- Correspondence may also be addressed to Yuzong Chen.
| | - Feng Zhu
- To whom correspondence should be addressed.
| |
Collapse
|
29
|
Pitsillou E, Liang JJ, Beh RC, Hung A, Karagiannis TC. Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain. Comput Biol Med 2022; 149:106035. [PMID: 36055162 PMCID: PMC9420038 DOI: 10.1016/j.compbiomed.2022.106035] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/15/2022] [Accepted: 08/20/2022] [Indexed: 11/21/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (−39.4 and −43.3 kcal/mol, respectively) and stronger than WT (−33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.
Collapse
|
30
|
Liu S, Chen L, Zhang Y, Zhou Y, He Y, Chen Z, Qi S, Zhu J, Chen X, Zhang H, Luo Y, Qiu Y, Tao L, Zhu F. M6AREG: m6A-centered regulation of disease development and drug response. Nucleic Acids Res 2022; 51:D1333-D1344. [PMID: 36134713 PMCID: PMC9825441 DOI: 10.1093/nar/gkac801] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 01/30/2023] Open
Abstract
As the most prevalent internal modification in eukaryotic RNAs, N6-methyladenosine (m6A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. With the rapid accumulation of research interest in m6A, its crucial roles in the regulations of disease development and drug response are gaining more and more attention. Thus, a database offering such valuable data on m6A-centered regulation is greatly needed; however, no such database is as yet available. Herein, a new database named 'M6AREG' is developed to (i) systematically cover, for the first time, data on the effects of m6A-centered regulation on both disease development and drug response, (ii) explicitly describe the molecular mechanism underlying each type of regulation and (iii) fully reference the collected data by cross-linking to existing databases. Since the accumulated data are valuable for researchers in diverse disciplines (such as pathology and pathophysiology, clinical laboratory diagnostics, medicinal biochemistry and drug design), M6AREG is expected to have many implications for the future conduct of m6A-based regulation studies. It is currently accessible by all users at: https://idrblab.org/m6areg/.
Collapse
Affiliation(s)
- Shuiping Liu
- Correspondence may also be addressed to Shuiping Liu.
| | | | | | | | - Ying He
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Shasha Qi
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jinyu Zhu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Xudong Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Hao Zhang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310000, China
| | - Lin Tao
- Correspondence may also be addressed to Lin Tao.
| | - Feng Zhu
- To whom correspondence should be addressed. Tel: +86 189 8946 6518; Fax: +86 571 8820 8444;
| |
Collapse
|
31
|
Lin S, Zhang G, Wei DQ, Xiong Y. DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism. Comput Biol Med 2022; 149:105984. [PMID: 35994933 DOI: 10.1016/j.compbiomed.2022.105984] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/17/2022] [Accepted: 08/14/2022] [Indexed: 11/18/2022]
Abstract
Polypharmacy (multiple use of drugs) is an effective strategy for combating complex or co-existing diseases. However, a major consequence of polypharmacy is a higher risk of adverse side effects due to drug-drug interactions, which are rare and observed in relatively small clinical testing. Thus, identification of polypharmacy side effects remains challenging. Here, we propose a deep learning-based method, DeepPSE, to predict polypharmacy side effects in an end-to-end way. DeepPSE is composed of two main modules. First, multiple types of neural networks are constructed and fused to learn the deep representation of a drug pair. Second, the encoder block of transformer that includes self-attention mechanism is built to get latent features, which are further fed into the fully connected layer to predict polypharmacy side effects of drug pairs. Further, DeepPSE is compared with five baseline or state-of-the-art methods on a benchmark dataset of 964 types of polypharmacy side effects across 63473 drug pairs. Experimental results demonstrate that DeepPSE achieves better performance than that of all five methods. The source codes and data are available at https://github.com/ShenggengLin/DeepPSE.
Collapse
Affiliation(s)
- Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Guangwei Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nayang, Henan, 473006, China; Peng Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
| |
Collapse
|
32
|
Xue J, Zhang H, Zeng S. Integrate thermostabilized fusion protein apocytochrome b562RIL and N-glycosylation mutations: A novel approach to heterologous expression of human UDP-glucuronosyltransferase (UGT) 2B7. Front Pharmacol 2022; 13:965038. [PMID: 36034790 PMCID: PMC9412022 DOI: 10.3389/fphar.2022.965038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/19/2022] [Indexed: 11/15/2022] Open
Abstract
Human UDP-glucuronosyltransferase (UGT) 2B7 is a crucial phase II metabolic enzyme that transfers glucuronic acid from UDP-glucuronic acid (UDPGA) to endobiotic and xenobiotic substrates. Biophysical and biochemical investigations of UGT2B7 are hampered by the challenge of the integral membrane protein purification. This study focused on the expression and purification of recombinant UGT2B7 by optimizing the insertion sites for the thermostabilized fusion protein apocytochrome b562RIL (BRIL) and various mutations to improve the protein yields and homogeneity. Preparation of the recombinant proteins with high purity accelerated the measurement of pharmacokinetic parameters of UGT2B7. The dissociation constants (KD) of two classical substrates (zidovudine and androsterone) and two inhibitors (schisanhenol and hesperetin) of UGT2B7 were determined using the surface plasmon resonance spectroscopy (SPR) for the first time. Using negative-staining transmission electron microscopy (TEM), UGT2B7 protein particles were characterized, which could be useful for further exploring its three-dimensional structure. The methods described in this study could be broadly applied to other UGTs and are expected to provide the basis for the exploration of metabolic enzyme kinetics, the mechanisms of drug metabolisms and drug interactions, changes in pharmacokinetics, and pharmacodynamics studies in vitro.
Collapse
Affiliation(s)
- Jia Xue
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haitao Zhang
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Hangzhou Institute of Innovative Medicine, Institute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- *Correspondence: Haitao Zhang, ; Su Zeng,
| | - Su Zeng
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- *Correspondence: Haitao Zhang, ; Su Zeng,
| |
Collapse
|
33
|
In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2. Comput Biol Med 2022; 147:105788. [PMID: 35809412 PMCID: PMC9245396 DOI: 10.1016/j.compbiomed.2022.105788] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/30/2022] [Accepted: 06/26/2022] [Indexed: 11/28/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC50 of 12 μM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.
Collapse
|
34
|
Zhang S, Sun X, Mou M, Amahong K, Sun H, Zhang W, Shi S, Li Z, Gao J, Zhu F. REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research. Comput Biol Med 2022; 148:105825. [PMID: 35872412 DOI: 10.1016/j.compbiomed.2022.105825] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 12/24/2022]
Abstract
Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.
Collapse
Affiliation(s)
- Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kuerbannisha Amahong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| |
Collapse
|
35
|
Xie J, Chen R, Wang Q, Mao H. Exploration and validation of Taraxacum mongolicum anti-cancer effect. Comput Biol Med 2022; 148:105819. [PMID: 35810695 DOI: 10.1016/j.compbiomed.2022.105819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
Taraxacum mongolicum gained a lot of concern and was applied in 93 formulas in China due to its fame as a traditional Chinese medicine. The earliest recorded application of Taraxacum mongolicum was traced back to the Han dynasty. Generations of doctors boosted the usage and enriched the pharmacological mechanism. Clinical application of the Taraxacum mongolicum is flourishing as it treats multiple diseases. This study aims to explore the anti-cancer effect, retrieve the active ingredients and screen the key targets of Taraxacum mongolicum in cancer therapy. We collected and evaluated 10 key active compounds to investigate the anti-cancer effect via 69 significant targets and a variety of biological processes and pathways. Gene Ontology (GO) enrichment analysis uncovered targets associated with protein phosphorylation, cell proliferation and apoptotic processes via regulation of kinases, ATP and enzyme binding activities. Half of the top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were directly involved in cancer. Based on standard selection criteria, seven hub targets were obtained. These targets functioned through distinct patterns and pathways in realizing the anti-cancer effect. Molecular docking was conducted to validate the potential combination between compounds and hub targets to explore the pharmacological mechanism of key compounds in Taraxacum mongolicum against cancer. In summary, our findings indicate that the famous and widely used Chinese herb, Taraxacum mongolicum, shows good anti-cancer effect through its active compounds, targeted genes, and multiple involved biological processes. The results may provide a theoretical basis for subsequent experimental validation and drug development of Taraxacum mongolicum extract against cancer.
Collapse
Affiliation(s)
- Jumin Xie
- Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Huangshi, Hubei, 435003, PR China
| | - Ruxi Chen
- Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Huangshi, Hubei, 435003, PR China
| | - Qingzhi Wang
- Medical College of YiChun University, Xuefu Road No 576, Yichun, Jiangxi, 336000, PR China.
| | - Hui Mao
- Department of Dermatology, Huangshi Central Hospital, Huangshi, Hubei, 435000, PR China.
| |
Collapse
|
36
|
Li F, Yin J, Lu M, Yang Q, Zeng Z, Zhang B, Li Z, Qiu Y, Dai H, Chen Y, Zhu F. ConSIG: consistent discovery of molecular signature from OMIC data. Brief Bioinform 2022; 23:6618243. [PMID: 35758241 DOI: 10.1093/bib/bbac253] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/09/2022] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.
Collapse
Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.,Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
37
|
Xiang C, Chen C, Li X, Wu Y, Xu Q, Wen L, Xiong W, Liu Y, Zhang T, Dou C, Ding X, Hu L, Chen F, Yan Z, Liang L, Wei G. Computational approach to decode the mechanism of curcuminoids against neuropathic pain. Comput Biol Med 2022; 147:105739. [PMID: 35763932 DOI: 10.1016/j.compbiomed.2022.105739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Curcumin (CUR), demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) are the main components of turmeric that commonly used to treat neuropathic pain (NP). However, the mechanism of the therapy is not sufficiently clarified. Herein, network pharmacology, molecular docking and molecular dynamics (MD) approaches were used to investigate the mechanism of curcuminoids for NP treatment. METHODS Active targets of curcuminoids were obtained from the Swiss Target database, and NP-related targets were retrieved from GeneCards, OMIM, Drugbank and TTD databases. A protein-protein interaction (PPI) network was built to screen the core targets. Furthermore, DAVID was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and curcuminoids were assessed by molecular docking and the MD simulations were run for 100ns to validate the docking results on the top six complexes. RESULTS CUR, DMC, and BDMC had 100, 99 and 100 targets respectively. After overlapping with NP there were 33, 33 and 31 targets respectively. PPI network analysis of TOP 10 core targets, TNF, GSK3β were common targets of curcuminoids. Molecular docking and MD results indicated that curcuminoids bind strongly with the core targets. The GO and KEGG showed that curcuminoids regulated nitrogen metabolism, the serotonergic synapse and ErbB signaling pathway to alleviate NP. Furthermore, specific targets in these three compounds were also analysed at the same time. CONCLUSIONS This study systematically explored and compared the anti-NP mechanism of curcuminoids, providing a novel perspective for their utilization.
Collapse
Affiliation(s)
- Chunxiao Xiang
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Chunlan Chen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Xi Li
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Yating Wu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Qing Xu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lingmiao Wen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Wei Xiong
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Yanjun Liu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Tinglan Zhang
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Chongyang Dou
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Xian Ding
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lin Hu
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Fangfang Chen
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Zhiyong Yan
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| | - Lingli Liang
- Department of Physiology and Pathophysiology, Institute of Neuroscience, Translational Medicine Institute, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, Shanxi, China.
| | - Guihua Wei
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
| |
Collapse
|
38
|
Feng G, Yao H, Li C, Liu R, Huang R, Fan X, Ge R, Miao Q. ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides. Comput Biol Med 2022; 145:105459. [DOI: 10.1016/j.compbiomed.2022.105459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 12/26/2022]
|
39
|
Dwivedi M, Mukhopadhyay S, Yadav S, Dubey KD. A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing. Comput Biol Med 2022; 146:105607. [PMID: 35617724 DOI: 10.1016/j.compbiomed.2022.105607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/02/2022] [Accepted: 05/09/2022] [Indexed: 11/27/2022]
Abstract
Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant TB (MDR-TB) become a major hurdle in its therapy. These MDR strains utilize the multidrug efflux pump as a decisive weapon to fight against antitubercular drugs. Tap membrane protein was observed as a crucial multidrug efflux pump in M. tuberculosis and its critical implication in MDR-MTB development makes it an effective drug target. In the present study, we have utilized various in silico approaches to predict the applicability of FDA-approved ion channel inhibitors and blockers as therapeutic leads against Tuberculosis by targeting multidrug efflux protein; Tap in MTB. Tap protein structure is predicted by Phyre2 server followed by model refinement, validation, physio-chemical catheterization and target prediction. Further, the interaction between Tap protein and ligands were analysed by molecular docking and MD simulation run of 100 ns. Based on implication and compatibility, 18 FDA-approved ion channel inhibitors and blockers are selected as a ligand against the Tap protein and eventually observed five ligands; Glimepiride, Flecainide, Flupiritine, Nimodipine and Amlodipine as promising compounds which have exhibited the significant stable interaction with Tap protein and are proposed to modulate or interfere with its activity. These compounds illustrated the substantial docking score and total binding enthalpy more than -7 kcal/mol and -42 kcal/mol respectively which implies that the selected FDA-approved compounds can spontaneously interact with the Tap protein to modulate its function. This study proposed Tap protein as a prominent drug target in MTB and investigated compounds that show considerable interaction with the Tap protein as potential therapeutic molecules. These interactions may lead to modulating or inhibit the activity of drug efflux protein thereby making MTB susceptible to antitubercular drugs.
Collapse
Affiliation(s)
- Manish Dwivedi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, 226028, India.
| | - Sutanu Mukhopadhyay
- Department of Chemistry, Ramakrishna Mission Vivekananda Centenary College, West Bengal, India
| | - Shalini Yadav
- Department of Chemistry, Shiv Nadar University, Greater Noida, 201314, India
| | | |
Collapse
|
40
|
Zhang C, Mou M, Zhou Y, Zhang W, Lian X, Shi S, Lu M, Sun H, Li F, Wang Y, Zeng Z, Li Z, Zhang B, Qiu Y, Zhu F, Gao J. Biological activities of drug inactive ingredients. Brief Bioinform 2022; 23:6582006. [PMID: 35524477 DOI: 10.1093/bib/bbac160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/01/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.
Collapse
Affiliation(s)
- Chenyang Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| |
Collapse
|
41
|
Stalin A, Daniel Reegan A, Rajiv Gandhi M, Saravanan RR, Balakrishna K, Hesham AEL, Ignacimuthu S, Zhang Y. Mosquitocidal efficacy of embelin and its derivatives against Aedes aegypti L. and Culex quinquefasciatus Say. (Diptera: Culicidae) and computational analysis of acetylcholinesterase 1 (AChE1) inhibition. Comput Biol Med 2022; 146:105535. [PMID: 35487124 DOI: 10.1016/j.compbiomed.2022.105535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 11/18/2022]
Abstract
Embelin was isolated from the chloroform extract of Embelia ribes (Burm.f.) fruits; its derivative compounds 6-bromoembelin and vilangin were prepared, and they were evaluated for mosquitocidal activities against the third instar larvae and pupae of Aedes aegypti L. and Culex quinquefasciatus Say. (Diptera: Culicidae). The concentrations used were 0.5, 1.0, 1.5, and 2.0 ppm. Embelin recorded LC50 values of 5.79 and 5.54 ppm against the larvae of Ae. aegypti and Cx. quinquefasciatus, respectively. Similarly, the LC50 values of embelin were 10.23 and 6.93 ppm against the pupae of Ae. aegypti and Cx. quinquefasciatus, respectively. Of the two derivatives tested, vilangin showed the highest larvicidal activity with LC50 values of 1.38 and 1.28 ppm against the larvae of Ae. aegypti and Cx. quinquefasciatus, respectively. Similarly, the LC50 values of vilangin were 1.60 and 1.43 ppm against the pupae of Ae. aegypti and Cx. quinquefasciatus, respectively. The LC50 values of 6-bromoembelin were 3.30 and 2.83 ppm against the larvae and 4.40 and 4.30 ppm against the pupae of Ae. aegypti and Cx. quinquefasciatus, respectively. The histopathological results displayed significant damage on cuboidal cells of the midgut (CU) in vilangin treated larvae of Ae. aegypti and Cx. quinquefasciatus at a concentration of 2.0 ppm. Similarly, peritrophic membrane (PM) was completely impaired in vilangin-treated larvae of Cx. quinquefasciatus and midgut content (MC) was very low in vilangin-treated larvae of Cx. quinquefasciatus. In addition, molecular docking and molecular dynamics studies demonstrated the efficacy of vilangin on the inhibition of acetylcholinesterase (AChE1) in Ae. aegypti and Cx. quinquefasciatus. The present results suggest that vilangin could be used to develop a natural active product against mosquito larvae.
Collapse
Affiliation(s)
- Antony Stalin
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610 054, China.
| | - Appadurai Daniel Reegan
- National Centre for Disease Control, Bengaluru Branch, No:8, NTI Campus, Bellary Road, Bengaluru, 560 003, Karnataka, India; Xavier Research Foundation, St. Xavier's College, Affiliated to the Manonmaniam Sundaranar University, Palayamkottai, 627 002, Tamil Nadu, India.
| | - Munusamy Rajiv Gandhi
- National Biodiversity Authority, 5th Floor, CSIR Road, TICEL Bio Park, Taramani, Chennai, 600 113, India
| | - R R Saravanan
- Department of Physics, Meenakshi Chandrasekaran College of Arts and Science, Karambayam, Pattukkottai, Thanjavur, 614 626, India
| | - Kedike Balakrishna
- Entomology Research Institute, Loyola College, Affiliated to the University of Madras, Chennai, 600 034, Tamil Nadu, India
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt
| | - Savarimuthu Ignacimuthu
- Xavier Research Foundation, St. Xavier's College, Affiliated to the Manonmaniam Sundaranar University, Palayamkottai, 627 002, Tamil Nadu, India
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated To Southwest Medical University, Luzhou, China.
| |
Collapse
|
42
|
PregTox: A Resource of Knowledge about Drug Fetal Toxicity. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4284146. [PMID: 35469349 PMCID: PMC9034948 DOI: 10.1155/2022/4284146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/16/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022]
Abstract
Background It is of vital importance to determine the safety of drugs. Pregnant women, as a special group, need to evaluate the effects of drugs on pregnant women as well as the fetus. The use of drugs during pregnancy may be subject to fetal toxicity, thus affecting the development of the fetus or even leading to stillbirth. The U.S. Food and Drug Administration (FDA) issued a toxicity rating for drugs used during pregnancy in 1979. These toxicity ratings are denoted by the letters A, B, C, D, and X. However, the query of drug pregnancy category has yet to be well established as electronic service. Results Here, we presented PregTox, a publicly accessible resource for pregnancy category information of 1114 drugs. The PregTox database also included chemical structures, important physico-chemical properties, protein targets, and relevant signaling pathways. An advantage of the database is multiple search options which allow systematic analyses. In a case study, we demonstrated that a set of chemical descriptors could effectively discriminate high-risk drugs from others (area under ROC curve reached 0.81). Conclusions PregTox can serve as a unique drug safety data source for drug development and pharmacological research.
Collapse
|
43
|
Chen Y, Wang Y, Ding Y, Su X, Wang C. RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Comput Biol Med 2022; 143:105322. [PMID: 35217342 DOI: 10.1016/j.compbiomed.2022.105322] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/11/2022] [Accepted: 02/13/2022] [Indexed: 12/21/2022]
Abstract
Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations.
Collapse
Affiliation(s)
- Yaojia Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yanpeng Wang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Xi Su
- Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China.
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
| |
Collapse
|
44
|
Xia W, Zheng L, Fang J, Li F, Zhou Y, Zeng Z, Zhang B, Li Z, Li H, Zhu F. PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods. Comput Biol Med 2022; 145:105465. [PMID: 35366467 DOI: 10.1016/j.compbiomed.2022.105465] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/06/2023]
Abstract
Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.
Collapse
Affiliation(s)
- Weiqi Xia
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Jiebin Fang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| |
Collapse
|
45
|
A novel mTOR-associated gene signature for predicting prognosis and evaluating tumor immune microenvironment in lung adenocarcinoma. Comput Biol Med 2022; 145:105394. [PMID: 35325730 DOI: 10.1016/j.compbiomed.2022.105394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND The mechanistic target of rapamycin (mTOR) was proven to have great impact on apoptosis, cell proliferation, autophagy, and many other fundamental cellular processes; moreover, it closely correlates with tumor occurrence and development. However, few studies have constructed signatures based on mTOR-associated genes to assess multiple indicators of prognosis in lung adenocarcinoma (LUAD) patients. METHODS mTOR-associated gene sets, whole mRNA expression matrices, and clinical information of LUAD patients in training and validation cohorts were obtained from multiple public databases. Multiple methods were used to screen candidate genes, construct signatures, validate internally and externally, and conduct further studies: differentially expressed gene analysis, LASSO Cox regression analysis, Cox regression analysis, risk factor analysis, nomogram analysis, functional enrichment analysis, analyses in tumor immune microenvironment, and therapy. RESULTS A prognostic signature containing 8 genes (LDHA, SLA, WNT7A, PLK1, CCT6A, BTG2, TXNRD1, and DDIT4) was constructed. It performed well in both internal and external validation. Subsequent analysis found that the prognostic signature was of great significance in evaluating the tumor immune microenvironment and could guide the treatment of patients with LUAD to a certain extent. CONCLUSION The constructed mTOR-associated gene signature accurately predicted the prognostic pattern of patients with LUAD and is expected to be extremely useful in guiding LUAD therapy.
Collapse
|
46
|
HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins. Comput Biol Med 2022; 145:105395. [PMID: 35334314 DOI: 10.1016/j.compbiomed.2022.105395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022]
Abstract
The identification of DNA-binding proteins (DBPs) has always been a hot issue in the field of sequence classification. However, considering that the experimental identification method is very resource-intensive, the construction of a computational prediction model is worthwhile. This study developed and evaluated a hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM) for predicting DBPs. First, we collected two datasets and performed feature extraction on the sequences to obtain six feature groups, and then constructed the corresponding kernels. To ensure the effective utilisation of the base kernel and avoid ignoring the difference between the sample and its neighbours, we proposed local kernel alignment to calculate the kernel between the sample and its neighbours, with each sample as the centre. We combined the global and local kernel alignments to develop a hybrid kernel alignment model, and balance the relationship between the two through parameters. By maximising the hybrid kernel alignment value, we obtained the weight of each kernel and then linearly combined the kernels in the form of weights. Finally, the fused kernel was input into a support vector machine for training and prediction. Finally, in the independent test sets PDB186 and PDB2272, we obtained the highest Matthew's correlation coefficient (MCC) (0.768 and 0.5962, respectively) and the highest accuracy (87.1% and 78.43%, respectively), which were superior to the other predictors. Therefore, HKAM-MKM is an efficient prediction tool for DBPs.
Collapse
|
47
|
Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022; 645:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
MOTIVATION The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics. RESULTS Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.
Collapse
Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
| | - Hua Shi
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
| |
Collapse
|
48
|
Li F, Zhou Y, Zhang Y, Yin J, Qiu Y, Gao J, Zhu F. POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. Brief Bioinform 2022; 23:6532538. [PMID: 35183059 DOI: 10.1093/bib/bbac040] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 12/17/2022] Open
Abstract
Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.
Collapse
Affiliation(s)
- Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Jianqing Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| |
Collapse
|
49
|
Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
Collapse
Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
| |
Collapse
|
50
|
Xue W, Fu T, Deng S, Yang F, Yang J, Zhu F. Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram. ACS Chem Neurosci 2022; 13:340-351. [PMID: 35041375 DOI: 10.1021/acschemneuro.1c00694] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Human serotine transporter (hSERT) is one of the most influential drug targets, and its allosteric modulators (e.g., escitalopram) have emerged to be the next-generation medication for psychiatric disorders. However, the molecular mechanism underlying the allosteric modulation of hSERT is still elusive. Here, the simulation strategies of conventional (cMD) and steered (SMD) molecular dynamics were applied to investigate this molecular mechanism from distinct perspectives. First, cMD simulations revealed that escitalopram's binding to hSERT's allosteric site simultaneously enhanced its binding to the orthosteric site. Then, SMD simulation identified that the occupation of hSERT's allosteric site by escitalopram could also block its dissociation from the orthosteric site. Finally, by comparing the simulated structures of two hSERT-escitalopram complexes with and without allosteric modulation, a new conformational coupling between an extracellular (Arg104-Glu494) and an intracellular (Lys490-Glu494) salt bridge was identified. In summary, this study explored the mechanism underlying the allosteric modulation of hSERT by collectively applying two MD simulation strategies, which could facilitate our understanding of the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.
Collapse
Affiliation(s)
- Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
| | - Tingting Fu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Shengzhe Deng
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Fengyuan Yang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jingyi Yang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|