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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.
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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
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Hamano M, Nakamura T, Ito R, Shimada Y, Iwata M, Takeshita JI, Eguchi R, Yamanishi Y. DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion. Bioinformatics 2024; 40:btae048. [PMID: 38273708 PMCID: PMC10868337 DOI: 10.1093/bioinformatics/btae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 01/03/2024] [Accepted: 01/23/2024] [Indexed: 01/27/2024] Open
Abstract
MOTIVATION Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging. RESULTS Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.
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Affiliation(s)
- Momoko Hamano
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Toru Nakamura
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Ryoku Ito
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yuki Shimada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Jun-ichi Takeshita
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8569, Japan
| | - Ryohei Eguchi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Nagoya, Aichi 464-8601, Japan
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Hossain Z, Hubbard M. Genomic characterization of three bacterial isolates antagonistic to the pea root rot pathogen Aphanomyces euteiches. Can J Microbiol 2024; 70:52-62. [PMID: 38061385 DOI: 10.1139/cjm-2023-0117] [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] [Indexed: 02/02/2024]
Abstract
Microorganisms living in soil and rhizosphere or inside plants can promote plant growth and health. Genomic characterization of beneficial microbes could shed light on their special features. Through extensive field survey across Saskatchewan, Canada, followed by in vitro and greenhouse characterization, we identified several bacterial isolates antagonistic to pea root rot pathogen Aphanomyces euteiches. In this study, the genomes of three isolates-Pseudomonas sp. rhizo 66 (PD-S66), Pseudomonas synxantha rhizo 25 (Ps-S25), and Serratia sp. root 2 (TS-R2)-were sequenced, assembled, and annotated. Genome size of PD-S66 was 6 279 416 bp with 65 contigs, 59.32% GC content, and 5653 predicted coding sequences (CDS). Genome size of Ps-S25 was 6 058 437 bp with 66 contigs, a GC content of 60.08%, and 5575 predicted CDS. The genome size of TS-R2 was 5 282 152 bp, containing 26 contigs, a GC content of 56.17%, and 4956 predicted CDS. For the identification of the isolates, digital DNA-DNA hybridization (dDDH) and average nucleotide identity (ANI) values were determined, which confirmed PD-S66 and TS-R2 as potential new species, belonging to Pseudomonas and Serratia genera, respectively, while Ps-S25 belongs to species Pseudomonas synxantha. Biosynthetic gene clusters were predicted using antiSMASH. The genomic data provided insight into the genetics and biochemical pathways supporting the antagonistic activity against A. euteiches of these isolates.
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Affiliation(s)
- Zakir Hossain
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, 1 Airport Road, Swift Current, SK S9H 3X2, Canada
| | - Michelle Hubbard
- Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, 1 Airport Road, Swift Current, SK S9H 3X2, Canada
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Lv Y, Liu Z, Deng L, Xia S, Mu Q, Xiao B, Xiu Y, Liu Z. Hesperetin promotes bladder cancer cells death via the PI3K/AKT pathway by network pharmacology and molecular docking. Sci Rep 2024; 14:1009. [PMID: 38200039 PMCID: PMC10781778 DOI: 10.1038/s41598-023-50476-8] [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: 07/28/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Patients with bladder cancer (BLCA) still show high recurrence after surgery and chemotherapy. Hesperetin (HE), as a natural compound, has attracted researchers' attention due to its low toxicity and easy access. However, the inhibitory effect of HE on BLCA remains unknown. The hub genes and enrichment pathways regulated by HE in the treatment of BLCA were predicted by network pharmacology. The molecular docking of HE and hub proteins was visualized. Colony and CCK8 assays were used to test cell proliferation, and BLCA migration was confirmed by transwell and wound healing assays. In addition, the occurrence of apoptosis and ferroptosis was demonstrated by Hoechst staining, transmission electron microscopy (TEM) and ROS (reactive oxygen species) assay. Western Blotting was performed to validate the hub proteins, target functions and pathways. SRC, PIK3R1 and MAPK1 were identified as hub targets for HE in BLCA, involving the PI3k/AKT pathway. Furthermore, HE inhibited the proliferation and migration of BLCA cells. The MMP2/MMP9 proteins were significantly inhibited by HE. The increased expression of Bax and cleaved caspase-3 indicated that HE could promote BLCA cell apoptosis. In addition, Hoechst staining revealed concentrated and illuminated apoptotic nuclei. The activation of ROS and the decline of GPX4 expression suggested that HE might induce ferroptosis as an anti-BLCA process. Shrunk mitochondria and apoptotic bodies were observed in BLCA cells treated with HE, with reduced or absent mitochondrial cristae. We propose for the first time that HE could inhibit the proliferation and migration of BLCA cells and promote apoptosis and ferroptosis. HE may act by targeting proteins such as SRC, PIK3R1 and MAPK1 and the PI3K/AKT pathway.
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Affiliation(s)
- Yue Lv
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Zhonghao Liu
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, Heilongjiang, China
| | - Leihong Deng
- Department of Ultrasound Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Shunyao Xia
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China
| | - Qingchun Mu
- Department of Neurosurgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570311, China
| | - Bang Xiao
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China
| | - Youcheng Xiu
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China
| | - Zan Liu
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, 23 Postal Street, Harbin, 150000, Heilongjiang, China.
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Gallo K, Goede A, Eckert OA, Gohlke BO, Preissner R. Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data. Nucleic Acids Res 2024; 52:D1503-D1507. [PMID: 37971295 PMCID: PMC10767915 DOI: 10.1093/nar/gkad1017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/17/2023] [Accepted: 10/27/2023] [Indexed: 11/19/2023] Open
Abstract
One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.
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Affiliation(s)
- Kathleen Gallo
- Charité - Universitätsmedizin Berlin, Institute of Physiology and GB IT, Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Andrean Goede
- Charité - Universitätsmedizin Berlin, Institute of Physiology and GB IT, Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Oliver-Andreas Eckert
- Charité - Universitätsmedizin Berlin, Institute of Physiology and GB IT, Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Bjoern-Oliver Gohlke
- Charité - Universitätsmedizin Berlin, Institute of Physiology and GB IT, Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Robert Preissner
- Charité - Universitätsmedizin Berlin, Institute of Physiology and GB IT, Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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Parvizpour S, Elengoe A, Alizadeh E, Razmara J, Shamsir MS. In silico targeting breast cancer biomarkers by applying rambutan ( Nephelium lappaceum) phytocompounds. J Biomol Struct Dyn 2023; 41:10037-10050. [PMID: 36451602 DOI: 10.1080/07391102.2022.2152868] [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: 05/16/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022]
Abstract
Worldwide, breast cancer is the leading type of cancer among women. Overexpression of various prognostic indicators, including nuclear receptors, is linked to breast cancer features. To date, no effective drug has been discovered to block the proliferation of breast cancer cells. This study has been designed to discover target-based small molecular-like natural drug candidates that have anti-cancer potential without causing any serious side effects. A comprehensive substrate-based drug design was carried out to discover the potential plant compounds against the target breast cancer biomarkers including phytochemicals screening, active site identification, molecular docking, pharmacokinetic (PK) properties prediction, toxicity prediction, and molecular dynamics (MD) simulation approaches. Twenty plant compounds extracted from the rambutan (Nephelium lappaceum) were obtained from PubChem Database; and screened against the breast cancer biomarkers including estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR). The best docking interaction was chosen based on the higher binding affinity. Analyzing the pharmacokinetic properties and toxicity prediction results indicated that the fifteen selected plant compounds have good potency without toxicity and are safe for humans. Four phytochemicals with a higher binding affinity were chosen for each breast cancer biomarker to study their stability in interaction with the target proteins using MD simulation. Among the above compounds, Ellagic acid showed the high binding affinity against all three breast cancer biomarkers.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Asita Elengoe
- Department of Biotechnology, Faculty of Science, Lincoln University College Malaysia, Petaling Jaya, Selangor, Malaysia
| | - Effat Alizadeh
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, Iran
| | - Mohd Shahir Shamsir
- Bioinformatics Research Group (BIRG), Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
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Samantray D, Tanwar AS, Murali TS, Brand A, Satyamoorthy K, Paul B. A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:445-460. [PMID: 37861712 DOI: 10.1089/omi.2023.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.
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Affiliation(s)
- Debyani Samantray
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Ankit Singh Tanwar
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, The Netherlands
- Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
| | - Thokur Sreepathy Murali
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Angela Brand
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, The Netherlands
- Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
- Department of Health Information, Prasanna School of Public Health (PSPH), Manipal Academy of Higher Education, Manipal, India
| | - Kapaettu Satyamoorthy
- SDM College of Medical Sciences and Hospital, Shri Dharmasthala Manjunatheshwara (SDM) University, Dharwad, India
| | - Bobby Paul
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
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Ren X, Yan CX, Zhai RX, Xu K, Li H, Fu XJ. Comprehensive survey of target prediction web servers for Traditional Chinese Medicine. Heliyon 2023; 9:e19151. [PMID: 37664753 PMCID: PMC10468387 DOI: 10.1016/j.heliyon.2023.e19151] [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: 05/30/2022] [Revised: 07/27/2023] [Accepted: 08/14/2023] [Indexed: 09/05/2023] Open
Abstract
Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.
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Affiliation(s)
- Xia Ren
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Chun-Xiao Yan
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Run-Xiang Zhai
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Kuo Xu
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Hui Li
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Xian-Jun Fu
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
- Shandong Engineering and Technology Research Center of Traditional Chinese Medicine, Jinan 250355, China
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Lee CW, Kim SM, Sa S, Hong M, Nam SM, Han HW. Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape. BMC Med Genomics 2023; 16:17. [PMID: 36717817 PMCID: PMC9885570 DOI: 10.1186/s12920-023-01444-8] [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: 08/10/2022] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug-gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.
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Affiliation(s)
- Chae Won Lee
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Sung Min Kim
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Soonok Sa
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Myunghee Hong
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
| | - Sang-Min Nam
- grid.452398.10000 0004 0570 1076Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Hyun Wook Han
- grid.410886.30000 0004 0647 3511Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, 13488 South Korea ,grid.410886.30000 0004 0647 3511Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, 13488 South Korea
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Gallo K, Kemmler E, Goede A, Becker F, Dunkel M, Preissner R, Banerjee P. SuperNatural 3.0-a database of natural products and natural product-based derivatives. Nucleic Acids Res 2022; 51:D654-D659. [PMID: 36399452 PMCID: PMC9825600 DOI: 10.1093/nar/gkac1008] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/07/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers' increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.
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Affiliation(s)
- Kathleen Gallo
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Emanuel Kemmler
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Andrean Goede
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Finnja Becker
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Mathias Dunkel
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Robert Preissner
- Institute of Physiology and Science-IT, Charite - Universitätsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115 Berlin, Germany
| | - Priyanka Banerjee
- To whom correspondence should be addressed. Tel: +49 30 450 528 505; Fax: +49 30 450 540 955;
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Hao Y, Chen M, Othman Y, Xie XQ, Feng Z. Virus-CKB 2.0: Viral-Associated Disease-Specific Chemogenomics Knowledgebase. ACS OMEGA 2022; 7:37476-37484. [PMID: 36312370 PMCID: PMC9609052 DOI: 10.1021/acsomega.2c04258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Transmissible and infectious viruses can cause large-scale epidemics around the world. This is because the virus can constantly mutate and produce different variants and subvariants to counter existing treatments. Therefore, a variety of treatments are urgently needed to keep up with the mutation of the viruses. To facilitate the research of such treatment, we updated our Virus-CKB 1.0 to Virus-CKB 2.0, which contains 10 kinds of viruses, including enterovirus, dengue virus, hepatitis C virus, Zika virus, herpes simplex virus, Andes orthohantavirus, human immunodeficiency virus, Ebola virus, Lassa virus, influenza virus, coronavirus, and norovirus. To date, Virus-CKB 2.0 archived at least 65 antiviral drugs (such as remdesivir, telaprevir, acyclovir, boceprevir, and nelfinavir) in the market, 178 viral-related targets with 292 available 3D crystal or cryo-EM structures, and 3766 chemical agents reported for these target proteins. Virus-CKB 2.0 is integrated with established tools for target prediction and result visualization; these include HTDocking, TargetHunter, blood-brain barrier (BBB) predictor, Spider Plot, etc. The Virus-CKB 2.0 server is accessible at https://www.cbligand.org/g/virus-ckb. By using the established chemogenomic tools and algorithms and newly developed tools, we can screen FDA-approved drugs and chemical compounds that may bind to these proteins involved in viral-associated disease regulation. If the virus strain mutates and the vaccine loses its effect, we can still screen drugs that can be used to treat the mutated virus in a fleeting time. In some cases, we can even repurpose FDA-approved drugs through Virus-CKB 2.0.
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Affiliation(s)
| | | | - Yasmin Othman
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhiwei Feng
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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12
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Nakamura T, Iwata M, Hamano M, Eguchi R, Takeshita JI, Yamanishi Y. Small compound-based direct cell conversion with combinatorial optimization of pathway regulations. Bioinformatics 2022; 38:ii99-ii105. [PMID: 36124791 DOI: 10.1093/bioinformatics/btac475] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Direct cell conversion, direct reprogramming (DR), is an innovative technology that directly converts source cells to target cells without bypassing induced pluripotent stem cells. The use of small compounds (e.g. drugs) for DR can help avoid carcinogenic risk induced by gene transfection; however, experimentally identifying small compounds remains challenging because of combinatorial explosion. RESULTS In this article, we present a new computational method, COMPRENDRE (combinatorial optimization of pathway regulations for direct reprograming), to elucidate the mechanism of small compound-based DR and predict new combinations of small compounds for DR. We estimated the potential target proteins of DR-inducing small compounds and identified a set of target pathways involving DR. We identified multiple DR-related pathways that have not previously been reported to induce neurons or cardiomyocytes from fibroblasts. To overcome the problem of combinatorial explosion, we developed a variant of a simulated annealing algorithm to identify the best set of compounds that can regulate DR-related pathways. Consequently, the proposed method enabled to predict new DR-inducing candidate combinations with fewer compounds and to successfully reproduce experimentally verified compounds inducing the direct conversion from fibroblasts to neurons or cardiomyocytes. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION The code supporting the current study is available at the http://labo.bio.kyutech.ac.jp/~yamani/comprendre. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Toru Nakamura
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Momoko Hamano
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Ryohei Eguchi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Jun-Ichi Takeshita
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8569, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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13
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Zheng J, Xiao X, Qiu WR. DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method. Front Genet 2022; 13:859188. [PMID: 35754843 PMCID: PMC9213727 DOI: 10.3389/fgene.2022.859188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/25/2022] [Indexed: 11/20/2022] Open
Abstract
Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.
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Affiliation(s)
- Jie Zheng
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
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14
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ZHANG BY, ZHENG YF, ZHAO J, KANG D, WANG Z, XU LJ, LIU AL, DU GH. Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation. Chin J Nat Med 2022; 20:332-351. [DOI: 10.1016/s1875-5364(22)60180-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Indexed: 11/03/2022]
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15
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Mehrabian A, Mashreghi M, Dadpour S, Badiee A, Arabi L, Hoda Alavizadeh S, Alia Moosavian S, Reza Jaafari M. Nanocarriers Call the Last Shot in the Treatment of Brain Cancers. Technol Cancer Res Treat 2022; 21:15330338221080974. [PMID: 35253549 PMCID: PMC8905056 DOI: 10.1177/15330338221080974] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Our brain is protected by physio-biological barriers. The blood–brain barrier (BBB) main mechanism of protection relates to the abundance of tight junctions (TJs) and efflux pumps. Although BBB is crucial for healthy brain protection against toxins, it also leads to failure in a devastating disease like brain cancer. Recently, nanocarriers have been shown to pass through the BBB and improve patients’ survival rates, thus becoming promising treatment strategies. Among nanocarriers, inorganic nanocarriers, solid lipid nanoparticles, liposomes, polymers, micelles, and dendrimers have reached clinical trials after delivering promising results in preclinical investigations. The size of these nanocarriers is between 10 and 1000 nm and is modified by surface attachment of proteins, peptides, antibodies, or surfactants. Multiple research groups have reported transcellular entrance as the main mechanism allowing for these nanocarriers to cross BBB. Transport proteins and transcellular lipophilic pathways exist in BBB for small and lipophilic molecules. Nanocarriers cannot enter via the paracellular route, which is limited to water-soluble agents due to the TJs and their small pore size. There are currently several nanocarriers in clinical trials for the treatment of brain cancer. This article reviews challenges as well as fitting attributes of nanocarriers for brain tumor treatment in preclinical and clinical studies.
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Affiliation(s)
- Amin Mehrabian
- School of Pharmacy, Biotechnology Research Center, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Warwick Medical School, University of Warwick, Coventry, UK
| | - Mohammad Mashreghi
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saba Dadpour
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Student Research Committee, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Badiee
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Leila Arabi
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Hoda Alavizadeh
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Alia Moosavian
- School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahmoud Reza Jaafari
- School of Pharmacy, Biotechnology Research Center, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,School of Pharmacy, 37552Mashhad University of Medical Sciences, Mashhad, Iran.,Nanotechnology Research Center, Pharmaceutical Technology Institute, 37552Mashhad University of Medical Sciences, Mashhad, Iran
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16
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Zhang S, Wang J, Lin Z, Liang Y. Application of Machine Learning Techniques in Drug-target Interactions Prediction. Curr Pharm Des 2021; 27:2076-2087. [PMID: 33238865 DOI: 10.2174/1381612826666201125105730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/06/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field. RESULTS The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category. CONCLUSION Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Jiesheng Wang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Zhenhui Lin
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Yunyun Liang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
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17
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Gao J, Wang Z, Fu J, A J, Ohno Y, Xu C. Combination treatment with cisplatin, paclitaxel and olaparib has synergistic and dose reduction potential in ovarian cancer cells. Exp Ther Med 2021; 22:935. [PMID: 34335884 PMCID: PMC8290430 DOI: 10.3892/etm.2021.10367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 09/29/2020] [Indexed: 12/27/2022] Open
Abstract
Ovarian cancer is the most lethal type of gynecological cancer. Due to its high heterogeneity and complicated pathological mechanisms, the 5-year survival rate of patients with ovarian cancer is <40%. Tumor cytoreductive surgery and systemic chemotherapy of platinum combined with paclitaxel are currently considered the gold standard for the treatment of ovarian cancer, and chemotherapy resistance has become a key constraint in improving the cure rate of ovarian cancer. Therefore, it is important to identify novel treatment methods and strategies for ovarian cancer. Targeted drugs can not only be used in combination with chemotherapy, but also act as maintenance therapy to promote patient survival time. PARP inhibitor is a novel type of ovarian cancer treatment targeted drug, which can induce an anticancer effect by inhibiting DNA damage and repair of ovarian cancer cells. The present study investigated the different effects of olaparib, cisplatin and paclitaxel in several dosages by single use and combinations on the proliferation of different human ovarian cancer cell lines, in order to verify the synergistic effects of the combinations of the three anticancer agents in pairs. The proliferation inhibitory rate of the cell lines was determined using a Cell Counting Kit-8 assay, while the combination index (CI) value of the combination of three agents in pairs was analyzed using Compusyn software. The proliferation was observed using a crystal violet assay, and the apoptosis ratio was measured via flow cytometry. The results of the present study revealed that the combination of cisplatin with olaparib group had a higher inhibition effect than each single group and had a higher dose-reduction index of >1 than the other two combinations at all concentrations in A2780 and OVCAR-3 cell lines. The difference in proliferation inhibition and induced apoptosis rate of A2780 cell lines was significant in the combination of cisplatin with olaparib group and the control group (P<0.01) at 0.25x IC50. For the OVCAR-3 cell line, the difference was also significant between two groups (P<0.05). The CI values in the A2780 cell line revealed significant differences between the low-dose group (0.0625x, 0.125x and 0.25x IC50) and the high-dose group (0.5x, 1.0x and 2.0x IC50) for the group that received the combination of cisplatin with olaparib (P<0.05). The present study highlighted that the group receiving a combination of cisplatin with olaparib exhibited the most significant synergistic effects among the three combinations, particularly at low doses.
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Affiliation(s)
- Jianwen Gao
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan.,School of Medical Engineering, Ma'anshan University, Ma'anshan, Anhui 243100, P.R. China.,Department of Biotechnological Pharmaceutics, Shanghai Pharmaceutical School, Shanghai 200135, P.R. China
| | - Zehua Wang
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, P.R. China.,Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai 200011, P.R. China
| | - Jiayu Fu
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Jisaihan A
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Yuko Ohno
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Congjian Xu
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, P.R. China.,Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai 200011, P.R. China.,Department of Obstetrics and Gynecology of Shanghai Medical School, Fudan University, Shanghai 200032, P.R. China
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18
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Feng Z, Chen M, Liang T, Shen M, Chen H, Xie XQ. Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research. Brief Bioinform 2021; 22:882-895. [PMID: 32715315 PMCID: PMC7454273 DOI: 10.1093/bib/bbaa155] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/07/2020] [Accepted: 06/18/2020] [Indexed: 01/08/2023] Open
Abstract
Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.
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Affiliation(s)
- Zhiwei Feng
- School of Pharmacy, University of Pittsburgh
| | - Maozi Chen
- South China Agricultural University, China
| | | | | | - Hui Chen
- School of Pharmacy, University of Pittsburgh
| | - Xiang-Qun Xie
- School of Pharmacy and a Professor of Pharmaceutical Sciences
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19
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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20
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Chen HG, Zhou XH. MNBDR: A Module Network Based Method for Drug Repositioning. Genes (Basel) 2020; 12:E25. [PMID: 33375395 PMCID: PMC7824496 DOI: 10.3390/genes12010025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/21/2020] [Accepted: 12/24/2020] [Indexed: 11/17/2022] Open
Abstract
Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein-protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein-protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.
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Affiliation(s)
| | - Xiong-Hui Zhou
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China;
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21
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Feng Z, Chen M, Shen M, Liang T, Chen H, Xie XQ. Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research. J Chem Inf Model 2020; 60:4429-4435. [PMID: 32786694 DOI: 10.1021/acs.jcim.0c00633] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https://www.cbligand.org/g/pain-ckb.
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Affiliation(s)
- Zhiwei Feng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Maozi Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Mingzhe Shen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Tianjian Liang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Hui Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; National Center of Excellence for Computational Drug Abuse Research; Drug Discovery Institute; and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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22
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Liu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:3023127. [PMID: 33149752 PMCID: PMC7603577 DOI: 10.1155/2020/3023127] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 07/13/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND The incidence of gout and hyperuricemia is increasing year by year in the world. Plantain is a traditional natural medicine commonly used in the treatment of gout and hyperuricemia, but the molecular mechanism of its active compounds is still unclear. Based on network pharmacology, this article predicts the targets and pathways of effective components of plantain for gout and hyperuricemia and provides effective reference for clinical medication. METHOD Traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and SymMap databases were used to screen out the active compounds and their targets in plantain. GeneCards, Therapeutic Target Database (TTD), and Online Mendelian Inheritance in Man (OMIM) databases were used to find the targets corresponding to gout and hyperuricemia. Venn diagram was used to obtain the intersection targets of plantain and diseases. The interaction network of the plantain active compounds-targets-pathways-diseases was constructed by using Cytoscape 3.7.2 software. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out. RESULT Seven active compounds were identified by network pharmacological analysis, including dinatin, baicalein, baicalin, sitosterol, 6-OH-luteolin, stigmasterol, and luteolin. Plantain plays a role in gout and hyperuricemia diseases by regulating various biological processes, cellular components, and molecular functions. The core targets of plantain for treating gout are MAPK1, RELA, TNF, NFKBIA, and IFNG, and the key pathways are pathways in cancer, hypoxia-inducible factor-1 (HIF-1) signaling pathway, interleukin (IL)-17 signaling pathway, Chagas disease (American trypanosomiasis), and relaxin signaling pathway. The core targets of plantain for hyperuricemia are RELA, MAPK1, NFKBIA, CASP3, CASP8, and TNF, and the main pathways are pathways in cancer, apoptosis, hepatitis B, IL-17 signaling pathway, and toxoplasmosis. CONCLUSION This study explored the related targets and mechanisms of plantain for the treatment of gout and hyperuricemia from the perspective of network pharmacological analysis, reflecting the characteristics of multiple components, multiple targets, and multiple pathways, and it provides a good theoretical basis for the clinical application of plantain.
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Affiliation(s)
- Pei Liu
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Huachong Xu
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Yucong Shi
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Li Deng
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Xiaoyin Chen
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
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23
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Dhillon BK, Smith M, Baghela A, Lee AHY, Hancock REW. Systems Biology Approaches to Understanding the Human Immune System. Front Immunol 2020; 11:1683. [PMID: 32849587 PMCID: PMC7406790 DOI: 10.3389/fimmu.2020.01683] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/24/2020] [Indexed: 12/18/2022] Open
Abstract
Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.
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Affiliation(s)
- Bhavjinder K. Dhillon
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Maren Smith
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Arjun Baghela
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Amy H. Y. Lee
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
- Molecular Biology & Biochemistry Department, Simon Fraser University, Burnaby, BC, Canada
| | - Robert E. W. Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
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24
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Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
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Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
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25
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Zhou W, Wu J, Zhu Y, Meng Z, Liu X, Liu S, Ni M, Jia S, Zhang J, Guo S. Study on the mechanisms of compound Kushen injection for the treatment of gastric cancer based on network pharmacology. BMC Complement Med Ther 2020; 20:6. [PMID: 32020871 PMCID: PMC7076865 DOI: 10.1186/s12906-019-2787-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 12/04/2019] [Indexed: 12/24/2022] Open
Abstract
Background As an effective prescription for gastric cancer (GC), Compound Kushen Injection (CKI) has been widely used even though few molecular mechanism analyses have been carried out. Methods In this study, we identified 16 active ingredients and 60 GC target proteins. Then, we established a compound-predicted target network and a GC target protein-protein interaction (PPI) network by Cytoscape 3.5.1 and systematically analyzed the potential targets of CKI for the treatment of GC. Finally, molecular docking was applied to verify the key targets. In addition, we analyzed the mechanism of action of the predicted targets by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Results The results showed that the potential targets, including CCND1, PIK3CA, AKT1, MAPK1, ERBB2, and MMP2, are the therapeutic targets of CKI for the treatment of GC. Functional enrichment analysis indicated that CKI has a therapeutic effect on GC by synergistically regulating some biological pathways, such as the cell cycle, pathways in cancer, the PI3K-AKT signaling pathway, the mTOR signaling pathway, and the FoxO signaling pathway. Moreover, molecular docking simulation indicated that the compounds had good binding activity to PIK3CA, AKT1, MAPK1, ERBB2, and MMP2 in vivo. Conclusion This research partially highlighted the molecular mechanism of CKI for the treatment of GC, which has great potential in the identification of the effective compounds in CKI and biomarkers to treat GC.
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Affiliation(s)
- Wei Zhou
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Jiarui Wu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China.
| | - Yingli Zhu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Ziqi Meng
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Xinkui Liu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Shuyu Liu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Mengwei Ni
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Shanshan Jia
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Jingyuan Zhang
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
| | - Siyu Guo
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
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26
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V K MA, Chandrasekaran VM, Pandurangan S. Protein Domain Level Cancer Drug Targets in the Network of MAPK Pathways. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:2057-2065. [PMID: 29993692 DOI: 10.1109/tcbb.2018.2829507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Proteins in the MAPK pathways considered as potential drug targets for cancer treatment. Pathways along with the cross-talks increase their scope to view them as a network of MAPK pathways. Side effect causing targeted domains act as a proxy for drug targets due to its structural similarity and frequent reuse of their variants. We proposed to identify non-repeatable protein domains as the drug targets to disrupt the signal transduction than targeting the whole protein. Network based approach is used to understand the contribution of 52 domains in non-hub, non-essential, and intra-pathway cancerous nodes and to identify potential drug target domains. 34 distinct domains in the cancerous proteins are playing vital roles in making cancer as a complex disease and pose challenges to identify potential drug targets. Distribution of domain families follows the power law in the network. Single promiscuous domains are contributing to the formation of hubs like Pkinease, Pkinease Tyr, and Ras. Hub nodes are positively correlated with the domain coverage and targeting them would disrupt functional properties of the proteins. EIF 4EBP, alpha Kinase, Sel1, ROKNT, and KH 1 are the domains identified as potential domain targets for the disruption of the signaling mechanism involved in cancer.
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27
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Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, Zhang Y, Li S, Yang F, Sun Q, Qin C, Zeng X, Chen Z, Chen YZ, Zhu F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 2019; 46:D1121-D1127. [PMID: 29140520 PMCID: PMC5753365 DOI: 10.1093/nar/gkx1076] [Citation(s) in RCA: 376] [Impact Index Per Article: 75.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 11/10/2017] [Indexed: 12/25/2022] Open
Abstract
Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.
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Affiliation(s)
- Ying Hong Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chun Yan Yu
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiao Xu Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoyu Zhang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Gao Tu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yang Zhang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Fengyuan Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qiu Sun
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Xian Zeng
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou 310006, China
| | - Yu Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore
| | - Feng Zhu
- Bioinformatics and Drug Design Group, Department of Pharmacy and Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.,Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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28
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Franco-Serrano L, Hernández S, Calvo A, Severi MA, Ferragut G, Pérez-Pons J, Piñol J, Pich Ò, Mozo-Villarias Á, Amela I, Querol E, Cedano J. MultitaskProtDB-II: an update of a database of multitasking/moonlighting proteins. Nucleic Acids Res 2019; 46:D645-D648. [PMID: 29136215 PMCID: PMC5753234 DOI: 10.1093/nar/gkx1066] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/20/2017] [Indexed: 12/31/2022] Open
Abstract
Multitasking, or moonlighting, is the capability of some proteins to execute two or more biological functions. MultitaskProtDB-II is a database of multifunctional proteins that has been updated. In the previous version, the information contained was: NCBI and UniProt accession numbers, canonical and additional biological functions, organism, monomeric/oligomeric states, PDB codes and bibliographic references. In the present update, the number of entries has been increased from 288 to 694 moonlighting proteins. MultitaskProtDB-II is continually being curated and updated. The new database also contains the following information: GO descriptors for the canonical and moonlighting functions, three-dimensional structure (for those proteins lacking PDB structure, a model was made using Itasser and Phyre), the involvement of the proteins in human diseases (78% of human moonlighting proteins) and whether the protein is a target of a current drug (48% of human moonlighting proteins). These numbers highlight the importance of these proteins for the analysis and explanation of human diseases and target-directed drug design. Moreover, 25% of the proteins of the database are involved in virulence of pathogenic microorganisms, largely in the mechanism of adhesion to the host. This highlights their importance for the mechanism of microorganism infection and vaccine design. MultitaskProtDB-II is available at http://wallace.uab.es/multitaskII.
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Affiliation(s)
- Luís Franco-Serrano
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Sergio Hernández
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Alejandra Calvo
- Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, CP 50000 Salto, Uruguay
| | - María A Severi
- Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, CP 50000 Salto, Uruguay
| | - Gabriela Ferragut
- Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, CP 50000 Salto, Uruguay
| | - JosepAntoni Pérez-Pons
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Jaume Piñol
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Òscar Pich
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Ángel Mozo-Villarias
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Isaac Amela
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Enrique Querol
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
- To whom correspondence should be addressed. Tel: +34 93 586 8951; Fax: +34 93 581 2011; . Correspondence may also be addressed to Juan Cedano. Tel: +598 47 337 133;
| | - Juan Cedano
- Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, CP 50000 Salto, Uruguay
- To whom correspondence should be addressed. Tel: +34 93 586 8951; Fax: +34 93 581 2011; . Correspondence may also be addressed to Juan Cedano. Tel: +598 47 337 133;
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29
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Sheng Z, Sun Y, Yin Z, Tang K, Cao Z. Advances in computational approaches in identifying synergistic drug combinations. Brief Bioinform 2019; 19:1172-1182. [PMID: 28475767 DOI: 10.1093/bib/bbx047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Indexed: 12/21/2022] Open
Abstract
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
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Affiliation(s)
- Zhen Sheng
- School of Life Sciences and Technology, Tongji University
| | - Yi Sun
- School of Life Sciences and Technology, Tongji University
| | - Zuojing Yin
- School of Life Sciences and Technology, Tongji University
| | - Kailin Tang
- Advanced Institute of Translational Medicine, Tongji University
| | - Zhiwei Cao
- School of Life Sciences and Technology, Tongji University
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30
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Durán C, Daminelli S, Thomas JM, Haupt VJ, Schroeder M, Cannistraci CV. Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. Brief Bioinform 2019; 19:1183-1202. [PMID: 28453640 PMCID: PMC6291778 DOI: 10.1093/bib/bbx041] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Indexed: 01/03/2023] Open
Abstract
The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.
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Affiliation(s)
| | - Simone Daminelli
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | | | | | - Michael Schroeder
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | - Carlo Vittorio Cannistraci
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
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31
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Fukuoka Y. Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2019; 1903:269-279. [PMID: 30547448 DOI: 10.1007/978-1-4939-8955-3_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be evaluated based on the output of an SVM, which was binary, the proposed method evaluated the reliability as a product of a distance from the separating hyperplane of the SVM and a similarity between the disease targeted by the drug and a candidate disease. A validation using real data revealed that the performance of the proposed method was promising.
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32
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Chamberlin SR, Blucher A, Wu G, Shinto L, Choonoo G, Kulesz-Martin M, McWeeney S. Natural Product Target Network Reveals Potential for Cancer Combination Therapies. Front Pharmacol 2019; 10:557. [PMID: 31214023 PMCID: PMC6555193 DOI: 10.3389/fphar.2019.00557] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022] Open
Abstract
A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.
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Affiliation(s)
- Steven R Chamberlin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States
| | - Aurora Blucher
- OHSU Knight Cancer Institute, Portland, OR, United States
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
| | - Lynne Shinto
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Gabrielle Choonoo
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States
| | - Molly Kulesz-Martin
- OHSU Knight Cancer Institute, Portland, OR, United States.,Departments of Dermatology and Cell, Developmental and Cancer Biology, Oregon Health and Sciences University, Portland, OR, United States
| | - Shannon McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
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33
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Quan Y, Luo ZH, Yang QY, Li J, Zhu Q, Liu YM, Lv BM, Cui ZJ, Qin X, Xu YH, Zhu LD, Zhang HY. Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates. Front Genet 2019; 10:474. [PMID: 31191604 PMCID: PMC6549477 DOI: 10.3389/fgene.2019.00474] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 05/01/2019] [Indexed: 01/10/2023] Open
Abstract
Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).
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Affiliation(s)
- Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Zhi-Hui Luo
- College of Life Sciences and Technology, Huazhong Agricultural University, Wuhan, China
| | - Qing-Yong Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jiang Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qiang Zhu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ye-Mao Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Bo-Min Lv
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ze-Jia Cui
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xuan Qin
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yan-Hua Xu
- Sci-meds Biopharmaceutical Co., Ltd., Wuhan, China
| | - Li-Da Zhu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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34
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Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, Johnston SE, Vrcic A, Wong B, Khan M, Asiedu J, Narayan R, Mader CC, Subramanian A, Golub TR. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med 2019; 23:405-408. [PMID: 28388612 DOI: 10.1038/nm.4306] [Citation(s) in RCA: 513] [Impact Index Per Article: 102.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Steven M Corsello
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua A Bittker
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Zihan Liu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Joshua Gould
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Patrick McCarren
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jodi E Hirschman
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Anita Vrcic
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Bang Wong
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Mariya Khan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jacob Asiedu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Rajiv Narayan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | | | - Todd R Golub
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
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35
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Sachdev K, Gupta MK. A comprehensive review of feature based methods for drug target interaction prediction. J Biomed Inform 2019; 93:103159. [PMID: 30926470 DOI: 10.1016/j.jbi.2019.103159] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/22/2022]
Abstract
Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. The computational methods of interaction prediction help limit the search space for these experiments. These computational methods can be divided into ligand based approaches, docking approaches and chemogenomic approaches. In this review, we aim to describe the various feature based chemogenomic methods for drug target interaction prediction. It provides a comprehensive overview of the various techniques, datasets, tools and metrics. The feature based methods have been categorized, explained and compared. A novel framework for drug target interaction prediction has also been proposed that aims to improve the performance of existing methods. To the best of our knowledge, this is the first comprehensive review focusing only on feature based methods of drug target interaction.
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Affiliation(s)
- Kanica Sachdev
- Computer Science and Engineering Department, SMVDU, J&K, India.
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36
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PheWAS-Based Systems Genetics Methods for Anti-Breast Cancer Drug Discovery. Genes (Basel) 2019; 10:genes10020154. [PMID: 30781719 PMCID: PMC6409623 DOI: 10.3390/genes10020154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/16/2019] [Accepted: 02/04/2019] [Indexed: 11/21/2022] Open
Abstract
Breast cancer is a high-risk disease worldwide. For such complex diseases that are induced by multiple pathogenic genes, determining how to establish an effective drug discovery strategy is a challenge. In recent years, a large amount of genetic data has accumulated, particularly in the genome-wide identification of disorder genes. However, understanding how to use these data efficiently for pathogenesis elucidation and drug discovery is still a problem because the gene–disease links that are identified by high-throughput techniques such as phenome-wide association studies (PheWASs) are usually too weak to have biological significance. Systems genetics is a thriving area of study that aims to understand genetic interactions on a genome-wide scale. In this study, we aimed to establish two effective strategies for identifying breast cancer genes based on the systems genetics algorithm. As a result, we found that the GeneRank-based strategy, which combines the prognostic phenotype-based gene-dependent network with the phenotypic-related PheWAS data, can promote the identification of breast cancer genes and the discovery of anti-breast cancer drugs.
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37
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Lv BM, Tong XY, Quan Y, Liu MY, Zhang QY, Song YF, Zhang HY. Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods. Molecules 2018; 23:molecules23123346. [PMID: 30567313 PMCID: PMC6320907 DOI: 10.3390/molecules23123346] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 12/14/2018] [Accepted: 12/14/2018] [Indexed: 12/22/2022] Open
Abstract
Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.
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Affiliation(s)
- Bo-Min Lv
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xin-Yu Tong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Meng-Yuan Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yun-Feng Song
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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38
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Sun YZ, Zhang DH, Cai SB, Ming Z, Li JQ, Chen X. MDAD: A Special Resource for Microbe-Drug Associations. Front Cell Infect Microbiol 2018; 8:424. [PMID: 30581775 PMCID: PMC6292923 DOI: 10.3389/fcimb.2018.00424] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/22/2018] [Indexed: 12/11/2022] Open
Abstract
The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.
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Affiliation(s)
- Ya-Zhou Sun
- Department of Computer Science and Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - De-Hong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Shu-Bin Cai
- Department of Computer Science and Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Zhong Ming
- Department of Computer Science and Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Jian-Qiang Li
- Department of Computer Science and Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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39
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Franco-Serrano L, Huerta M, Hernández S, Cedano J, Perez-Pons J, Piñol J, Mozo-Villarias A, Amela I, Querol E. Multifunctional Proteins: Involvement in Human Diseases and Targets of Current Drugs. Protein J 2018; 37:444-453. [PMID: 30123928 PMCID: PMC6132618 DOI: 10.1007/s10930-018-9790-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Multifunctionality or multitasking is the capability of some proteins to execute two or more biochemical functions. The objective of this work is to explore the relationship between multifunctional proteins, human diseases and drug targeting. The analysis of the proportion of multitasking proteins from the MultitaskProtDB-II database shows that 78% of the proteins analyzed are involved in human diseases. This percentage is much higher than the 17.9% found in human proteins in general. A similar analysis using drug target databases shows that 48% of these analyzed human multitasking proteins are targets of current drugs, while only 9.8% of the human proteins present in UniProt are specified as drug targets. In almost 50% of these proteins, both the canonical and moonlighting functions are related to the molecular basis of the disease. A procedure to identify multifunctional proteins from disease databases and a method to structurally map the canonical and moonlighting functions of the protein have also been proposed here. Both of the previous percentages suggest that multitasking is not a rare phenomenon in proteins causing human diseases, and that their detailed study might explain some collateral drug effects.
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Affiliation(s)
- Luis Franco-Serrano
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Mario Huerta
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Sergio Hernández
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Juan Cedano
- Laboratorio de Inmunología, Universidad de la República Regional Norte-Salto, Rivera 1350, 50000, Salto, Uruguay
| | - JosepAntoni Perez-Pons
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Jaume Piñol
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Angel Mozo-Villarias
- Departament de Medicina Experimental and Institut de Recerca Biomèdica, Universitat de Lleida, 25198, Lleida, Spain
| | - Isaac Amela
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Enrique Querol
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain.
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40
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Chen R, Liu X, Jin S, Lin J, Liu J. Machine Learning for Drug-Target Interaction Prediction. Molecules 2018; 23:E2208. [PMID: 30200333 PMCID: PMC6225477 DOI: 10.3390/molecules23092208] [Citation(s) in RCA: 119] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 08/27/2018] [Accepted: 08/27/2018] [Indexed: 12/18/2022] Open
Abstract
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.
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Affiliation(s)
- Ruolan Chen
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
| | - Xiangrong Liu
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
| | - Shuting Jin
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
| | - Jiawei Lin
- Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China.
| | - Juan Liu
- Department of Instrumental and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
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41
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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42
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Sawada R, Iwata M, Umezaki M, Usui Y, Kobayashi T, Kubono T, Hayashi S, Kadowaki M, Yamanishi Y. KampoDB, database of predicted targets and functional annotations of natural medicines. Sci Rep 2018; 8:11216. [PMID: 30046160 PMCID: PMC6060122 DOI: 10.1038/s41598-018-29516-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 07/12/2018] [Indexed: 11/18/2022] Open
Abstract
Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases. Here, we present KampoDB (http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel platform for the analysis of natural medicines, which provides various useful scientific resources on Japanese traditional formulas Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these constituent compounds. Potential target proteins of these constituent compounds were predicted by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins, and has functional annotations for biological pathways and molecular functions. KampoDB is useful for mode-of-action analysis of natural medicines and prediction of new indications for a wide range of diseases.
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Affiliation(s)
- Ryusuke Sawada
- Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Masahito Umezaki
- Division of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Yoshihiko Usui
- Division of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Toshikazu Kobayashi
- Division of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Takaki Kubono
- Division of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Shusaku Hayashi
- Division of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Makoto Kadowaki
- Division of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, 332-0012, Japan.
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Chujan S, Suriyo T, Ungtrakul T, Pomyen Y, Satayavivad J. Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis. Biomed Rep 2018; 9:42-52. [PMID: 29930804 PMCID: PMC6007048 DOI: 10.3892/br.2018.1101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 05/18/2018] [Indexed: 12/14/2022] Open
Abstract
Cholangiocarcinoma (CCA) remains to be a major health problem in several Asian countries including Thailand. The molecular mechanism of CCA is poorly understood. Early diagnosis is difficult, and at present, no effective therapeutic drug is available. The present study aimed to identify the molecular mechanism of CCA by gene expression profile analysis and to search for current approved drugs which may interact with the upregulated genes in CCA. Gene Expression Omnibus (GEO) was used to analyze the gene expression profiles of CCA patients and normal subjects. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology enrichment analysis was also performed, with the KEGG pathway analysis indicating that pancreatic secretion, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism may serve important roles in CCA oncogenesis. The drug signature database (DsigDB) was used to search for US Food and Drug Administration (FDA)-approved drugs potentially capable of reversing the effects of the upregulated gene expression in CCA. A total of 61 antineoplastic and 86 non-antineoplastic drugs were identified. Checkpoint kinase 1 was the most interacting with drug signatures. Many of the targeted protein inhibitors that were identified have been approved by the US-FDA as therapeutic agents for non-antineoplastic diseases, including cimetidine, valproic acid and lovastatin. The current study demonstrated an application for bioinformatics analysis in assessing the potential efficacy of currently approved drugs for novel use. The present results suggest novel indications regarding existing drugs useful for CCA treatment. However, further in vitro and in vivo studies are required to support the current predictions.
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Affiliation(s)
- Suthipong Chujan
- Applied Biological Sciences Program, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Tawit Suriyo
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok 10400, Thailand
| | - Teerapat Ungtrakul
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Yotsawat Pomyen
- Translational Research Unit, Chulabhorn Research Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Jutamaad Satayavivad
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Education, Bangkok 10400, Thailand.,Environmental Toxicology Program, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
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44
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Pan B, Zang J, He J, Wang Z, Liu L. Add-On therapy with Chinese herb medicine Bo-Er-Ning capsule (BENC) improves outcomes of gastric cancer patients: a randomized clinical trial followed with bioinformatics-assisted mechanism study. Am J Cancer Res 2018; 8:1090-1105. [PMID: 30034946 PMCID: PMC6048393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 05/08/2018] [Indexed: 06/08/2023] Open
Abstract
As a Chinese herb medicine (CHM), Bo-Er-Ning capsule (BENC) has been approved in China for adjuvant treatment of cancer, but the particular therapeutic effect of BENC on gastric cancer (GC) has yet to be evaluated. In this study, we implemented an efficacy-driven approach by directly starting the study with a randomized clinical trial to assess the add-on therapeutic effect of BENC on advanced GC patients. Our results showed that the addition of BENC to chemotherapy resulted in higher Karnofsky performance scores and better 3-year overall survival, compared to those treated with the conventional chemotherapy regimen. Subsequently, we explored the mechanism of BENC action on GC cells in the assistance of BATMAN-TCM, the first online bioinformatics analysis tool designed especially for the mechanism study of CHM, by which we identified 263 candidate protein targets of BENC involved in GC treatment. The further enrichment analysis suggested that BENC treatment affected a diversity of biological processes of GC cells, such as cell proliferation, cell cycle and apoptosis, which were further validated in the following in vitro and in vivo assays, indicating such a bioinformatics-assisted approach was feasible and powerful to CHM mechanism study. Thus, as exemplified by BENC, we provided an efficacy-driven and bioinformatics-assisted strategy for CHM research, which may help promote the discovery and application of novel CHM drugs on patients with refractory diseases.
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Affiliation(s)
- Boyu Pan
- Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy; Tianjin’s Clinical Research Center for CancerTianjin 300060, China
| | - Jingyuan Zang
- Department of Gastroenterology, Tianjin HospitalTianjin 300211, China
| | - Jie He
- Department of Oncology, Zhangqiu People’s Hospital of ShandongJinan 250200, China
| | - Zhen Wang
- Department of Oncology, Zhangqiu People’s Hospital of ShandongJinan 250200, China
| | - Liren Liu
- Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy; Tianjin’s Clinical Research Center for CancerTianjin 300060, China
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45
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Aksam VKMD, Chandrasekaran VM, Pandurangan S. Cancer drug target identification and node-level analysis of the network of MAPK pathways. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/s13721-018-0165-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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46
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Chen X, Sun YZ, Zhang DH, Li JQ, Yan GY, An JY, You ZH. NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2017:4027556. [PMID: 29220444 PMCID: PMC5527270 DOI: 10.1093/database/bax057] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 06/30/2017] [Indexed: 11/14/2022]
Abstract
In recent years, more and more non-coding RNAs (ncRNAs) have been identified and increasing evidences have shown that ncRNAs may affect gene expression and disease progression, making them a new class of targets for drug discovery. It thus becomes important to understand the relationship between ncRNAs and drug targets. For this purpose, an ncRNAs and drug targets association database would be extremely beneficial. Here, we developed ncRNA Drug Targets Database (NRDTD) that collected 165 entries of clinically or experimentally supported ncRNAs as drug targets, including 97 ncRNAs and 96 drugs. Moreover, we annotated ncRNA-drug target associations with drug information from KEGG, PubChem, DrugBank, CTD or Wikipedia, GenBank sequence links, OMIM disease ID, pathway and function annotation for ncRNAs, detailed description of associations between ncRNAs and diseases from HMDD or LncRNADisease and the publication PubMed ID. Additionally, we provided users a link to submit novel disease-ncRNA-drug associations and corresponding supporting evidences into the database. We hope NRDTD will be a useful resource for investigating the roles of ncRNAs in drug target identification, drug discovery and disease treatment. Database URL:http://chengroup.cumt.edu.cn/NRDTD
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - De-Hong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Gui-Ying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
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47
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Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures. Sci Rep 2018; 8:156. [PMID: 29317676 PMCID: PMC5760621 DOI: 10.1038/s41598-017-18315-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 12/08/2017] [Indexed: 02/06/2023] Open
Abstract
Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.
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48
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Zhu Y, Elemento O, Pathak J, Wang F. Drug knowledge bases and their applications in biomedical informatics research. Brief Bioinform 2018; 20:1308-1321. [DOI: 10.1093/bib/bbx169] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 11/15/2017] [Indexed: 11/14/2022] Open
Abstract
Abstract
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.
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49
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Benson HE, Watterson S, Sharman JL, Mpamhanga CP, Parton A, Southan C, Harmar AJ, Ghazal P. Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway. Br J Pharmacol 2017; 174:4362-4382. [PMID: 28910500 PMCID: PMC5715582 DOI: 10.1111/bph.14037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 08/10/2017] [Accepted: 08/30/2017] [Indexed: 12/22/2022] Open
Abstract
Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.
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Affiliation(s)
- Helen E Benson
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | - Joanna L Sharman
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Chido P Mpamhanga
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Andrew Parton
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | | | - Anthony J Harmar
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Peter Ghazal
- Division of Infection and Pathway Medicine, University of Edinburgh Medical School, Edinburgh, UK.,Centre for Synthetic and Systems Biology, CH Waddington Building, King's Buildings, Edinburgh, UK
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50
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Lin Y, Mehta S, Küçük-McGinty H, Turner JP, Vidovic D, Forlin M, Koleti A, Nguyen DT, Jensen LJ, Guha R, Mathias SL, Ursu O, Stathias V, Duan J, Nabizadeh N, Chung C, Mader C, Visser U, Yang JJ, Bologa CG, Oprea TI, Schürer SC. Drug target ontology to classify and integrate drug discovery data. J Biomed Semantics 2017; 8:50. [PMID: 29122012 PMCID: PMC5679337 DOI: 10.1186/s13326-017-0161-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 10/17/2017] [Indexed: 11/12/2022] Open
Abstract
Background One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. Results As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. Conclusions DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (http://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (http://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource. Electronic supplementary material The online version of this article (10.1186/s13326-017-0161-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yu Lin
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | - Saurabh Mehta
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.,Department of Applied Chemistry, Delhi Technological University, Delhi, India
| | - Hande Küçük-McGinty
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.,Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - John Paul Turner
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Dusica Vidovic
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Michele Forlin
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Amar Koleti
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, Rockville, MD, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rajarshi Guha
- National Center for Advancing Translational Science, Rockville, MD, USA
| | - Stephen L Mathias
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Oleg Ursu
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jianbin Duan
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.,Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Nooshin Nabizadeh
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | - Caty Chung
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | - Christopher Mader
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | - Ubbo Visser
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Jeremy J Yang
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Cristian G Bologa
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Department of Internal Medicine, Translational Informatics Division, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Stephan C Schürer
- Center for Computational Science, University of Miami, Coral Gables, FL, USA. .,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
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