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Tripathi T, Singh DB, Tripathi T. Computational resources and chemoinformatics for translational health research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:27-55. [PMID: 38448138 DOI: 10.1016/bs.apcsb.2023.11.003] [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: 03/08/2024]
Abstract
The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.
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Affiliation(s)
- Tripti Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong, India
| | - Dev Bukhsh Singh
- Department of Biotechnology, Siddharth University, Kapilvastu, Siddharth Nagar, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Zoology, North-Eastern Hill University, Shillong, India.
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CHENG K, YUAN J, LIU J, ZHANG S, XU Q, XIE Y, ZHAO J, ZHANG X, TANG X, ZHENG Y, WANG Z. Identifying Qingkailing ingredients-dependent mesenchymal-epithelial transition factor-axiation "π" structuring module with angiogenesis and neurogenesis effects. J TRADIT CHIN MED 2024; 44:35-43. [PMID: 38213237 PMCID: PMC10774727 DOI: 10.19852/j.cnki.jtcm.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/22/2023] [Indexed: 01/13/2024]
Abstract
OBJECTIVE To explore the functional role of the drug-dependent mesenchymal-epithelial transition (Met)-axiation "π" structural module of neurogenesis after processing by three components of Qingkailing injection in neurogenesis and angiogenesis in cerebral ischemia. METHODS We used a Glutathione S-transferase (GST)-pull down assay, isothermal titration calorimetry assay, and other related methods to identify the relationships among Met, inositol polyphosphate phosphatase like 1 (Inppl1), and death associated protein kinase 3 (Dapk3) in this allosteric module. The biological effects of the modules of neurons generation composed of Met, Inppl1, and Dapk3 were measured through Western blot, apoptosis analysis, and double immunofluorescence labeling. RESULTS The GST-pull down assay revealed that proline-serine-threonine rich domain of Met binds to the Src homology domain of Inppl1 to form a protein-protein complex; Dapk3 with a C-terminal domain interacts weakly with the protein kinase C domain of Met in the intracellular region. Thus, we obtained a "π" structuring module considered a neural regeneration module. The biological effects of angiogenesis and neurogenesis modules composed of Met, Inppl1, and Dapk3 were also verified. CONCLUSION The study suggested that understanding the functional modules that contribute to pharmaceutics might provide novel signatures that can be used as endpoints to define disease processes under stroke or cerebral ischemia conditions.
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Affiliation(s)
- Kunming CHENG
- 1 Provincial Engineering Laboratory for Screening and Re-evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Teaching and Research Section of Traditional Chinese Medicine, School of Pharmacy, Wannan Medical College, Wuhu 241000, China
| | - Jianan YUAN
- 1 Provincial Engineering Laboratory for Screening and Re-evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Teaching and Research Section of Traditional Chinese Medicine, School of Pharmacy, Wannan Medical College, Wuhu 241000, China
| | - Jun LIU
- 2 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shengpeng ZHANG
- 1 Provincial Engineering Laboratory for Screening and Re-evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Teaching and Research Section of Traditional Chinese Medicine, School of Pharmacy, Wannan Medical College, Wuhu 241000, China
| | - Qixiang XU
- 1 Provincial Engineering Laboratory for Screening and Re-evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Teaching and Research Section of Traditional Chinese Medicine, School of Pharmacy, Wannan Medical College, Wuhu 241000, China
| | - Yong XIE
- 3 Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Department of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Jingfeng ZHAO
- 3 Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Department of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
| | - Xiaoxu ZHANG
- 2 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xudong TANG
- 4 Department of Gastroenterology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Yongqiu ZHENG
- 1 Provincial Engineering Laboratory for Screening and Re-evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Teaching and Research Section of Traditional Chinese Medicine, School of Pharmacy, Wannan Medical College, Wuhu 241000, China
| | - Zhong WANG
- 2 Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
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Tithi TI, Tahsin MR, Anjum J, Zaman TS, Aktar F, Bahar NB, Tasnim S, Sultana A, Jahan I, Afrin SS, Akter T, Sen P, Koly FJ, Reza MS, Chowdhury JA, Kabir S, Chowdhury AA, Amran MS. An in vivo and in silico evaluation of the hepatoprotective potential of Gynura procumbens: A promising agent for combating hepatotoxicity. PLoS One 2023; 18:e0291125. [PMID: 37713406 PMCID: PMC10503776 DOI: 10.1371/journal.pone.0291125] [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: 10/03/2022] [Accepted: 08/22/2023] [Indexed: 09/17/2023] Open
Abstract
INTRODUCTION The liver, the most important metabolic organ of the body, performs a wide variety of vital functions. Hepatic cell injury occurs by the activation of reactive oxygen species (ROS) that are generated by carbon tetrachloride (CCl4), xenobiotics, and other toxic substances through cytochrome P450-dependent steps resulting from the covalent bond formation with lipoproteins and nucleic acids. Observing the urgent state of hepatotoxic patients worldwide, different medicinal plants and their properties can be explored to combat such free radical damage to the liver. In vivo and in silico studies were designed and conducted to evaluate the antioxidant and hepatoprotective properties of Gynura procumbens in rats. MATERIALS AND METHODS Gynura procumbens leaves were collected and extracted using 70% ethanol. The required chemicals CCl4, standard drug (silymarin), and blood serum analysis kits were stocked. The in vivo tests were performed in 140 healthy Wister albino rats of either sex under well-controlled parameters divided into 14 groups, strictly maintaining Institutional Animal Ethics Committee (IEAC) protocols. For the histopathology study, 10% buffered neutral formalin was used for organ preservation. Later the specimens were studied under a fluorescence microscope. In silico molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were performed, and the results were analyzed statistically. RESULTS AND DISCUSSION Gynura procumbens partially negate the deleterious effect of carbon tetrachloride on normal weight gain in rats. The elevated level of serum glutamate pyruvate transaminase (SGPT), serum glutamate oxaloacetate transaminase (SGOT), alkaline phosphatase (ALP), creatinine, LDH, total cholesterol (TC), low-density lipoprotein (LDL), triglycerides (TG), malondialdehyde (MDA), deoxyribonucleic acid (DNA) fragmentation ranges, gamma-glutamyl transferase (γ-GT) in CCl4 treated groups were decreased by both standard drug silymarin and G. procumbens leaf extract. We have found significant & highly significant changes statistically for different doses, here p<0.05 & p<0.01, respectively. On the other hand, G. procumbens and silymarin displayed Statistically significant (p<0.05) and high significant(p<0.01) increased levels of HDL, CAT SOD (here p<0.05 & p<0.01 for different doses) when the treatment groups were compared with the disease control group. Because the therapeutic activity imparted by plants and drugs accelerates the movement of the disturbed pathophysiological state toward the healthy state. In the molecular docking analysis, G. procumbens phytoconstituents performed poorly against transforming growth factor-beta 1 (TGF-β1) compared to the control drug silymarin. In contrast, 26 phytoconstituents scored better than the control bezafibrate against peroxisome proliferator-activated receptor alpha (PPAR-α). The top scoring compounds for both macromolecules were observed to form stable complexes in the molecular dynamics simulations. Flavonoids and phenolic compounds performed better than other constituents in providing hepatoprotective activity. It can, thus, be inferred that the extract of G. procumbens showed good hepatoprotective properties in rats.
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Affiliation(s)
- Tanzia Islam Tithi
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Md. Rafat Tahsin
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
| | - Juhaer Anjum
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | | | - Fahima Aktar
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Nasiba Binte Bahar
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Sabiha Tasnim
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Arifa Sultana
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Ishrat Jahan
- Department of Pharmacy, University of Asia Pacific, Farmgate, Dhaka, Bangladesh
| | | | - Tahmina Akter
- Department of Physiology, Dhaka Medical College, Dhaka, Bangladesh
| | - Priyanka Sen
- Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Fahima Jannat Koly
- Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Md. Selim Reza
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Jakir Ahmed Chowdhury
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Shaila Kabir
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Abu Asad Chowdhury
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Md. Shah Amran
- Molecular Pharmacology and Herbal Drug Research Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
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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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Murad T, Ali S, Patterson M. Exploring the Potential of GANs in Biological Sequence Analysis. BIOLOGY 2023; 12:854. [PMID: 37372139 DOI: 10.3390/biology12060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
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Affiliation(s)
- Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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D’Souza S, Prema KV, Balaji S, Shah R. Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins. INTERDISCIPLINARY SCIENCES: COMPUTATIONAL LIFE SCIENCES 2023; 15:306-315. [PMID: 36967455 PMCID: PMC10148762 DOI: 10.1007/s12539-023-00557-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/29/2023]
Abstract
AbstractChemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand–target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability.
Graphical abstract
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Affiliation(s)
- Sofia D’Souza
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal, India
| | - K. V. Prema
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Bengaluru, India
| | - S. Balaji
- Department of Biotechnology, Manipal Academy of Higher Education, Manipal, India
| | - Ronak Shah
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal, India
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Muniyappan S, Rayan AXA, Varrieth GT. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9530-9571. [PMID: 37161255 DOI: 10.3934/mbe.2023419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
MOTIVATION In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). METHODS In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. RESULTS The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, India
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Bongers BJ, Sijben HJ, Hartog PBR, Tarnovskiy A, IJzerman AP, Heitman LH, van Westen GJP. Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors. J Chem Inf Model 2023; 63:1745-1755. [PMID: 36926886 PMCID: PMC10052348 DOI: 10.1021/acs.jcim.2c01645] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.
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Affiliation(s)
- Brandon J Bongers
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Huub J Sijben
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Peter B R Hartog
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | | | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands.,Oncode Institute, Jaarbeursplein 6, Utrecht 3521 AL, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
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Abbasi Mesrabadi H, Faez K, Pirgazi J. Drug-target interaction prediction based on protein features, using wrapper feature selection. Sci Rep 2023; 13:3594. [PMID: 36869062 PMCID: PMC9984486 DOI: 10.1038/s41598-023-30026-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/14/2023] [Indexed: 03/05/2023] Open
Abstract
Drug-target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug-target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.
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Affiliation(s)
- Hengame Abbasi Mesrabadi
- Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Karim Faez
- Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Jamshid Pirgazi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
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Lin X, Quan Z, Wang ZJ, Guo Y, Zeng X, Yu PS. Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:932-943. [PMID: 35951570 DOI: 10.1109/tcbb.2022.3198003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein strings and/or the specific descriptors. However, they often ignore the fact that molecules are essentially modeled by the molecular graph. We observe that in real-world scenarios, the topological structure information of the molecular graph usually provides an overview of how the atoms are connected, and the local chemical context reveals the functionality of the protein sequence in CPI. These two types of information are complementary to each other and they are both significant for modeling compound-protein pairs. Motivated by this, we propose an end-to-end deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task. Our framework can integrate any popular graph neural networks for learning compounds, and it combines with a convolutional neural network for embedding sequences. To compare our method with classic and state-of-the-art deep learning methods, we conduct extensive experiments based on several widely-used CPI datasets. The experimental results show the feasibility and competitiveness of our proposed method.
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Current Pharmacotherapy and Multi-Target Approaches for Alzheimer's Disease. Pharmaceuticals (Basel) 2022; 15:ph15121560. [PMID: 36559010 PMCID: PMC9781592 DOI: 10.3390/ph15121560] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by decreased synaptic transmission and cerebral atrophy with appearance of amyloid plaques and neurofibrillary tangles. Cognitive, functional, and behavioral alterations are commonly associated with the disease. Different pathophysiological pathways of AD have been proposed, some of which interact and influence one another. Current treatment for AD mainly involves the use of therapeutic agents to alleviate the symptoms in AD patients. The conventional single-target treatment approaches do not often cause the desired effect in the disease due to its multifactorial origin. Thus, multi-target strategies have since been undertaken, which aim to simultaneously target multiple targets involved in the development of AD. In this review, we provide an overview of the pathogenesis of AD and the current drug therapies for the disease. Additionally, rationales of the multi-target approaches and examples of multi-target drugs with pharmacological actions against AD are also discussed.
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13
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Gorin G, Vastola JJ, Fang M, Pachter L. Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments. Nat Commun 2022; 13:7620. [PMID: 36494337 PMCID: PMC9734650 DOI: 10.1038/s41467-022-34857-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 11/09/2022] [Indexed: 12/13/2022] Open
Abstract
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.
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Affiliation(s)
- Gennady Gorin
- grid.20861.3d0000000107068890Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125 USA
| | - John J. Vastola
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
| | - Meichen Fang
- grid.20861.3d0000000107068890Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 USA
| | - Lior Pachter
- grid.20861.3d0000000107068890Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 USA ,grid.20861.3d0000000107068890Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 USA
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14
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Maghsoudi S, Taghavi Shahraki B, Rameh F, Nazarabi M, Fatahi Y, Akhavan O, Rabiee M, Mostafavi E, Lima EC, Saeb MR, Rabiee N. A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery. Chem Biol Drug Des 2022; 100:699-721. [PMID: 36002440 PMCID: PMC9539342 DOI: 10.1111/cbdd.14136] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/07/2022] [Accepted: 08/21/2022] [Indexed: 11/29/2022]
Abstract
Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.
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Affiliation(s)
- Saeid Maghsoudi
- Faculty of Medicine, Department of Physiology and PathophysiologyUniversity of ManitobaWinnipegManitobaCanada
- Biology of Breathing Group, Children's Hospital Research Institute of Manitoba (CHRIM), University of ManitobaWinnipegManitobaCanada
| | | | | | - Masoomeh Nazarabi
- Faculty of Organic Chemistry, Department of ChemistryUniversity of KashanKashanIran
| | - Yousef Fatahi
- Department of Pharmaceutical Nanotechnology, Faculty of PharmacyTehran University of Medical SciencesTehranIran
- Nanotechnology Research Center, Faculty of PharmacyTehran University of Medical SciencesTehranIran
| | - Omid Akhavan
- Department of PhysicsSharif University of TechnologyTehranIran
| | - Mohammad Rabiee
- Biomaterials Group, Department of Biomedical EngineeringAmirkabir University of TechnologyTehranIran
| | - Ebrahim Mostafavi
- Stanford Cardiovascular Institute, Stanford University School of MedicineStanfordCaliforniaUSA
- Department of MedicineStanford University School of MedicineStanfordCaliforniaUSA
| | - Eder C. Lima
- Institute of Chemistry, Federal University of Rio Grande Do Sul (UFRGS)Porto AlegreBrazil
| | - Mohammad Reza Saeb
- Department of Polymer Technology, Faculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Navid Rabiee
- Department of PhysicsSharif University of TechnologyTehranIran
- School of EngineeringMacquarie UniversitySydneyNew South WalesAustralia
- Department of Materials Science and EngineeringPohang University of Science and Technology (POSTECH)PohangSouth Korea
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15
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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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16
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Qian Y, Li X, Wu J, Zhou A, Xu Z, Zhang Q. Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds. J Comput Chem 2022; 43:255-264. [PMID: 34846751 DOI: 10.1002/jcc.26786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/05/2022]
Abstract
Identifying potential associations between proteins and compounds is significant and challenging in the drug discovery process. Existing deep-learning-based methods tend to treat compounds and proteins as sequences or graphs. Inspired by the rapid development of computer vision technology, we argue that more abundant characterizations can be extracted from the images of compounds than from their sequences or graphs. Therefore, we propose an interaction model named picture-word order compound protein interaction (PWO-CPI) which learns the representation from structural images of compounds and protein sequences by using convolutional neural network (CNN). The experiments show that PWO-CPI outperforms state-of-the-art CPI prediction models. We also perform drug-drug interaction (DDI) experiments to validate the strong potential of structural formula images of molecular structures as molecular features. In addition, with the aid of generative adversarial networks, the visualization of image features demonstrates PWO-CPI can learn compound structural features implicitly and automatically.
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Affiliation(s)
- Ying Qian
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Xuelian Li
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Jian Wu
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Aimin Zhou
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Zhijian Xu
- Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Qian Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
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17
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Fereshteh S, Kalhor H, Sepehr A, Rahimi H, Zafari M, Ahangari Cohan R, Badmasti F. Rational design of inhibitors against LpxA protein of Acinetobacter baumannii using a virtual screening method. J INDIAN CHEM SOC 2022. [DOI: 10.1016/j.jics.2021.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Dafniet B, Cerisier N, Boezio B, Clary A, Ducrot P, Dorval T, Gohier A, Brown D, Audouze K, Taboureau O. Development of a chemogenomics library for phenotypic screening. J Cheminform 2021; 13:91. [PMID: 34819133 PMCID: PMC8611952 DOI: 10.1186/s13321-021-00569-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/06/2021] [Indexed: 12/03/2022] Open
Abstract
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.
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Affiliation(s)
- Bryan Dafniet
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Natacha Cerisier
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Batiste Boezio
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Anaelle Clary
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Thierry Dorval
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Arnaud Gohier
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - David Brown
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Karine Audouze
- Université de Paris, INSERM UMR S-1124, 75006, Paris, France
| | - Olivier Taboureau
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France.
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19
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Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB. Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review. Int J Mol Sci 2021; 22:8962. [PMID: 34445667 PMCID: PMC8396480 DOI: 10.3390/ijms22168962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023] Open
Abstract
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Gayatri Gandhi
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Jian Ming Lee
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Wendy Wai Yeng Yeo
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Sy-Bing Choi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
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20
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Konagurthu AS, Subramanian R, Allison L, Abramson D, Stuckey PJ, Garcia de la Banda M, Lesk AM. Universal Architectural Concepts Underlying Protein Folding Patterns. Front Mol Biosci 2021; 7:612920. [PMID: 33996891 PMCID: PMC8120156 DOI: 10.3389/fmolb.2020.612920] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/16/2020] [Indexed: 11/17/2022] Open
Abstract
What is the architectural “basis set” of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures—called concepts—typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.
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Affiliation(s)
- Arun S Konagurthu
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Ramanan Subramanian
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Lloyd Allison
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - David Abramson
- Research Computing Center, University of Queensland, Brisbane, QLD, Australia
| | - Peter J Stuckey
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia
| | - Maria Garcia de la Banda
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Arthur M Lesk
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, United States.,MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
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21
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Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy. NPJ Syst Biol Appl 2021; 7:11. [PMID: 33589646 PMCID: PMC7884732 DOI: 10.1038/s41540-021-00171-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/13/2021] [Indexed: 01/31/2023] Open
Abstract
Advancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from "one-target, one-drug" to "target-network, multi-component therapeutics". We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein-protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.
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22
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Borba JVVB, Silva AC, Lima MNN, Mendonca SS, Furnham N, Costa FTM, Andrade CH. Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 124:187-223. [PMID: 33632465 DOI: 10.1016/bs.apcsb.2020.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.
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Affiliation(s)
- Joyce Villa Verde Bastos Borba
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Arthur Carvalho Silva
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Marilia Nunes Nascimento Lima
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Sabrina Silva Mendonca
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fabio Trindade Maranhão Costa
- Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Carolina Horta Andrade
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
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23
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Wu S, Liu C, Feng J, Yang A, Guo F, Qiao J. QSIdb: quorum sensing interference molecules. Brief Bioinform 2020; 22:5916938. [PMID: 33003203 DOI: 10.1093/bib/bbaa218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 12/30/2022] Open
Abstract
Quorum sensing interference (QSI), the disruption and manipulation of quorum sensing (QS) in the dynamic control of bacteria populations could be widely applied in synthetic biology to realize dynamic metabolic control and develop potential clinical therapies. Conventionally, limited QSI molecules (QSIMs) were developed based on molecular structures or for specific QS receptors, which are in short supply for various interferences and manipulations of QS systems. In this study, we developed QSIdb (http://qsidb.lbci.net/), a specialized repository of 633 reported QSIMs and 73 073 expanded QSIMs including both QS agonists and antagonists. We have collected all reported QSIMs in literatures focused on the modifications of N-acyl homoserine lactones, natural QSIMs and synthetic QS analogues. Moreover, we developed a pipeline with SMILES-based similarity assessment algorithms and docking-based validations to mine potential QSIMs from existing 138 805 608 compounds in the PubChem database. In addition, we proposed a new measure, pocketedit, for assessing the similarities of active protein pockets or QSIMs crosstalk, and obtained 273 possible potential broad-spectrum QSIMs. We provided user-friendly browsing and searching facilities for easy data retrieval and comparison. QSIdb could assist the scientific community in understanding QS-related therapeutics, manipulating QS-based genetic circuits in metabolic engineering, developing potential broad-spectrum QSIMs and expanding new ligands for other receptors.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Chunjiang Liu
- State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin, China
| | - Jie Feng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jianjun Qiao
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University) and Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin, China
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24
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Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 2020; 12:46. [PMID: 33431024 PMCID: PMC7374666 DOI: 10.1186/s13321-020-00450-7] [Citation(s) in RCA: 130] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/13/2023] Open
Abstract
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.
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Affiliation(s)
- Tamer N Jarada
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Jon G Rokne
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
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25
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Wang W, Lv H, Zhao Y. Predicting DNA binding protein-drug interactions based on network similarity. BMC Bioinformatics 2020; 21:322. [PMID: 32689927 PMCID: PMC7372772 DOI: 10.1186/s12859-020-03664-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 07/15/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The study of DNA binding protein (DBP)-drug interactions can open a breakthrough for the treatment of genetic diseases and cancers. Currently, network-based methods are widely used for protein-drug interaction prediction, and many hidden relationships can be found through network analysis. We proposed a DCA (drug-cluster association) model for predicting DBP-drug interactions. The clusters are some similarities in the drug-binding site trimmers with their physicochemical properties. First, DBPs-drug binding sites are extracted from scPDB database. Second, each binding site is represented as a trimer which is obtained by sliding the window in the binding sites. Third, the trimers are clustered based on the physicochemical properties. Fourth, we build the network by generating the interaction matrix for representing the DCA network. Fifth, three link prediction methods are detected in the network. Finally, the common neighbor (CN) method is selected to predict drug-cluster associations in the DBP-drug network model. RESULT This network shows that drugs tend to bind to positively charged sites and the binding process is more likely to occur inside the DBPs. The results of the link prediction indicate that the CN method has better prediction performance than the PA and JA methods. The DBP-drug network prediction model is generated by using the CN method which predicted more accurately drug-trimer interactions and DBP-drug interactions. Such as, we found that Erythromycin (ERY) can establish an interaction relationship with HTH-type transcriptional repressor, which is fitted well with silico DBP-drug prediction. CONCLUSION The drug and protein bindings are local events. The binding of the drug-DBPs binding site represents this local binding event, which helps to understand the mechanism of DBP-drug interactions.
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Affiliation(s)
- Wei Wang
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China. .,Big Data Engineering Laboratory for Teaching Resources & assessment of Education Quality, Henan Province, Xinxiang, China.
| | - Hehe Lv
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
| | - Yuan Zhao
- Department of Computer Science and Technology, College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China
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Eguida M, Rognan D. A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design. J Med Chem 2020; 63:7127-7142. [DOI: 10.1021/acs.jmedchem.0c00422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Merveille Eguida
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
| | - Didier Rognan
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
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27
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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Vaz WF, Custodio JMF, D'Oliveira GDC, Neves BJ, Junior PSC, Filho JTM, Andrade CH, Perez CN, Silveira-Lacerda EP, Napolitano HB. Dihydroquinoline derivative as a potential anticancer agent: synthesis, crystal structure, and molecular modeling studies. Mol Divers 2020; 25:55-66. [PMID: 31900682 DOI: 10.1007/s11030-019-10024-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 12/05/2019] [Indexed: 01/16/2023]
Abstract
Cancer is one of the leading causes of death worldwide and requires intense and growing research investments from the public and private sectors. This is expected to lead to the development of new medicines. A determining factor in this process is the structural understanding of molecules with potential anticancer properties. Since the major compounds used in cancer therapies fail to encompass every spectrum of this disease, there is a clear need to research new molecules for this purpose. As it follows, we have studied the class of quinolinones that seem effective for such therapy. This paper describes the structural elucidation of a novel dihydroquinoline by single-crystal X-ray diffraction and spectroscopy characterization. Topology studies were carried through Hirshfeld surfaces analysis and molecular electrostatic potential map; electronic stability was evaluated from the calculated energy of frontier molecular orbitals. Additionally, in silico studies by molecular docking indicated that this dihydroquinoline could act as an anticancer agent due to their higher binding affinity with human aldehyde dehydrogenase 1A1 (ALDH 1A1). Tests in vitro were performed for VERO (normal human skin keratinocytes), B16F10 (mouse melanoma), and MDA-MB-231 (metastatic breast adenocarcinoma), and the results certified that compound as a potential anticancer agent. A Dihydroquinoline derivative was tested against three cancer cell lines and the results attest that compound as potential anticancer agent.
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Affiliation(s)
- W F Vaz
- Universidade Estadual de Goiás, Anápolis, GO, 75132-400, Brazil.
- Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso, Lucas do Rio Verde, MT, 78455-000, Brazil.
| | - J M F Custodio
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | | | - B J Neves
- LabMol, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - P S C Junior
- Universidade Federal de Mato Grosso do Sul, Nova Andradina, MS, 79750-000, Brazil
| | - J T M Filho
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | - C H Andrade
- LabMol, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - C N Perez
- Universidade Federal de Goiás, Goiânia, GO, 74690-900, Brazil
| | - E P Silveira-Lacerda
- Laboratório de Genética Molecular e Citogenética, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, 74605-170, Brazil
| | - H B Napolitano
- Universidade Estadual de Goiás, Anápolis, GO, 75132-400, Brazil
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Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases. Curr Med Chem 2019. [DOI: 10.2174/0929867325666180309114824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs)
have reached clinical trials in the last decades, underscoring the need for new, safe and effective
treatments. In such context, drug repositioning, which allows finding novel indications
for approved drugs whose pharmacokinetic and safety profiles are already known,
emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent
of the typical drug discovery process that involves the systematic screening of chemical
compounds against drug targets in high-throughput screening (HTS) efforts, for the identification
of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics
attempts to identify all potential ligands for all possible targets and diseases. In
this review, we summarize current methodological development efforts in drug repositioning
that use state-of-the-art computational ligand- and structure-based chemogenomics approaches.
Furthermore, we highlighted the recent progress in computational drug repositioning
for some NTDs, based on curation and modeling of genomic, biological, and chemical data.
Additionally, we also present in-house and other successful examples and suggest possible solutions
to existing pitfalls.
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Affiliation(s)
- Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Bruno Junior Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Cleber Camilo Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Juliana Rodrigues
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Diego Cabral Silva
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Rodolpho Campos Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Pedro Vitor Lemos Cravo
- Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil
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Lima MNN, Neves BJ, Cassiano GC, Gomes MN, Tomaz KCP, Ferreira LT, Tavella TA, Calit J, Bargieri DY, Muratov EN, Costa FTM, Andrade CH. Chalcones as a basis for computer-aided drug design: innovative approaches to tackle malaria. Future Med Chem 2019; 11:2635-2646. [PMID: 31556721 PMCID: PMC7333642 DOI: 10.4155/fmc-2018-0255] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 07/10/2019] [Indexed: 11/17/2022] Open
Abstract
Aim: Computer-aided drug design approaches were applied to identify chalcones with antiplasmodial activity. Methodology: The virtual screening was performed as follows: structural standardization of in-house database of chalcones; identification of potential Plasmodium falciparum protein targets for the chalcones; homology modeling of the predicted P. falciparum targets; molecular docking studies; and in vitro experimental validation. Results: Using these models, we prioritized 16 chalcones with potential antiplasmodial activity, for further experimental evaluation. Among them, LabMol-86 and LabMol-87 showed potent in vitro antiplasmodial activity against P. falciparum, while LabMol-63 and LabMol-73 were potent inhibitors of Plasmodium berghei progression into mosquito stages. Conclusion: Our results encourage the exploration of chalcones in hit-to-lead optimization studies for tackling malaria.
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Affiliation(s)
- Marilia NN Lima
- LabMol, Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Rua 240, Qd. 87, Goiânia, GO 74605-170, Brazil
| | - Bruno J Neves
- LabMol, Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Rua 240, Qd. 87, Goiânia, GO 74605-170, Brazil
- Laboratory of Cheminformatics, University Center of Anápolis (UniEVANGÉLICA), Anápolis, GO 75083-515, Brazil
| | - Gustavo C Cassiano
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
| | - Marcelo N Gomes
- LabMol, Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Rua 240, Qd. 87, Goiânia, GO 74605-170, Brazil
- Metropolitan College of Anápolis, FAMA, Anápolis, GO 75064-780, Brazil
- InSiChem Drug Discovery, Anápolis, GO 75132-903, Brazil
| | - Kaira CP Tomaz
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
| | - Leticia T Ferreira
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
| | - Tatyana A Tavella
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
| | - Juliana Calit
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Daniel Y Bargieri
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology & Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27955-7568, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Fabio TM Costa
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
| | - Carolina Horta Andrade
- LabMol, Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Rua 240, Qd. 87, Goiânia, GO 74605-170, Brazil
- Laboratory of Tropical Diseases, Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology & Immunology, Institute of Biology, UNICAMP, Campinas, SP 13083-970, Brazil
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 221] [Impact Index Per Article: 44.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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32
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Wei Y, Li W, Du T, Hong Z, Lin J. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method. Int J Mol Sci 2019; 20:ijms20143572. [PMID: 31336592 PMCID: PMC6678913 DOI: 10.3390/ijms20143572] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 07/13/2019] [Accepted: 07/21/2019] [Indexed: 12/11/2022] Open
Abstract
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.
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Affiliation(s)
- Yu Wei
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Wei Li
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China
| | - Tengfei Du
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Zhangyong Hong
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China.
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China.
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
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Zhang W, Huai Y, Miao Z, Qian A, Wang Y. Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front Pharmacol 2019; 10:743. [PMID: 31379563 PMCID: PMC6657703 DOI: 10.3389/fphar.2019.00743] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/07/2019] [Indexed: 01/01/2023] Open
Abstract
As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.
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Affiliation(s)
- Wenjuan Zhang
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Ying Huai
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Zhiping Miao
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Airong Qian
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Yonghua Wang
- Lab of Systems Pharmacology, College of Life Sciences, Northwest University, Xi’an, China
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Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Sci Rep 2019; 9:7703. [PMID: 31118426 PMCID: PMC6531441 DOI: 10.1038/s41598-019-43125-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 04/12/2019] [Indexed: 02/08/2023] Open
Abstract
Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5–98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.
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Cheong SL, Federico S, Spalluto G, Klotz KN, Pastorin G. The current status of pharmacotherapy for the treatment of Parkinson's disease: transition from single-target to multitarget therapy. Drug Discov Today 2019; 24:1769-1783. [PMID: 31102728 DOI: 10.1016/j.drudis.2019.05.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 04/02/2019] [Accepted: 05/10/2019] [Indexed: 12/23/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons. Motor features such as tremor, rigidity, bradykinesia and postural instability are common traits of PD. Current treatment options provide symptomatic relief to the condition but are unable to reverse disease progression. The conventional single-target therapeutic approach might not always induce the desired effect owing to the multifactorial nature of PD. Hence, multitarget strategies have been proposed to simultaneously target multiple proteins involved in the development of PD. Herein, we provide an overview of the pathogenesis of PD and the current pharmacotherapies. Furthermore, rationales and examples of multitarget approaches that have been tested in preclinical trials for the treatment of PD are also discussed.
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Affiliation(s)
- Siew L Cheong
- Department of Pharmaceutical Chemistry, School of Pharmacy, International Medical University, Malaysia.
| | - Stephanie Federico
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Italy
| | - Giampiero Spalluto
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Italy
| | - Karl-Norbert Klotz
- Institut für Pharmakologie und Toxikologie, Universität Würzburg, Germany
| | - Giorgia Pastorin
- Department of Pharmacy, National University of Singapore, Singapore
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Drewry DH, Wells CI, Zuercher WJ, Willson TM. A Perspective on Extreme Open Science: Companies Sharing Compounds without Restriction. SLAS DISCOVERY 2019; 24:505-514. [PMID: 31034310 DOI: 10.1177/2472555219838210] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Although the human genome provides the blueprint for life, most of the proteins it encodes remain poorly studied. This perspective describes how one group of scientists, in seeking new targets for drug discovery, used open science through unrestricted sharing of small molecules to shed light on dark matter of the genome. Starting initially with a single pharmaceutical company before expanding to multiple companies, a precedent was established for sharing published kinase inhibitors as chemical tools. The integration of open science and kinase chemogenomics has supported the study of many new potential drug targets by the scientific community.
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Affiliation(s)
- David H Drewry
- 1 Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carrow I Wells
- 1 Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William J Zuercher
- 1 Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Timothy M Willson
- 1 Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Lv Y, Hou X, Zhang Q, Li R, Xu L, Chen Y, Tian Y, Sun R, Zhang Z, Xu F. Untargeted Metabolomics Study of the In Vitro Anti-Hepatoma Effect of Saikosaponin d in Combination with NRP-1 Knockdown. Molecules 2019; 24:molecules24071423. [PMID: 30978940 PMCID: PMC6480384 DOI: 10.3390/molecules24071423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 04/03/2019] [Accepted: 04/10/2019] [Indexed: 12/22/2022] Open
Abstract
Saikosaponin d (SSd) is one of the main active ingredients in Radix Bupleuri. In our study, network pharmacology databases and metabolomics were used in combination to explore the new targets and reveal the in-depth mechanism of SSd. A total of 35 potential targets were chosen through database searching (HIT and TCMID), literature mining, or chemical similarity predicting (Pubchem). Out of these obtained targets, Neuropilin-1 (NRP-1) was selected for further research based on the degree of molecular docking scores and novelty. Cell viability and wound healing assays demonstrated that SSd combined with NRP-1 knockdown could significantly enhance the damage of HepG2. Metabolomics analysis was then performed to explore the underlying mechanism. The overall difference between groups was quantitatively evaluated by the metabolite deregulation score (MDS). Results showed that NRP-1 knockdown exhibited the lowest MDS, which demonstrated that the metabolic profile experienced the slightest interference. However, SSd alone, or NRP-1 knockdown in combination with SSd, were both significantly influenced. Differential metabolites mainly involved short- or long-chain carnitines and phospholipids. Further metabolic pathway analysis revealed that disturbed lipid transportation and phospholipid metabolism probably contributed to the enhanced anti-hepatoma effect by NRP-1 knockdown in combination with SSd. Taken together, in this study, we provided possible interaction mechanisms between SSd and its predicted target NRP-1.
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Affiliation(s)
- Yingtong Lv
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Xiaoying Hou
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Qianqian Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Ruiting Li
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Lei Xu
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Yadong Chen
- Department of Organic Chemistry, China Pharmaceutical University, Nanjing 210009, China.
| | - Yuan Tian
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Rong Sun
- Advanced Medical Research Institute, Shandong University, Jinan 250100, China.
| | - Zunjian Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
| | - Fengguo Xu
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
- Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
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Pérez-Sianes J, Pérez-Sánchez H, Díaz F. Virtual Screening Meets Deep Learning. Curr Comput Aided Drug Des 2019; 15:6-28. [PMID: 30338743 DOI: 10.2174/1573409914666181018141602] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 10/08/2018] [Accepted: 10/11/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. OBJECTIVE This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
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Affiliation(s)
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
| | - Fernando Díaz
- Departamento de Informática, Escuela de Ingeniería Informática, University of Valladolid, Segovia, Spain
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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Ferreira DD, Mesquita JT, da Costa Silva TA, Romanelli MM, da Gama Jaen Batista D, da Silva CF, da Gama ANS, Neves BJ, Melo-Filho CC, Correia Soeiro MDN, Andrade CH, Tempone AG. Efficacy of sertraline against Trypanosoma cruzi: an in vitro and in silico study. J Venom Anim Toxins Incl Trop Dis 2018; 24:30. [PMID: 30450114 PMCID: PMC6208092 DOI: 10.1186/s40409-018-0165-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 10/05/2018] [Indexed: 01/07/2023] Open
Abstract
Background Drug repurposing has been an interesting and cost-effective approach, especially for neglected diseases, such as Chagas disease. Methods In this work, we studied the activity of the antidepressant drug sertraline against Trypanosoma cruzi trypomastigotes and intracellular amastigotes of the Y and Tulahuen strains, and investigated its action mode using cell biology and in silico approaches. Results Sertraline demonstrated in vitro efficacy against intracellular amastigotes of both T. cruzi strains inside different host cells, including cardiomyocytes, with IC50 values between 1 to 10 μM, and activity against bloodstream trypomastigotes, with IC50 of 14 μM. Considering the mammalian cytotoxicity, the drug resulted in a selectivity index of 17.8. Sertraline induced a change in the mitochondrial integrity of T. cruzi, resulting in a decrease in ATP levels, but not affecting reactive oxygen levels or plasma membrane permeability. In silico approaches using chemogenomic target fishing, homology modeling and molecular docking suggested the enzyme isocitrate dehydrogenase 2 of T. cruzi (TcIDH2) as a potential target for sertraline. Conclusions The present study demonstrated that sertraline had a lethal effect on different forms and strains of T. cruzi, by affecting the bioenergetic metabolism of the parasite. These findings provide a starting point for future experimental assays and may contribute to the development of new compounds. Electronic supplementary material The online version of this article (10.1186/s40409-018-0165-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daiane Dias Ferreira
- Instituto Adolfo Lutz, Centre for Parasitology and Mycology, Avenida Dr. Arnaldo 351, 8° andar, sala 9, CEP, São Paulo, SP 01246-000 Brazil
| | - Juliana Tonini Mesquita
- Instituto Adolfo Lutz, Centre for Parasitology and Mycology, Avenida Dr. Arnaldo 351, 8° andar, sala 9, CEP, São Paulo, SP 01246-000 Brazil
| | - Thais Alves da Costa Silva
- Instituto Adolfo Lutz, Centre for Parasitology and Mycology, Avenida Dr. Arnaldo 351, 8° andar, sala 9, CEP, São Paulo, SP 01246-000 Brazil
| | - Maiara Maria Romanelli
- Instituto Adolfo Lutz, Centre for Parasitology and Mycology, Avenida Dr. Arnaldo 351, 8° andar, sala 9, CEP, São Paulo, SP 01246-000 Brazil
| | - Denise da Gama Jaen Batista
- 2Fundação Oswaldo Cruz, Laboratório de Biologia Celular do Instituto Oswaldo Cruz, Av. Brasil, 4365 Manguinhos, CEP, Rio de Janeiro, RJ 21040-360 Brazil
| | - Cristiane França da Silva
- 2Fundação Oswaldo Cruz, Laboratório de Biologia Celular do Instituto Oswaldo Cruz, Av. Brasil, 4365 Manguinhos, CEP, Rio de Janeiro, RJ 21040-360 Brazil
| | - Aline Nefertiti Silva da Gama
- 2Fundação Oswaldo Cruz, Laboratório de Biologia Celular do Instituto Oswaldo Cruz, Av. Brasil, 4365 Manguinhos, CEP, Rio de Janeiro, RJ 21040-360 Brazil
| | - Bruno Junior Neves
- 3Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240 Setor Leste Universitário, Goiânia, GO 74605170 Brazil
| | - Cleber Camilo Melo-Filho
- 3Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240 Setor Leste Universitário, Goiânia, GO 74605170 Brazil
| | - Maria de Nazare Correia Soeiro
- 2Fundação Oswaldo Cruz, Laboratório de Biologia Celular do Instituto Oswaldo Cruz, Av. Brasil, 4365 Manguinhos, CEP, Rio de Janeiro, RJ 21040-360 Brazil
| | - Carolina Horta Andrade
- 3Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240 Setor Leste Universitário, Goiânia, GO 74605170 Brazil
| | - Andre Gustavo Tempone
- Instituto Adolfo Lutz, Centre for Parasitology and Mycology, Avenida Dr. Arnaldo 351, 8° andar, sala 9, CEP, São Paulo, SP 01246-000 Brazil
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Hiller NDJ, Silva NAAE, Faria RX, Souza ALA, Resende JALC, Borges Farias A, Correia Romeiro N, de Luna Martins D. Synthesis and Evaluation of the Anticancer and Trypanocidal Activities of Boronic Tyrphostins. ChemMedChem 2018; 13:1395-1404. [PMID: 29856519 DOI: 10.1002/cmdc.201800206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 05/14/2018] [Indexed: 12/13/2022]
Abstract
Molecules containing an (cyanovinyl)arene moiety are known as tyrphostins because of their ability to inhibit proteins from the tyrosine kinase family, an interesting target for the development of anticancer and trypanocidal drugs. In the present work, (E)-(cyanovinyl)benzeneboronic acids were synthesized by Knoevenagel condensations without the use of any catalysts in water through a simple protocol that completely avoided the use of organic solvents in the synthesis and workup process. The in vitro anticancer and trypanocidal activities of the synthesized boronic acids were also evaluated, and it was discovered that the introduction of the boronic acid functionality improved the activity of the boronic tyrphostins. In silico target fishing with the use of a chemogenomic approach suggested that tyrosine-phosphorylation-regulated kinase 1a (DYRK1A) was a potential target for some of the designed compounds.
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Affiliation(s)
- Noemi de J Hiller
- Research Group on Catalysis and Synthesis (CSI), Universidade Federal Fluminense, Laboratório 413, Instituto de Química, Campus do Valonguinho, Centro, Niterói, RJ, 24020-141, Brazil
| | - Nayane A A E Silva
- Research Group on Catalysis and Synthesis (CSI), Universidade Federal Fluminense, Laboratório 413, Instituto de Química, Campus do Valonguinho, Centro, Niterói, RJ, 24020-141, Brazil
| | - Robson X Faria
- Laboratory of Toxoplasmosis and other Protozoan Diseases, Oswaldo Cruz Institute (Fiocruz), Brasil
| | - André Luís A Souza
- Laboratory of Biochemistry of Peptides, Oswaldo Cruz Institute (Fiocruz), Brazil
| | - Jackson A L C Resende
- Laboratory of Solid-State Chemistry, Universidade Federal do Mato Grosso, Instituto de Ciências Exatas e da Terra, Campus Universitário do Araguaia, Barra do Garças, MT, 78600-000, Brazil
| | - André Borges Farias
- Núcleo de Pesquisas em Ecologia e Desenvolvimento Social (NUPEM), Universidade Federal do Rio de Janeiro, Campus de Macaé, Av. Rotary Club s/n; São José do Barreto, Macaé, RJ, 27901-000, Brazil
| | - Nelilma Correia Romeiro
- Núcleo de Pesquisas em Ecologia e Desenvolvimento Social (NUPEM), Universidade Federal do Rio de Janeiro, Campus de Macaé, Av. Rotary Club s/n; São José do Barreto, Macaé, RJ, 27901-000, Brazil
| | - Daniela de Luna Martins
- Research Group on Catalysis and Synthesis (CSI), Universidade Federal Fluminense, Laboratório 413, Instituto de Química, Campus do Valonguinho, Centro, Niterói, RJ, 24020-141, Brazil
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Rogerio KR, Carvalho LJM, Domingues LHP, Neves BJ, Moreira Filho JT, Castro RN, Bianco Júnior C, Daniel-Ribeiro CT, Andrade CH, Graebin CS. Synthesis and molecular modelling studies of pyrimidinones and pyrrolo[3,4-d]-pyrimidinodiones as new antiplasmodial compounds. Mem Inst Oswaldo Cruz 2018; 113:e170452. [PMID: 29924131 PMCID: PMC6001580 DOI: 10.1590/0074-02760170452] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 05/10/2018] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Malaria is responsible for 429,000 deaths per year worldwide, and more than 200 million cases were reported in 2015. Increasing parasite resistance has imposed restrictions to the currently available antimalarial drugs. Thus, the search for new, effective and safe antimalarial drugs is crucial. Heterocyclic compounds, such as dihydropyrimidinones (DHPM), synthesised via the Biginelli multicomponent reaction, as well as bicyclic compounds synthesised from DHPMs, have emerged as potential antimalarial candidates in the last few years. METHODS Thirty compounds were synthesised employing the Biginelli multicomponent reaction and subsequent one-pot substitution/cyclisation protocol; the compounds were then evaluated in vitro against chloroquine-resistant Plasmodium falciparum parasites (W2 strain). Drug cytotoxicity in baseline kidney African Green Monkey cells (BGM) was also evaluated. The most active in vitro compounds were evaluated against P. berghei parasites in mice. Additionally, we performed an in silico target fishing approach with the most active compounds, aiming to shed some light into the mechanism at a molecular level. RESULTS The synthetic route chosen was effective, leading to products with high purity and yields ranging from 10-84%. Three out of the 30 compounds tested were identified as active against the parasite and presented low toxicity. The in silico study suggested that among all the molecular targets identified by our target fishing approach, Protein Kinase 3 (PK5) and Glycogen Synthase Kinase 3β (GSK-3β) are the most likely molecular targets for the synthesised compounds. CONCLUSIONS We were able to easily obtain a collection of heterocyclic compounds with in vitro anti-P. falciparum activity that can be used as scaffolds for the design and development of new antiplasmodial drugs.
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Affiliation(s)
- Kamilla Rodrigues Rogerio
- Laboratório de Diversidade Molecular e Química Medicinal, Departamento de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil
| | - Leonardo J M Carvalho
- Laboratório de Pesquisas em Malária, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brasil
| | - Luiza Helena Pinto Domingues
- Laboratório de Diversidade Molecular e Química Medicinal, Departamento de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil
| | - Bruno Junior Neves
- Laboratório de Planejamento de Fármacos e Modelagem Molecular, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brasil
| | - José Teófilo Moreira Filho
- Laboratório de Planejamento de Fármacos e Modelagem Molecular, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brasil
| | - Rosane Nora Castro
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil
| | - Cesare Bianco Júnior
- Laboratório de Pesquisas em Malária, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brasil
| | - Claudio Tadeu Daniel-Ribeiro
- Laboratório de Pesquisas em Malária, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brasil
| | - Carolina Horta Andrade
- Laboratório de Planejamento de Fármacos e Modelagem Molecular, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, GO, Brasil
| | - Cedric Stephan Graebin
- Laboratório de Diversidade Molecular e Química Medicinal, Departamento de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil
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Grisoni F, Ballabio D, Todeschini R, Consonni V. Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach. Methods Mol Biol 2018; 1800:3-53. [PMID: 29934886 DOI: 10.1007/978-1-4939-7899-1_1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.
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Affiliation(s)
- Francesca Grisoni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy.
| | - Davide Ballabio
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Roberto Todeschini
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Viviana Consonni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
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Grisoni F, Consonni V, Todeschini R. Impact of Molecular Descriptors on Computational Models. Methods Mol Biol 2018; 1825:171-209. [PMID: 30334206 DOI: 10.1007/978-1-4939-8639-2_5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.
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Affiliation(s)
- Francesca Grisoni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy.
| | - Viviana Consonni
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Roberto Todeschini
- Department of Earth and Environmental Sciences, Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
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Somody JC, MacKinnon SS, Windemuth A. Structural coverage of the proteome for pharmaceutical applications. Drug Discov Today 2017; 22:1792-1799. [DOI: 10.1016/j.drudis.2017.08.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 08/16/2017] [Accepted: 08/17/2017] [Indexed: 01/09/2023]
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47
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Venkanna A, Kwon OW, Afzal S, Jang C, Cho KH, Yadav DK, Kim K, Park HG, Chun KH, Kim SY, Kim MH. Pharmacological use of a novel scaffold, anomeric N,N-diarylamino tetrahydropyran: molecular similarity search, chemocentric target profiling, and experimental evidence. Sci Rep 2017; 7:12535. [PMID: 28970544 PMCID: PMC5624941 DOI: 10.1038/s41598-017-12082-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 08/29/2017] [Indexed: 11/30/2022] Open
Abstract
Rational drug design against a determined target (disease, pathway, or protein) is the main strategy in drug discovery. However, regardless of the main strategy, chemists really wonder how to maximize the utility of their new compounds by drug repositioning them as clinical drug candidates in drug discovery. In this study, we started our drug discovery "from curiosity in the chemical structure of a drug scaffold itself" rather than "for a specific target". As a new drug scaffold, anomeric diarylamino cyclic aminal scaffold 1, was designed by combining two known drug scaffolds (diphenylamine and the most popular cyclic ether, tetrahydropyran/tetrahydrofuran) and synthesized through conventional Brønsted acid catalysis and metal-free α-C(sp3)-H functionalized oxidative cyclization. To identify the utility of the new scaffold 1, it was investigated through 2D and 3D similarity screening and chemocentric target prediction. The predicted proteins were investigated by an experimental assay. The scaffold 1 was reported to have an antineuroinflammatory agent to reduce NO production, and compound 10 concentration-dependently regulated the expression level of IL-6, PGE-2, TNF-α, ER-β, VDR, CTSD, and iNOS, thus exhibiting neuroprotective activity.
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Affiliation(s)
- Arramshetti Venkanna
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Oh Wook Kwon
- Natural F&P Corp. 152 Saemal-ro, Songpa-gu, Seoul, Korea
| | - Sualiha Afzal
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Cheongyun Jang
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Kyo Hee Cho
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Dharmendra K Yadav
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Kang Kim
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Hyeung-Geun Park
- Research Institute of Pharmaceutical Science and College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Kwang-Hoon Chun
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Sun Yeou Kim
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.
| | - Mi-Hyun Kim
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.
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48
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Gomes MN, Muratov EN, Pereira M, Peixoto JC, Rosseto LP, Cravo PVL, Andrade CH, Neves BJ. Chalcone Derivatives: Promising Starting Points for Drug Design. Molecules 2017; 22:E1210. [PMID: 28757583 PMCID: PMC6152227 DOI: 10.3390/molecules22081210] [Citation(s) in RCA: 199] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 07/11/2017] [Accepted: 07/14/2017] [Indexed: 12/20/2022] Open
Abstract
Medicinal chemists continue to be fascinated by chalcone derivatives because of their simple chemistry, ease of hydrogen atom manipulation, straightforward synthesis, and a variety of promising biological activities. However, chalcones have still not garnered deserved attention, especially considering their high potential as chemical sources for designing and developing new effective drugs. In this review, we summarize current methodological developments towards the design and synthesis of new chalcone derivatives and state-of-the-art medicinal chemistry strategies (bioisosterism, molecular hybridization, and pro-drug design). We also highlight the applicability of computer-assisted drug design approaches to chalcones and address how this may contribute to optimizing research outputs and lead to more successful and cost-effective drug discovery endeavors. Lastly, we present successful examples of the use of chalcones and suggest possible solutions to existing limitations.
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Affiliation(s)
- Marcelo N Gomes
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27955-7568, USA.
| | - Maristela Pereira
- Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia 74001-970, Brazil.
| | - Josana C Peixoto
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
| | - Lucimar P Rosseto
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
| | - Pedro V L Cravo
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
- GHTM/Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, 1349-008 Lisboa, Portugal.
| | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
| | - Bruno J Neves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
- Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia 74001-970, Brazil.
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
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49
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Cerisier N, Regad L, Triki D, Petitjean M, Flatters D, Camproux AC. Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain. Mol Inform 2017; 36. [PMID: 28696518 DOI: 10.1002/minf.201700040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/26/2017] [Indexed: 12/21/2022]
Abstract
While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.
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Affiliation(s)
- Natacha Cerisier
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Leslie Regad
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Dhoha Triki
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Michel Petitjean
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Delphine Flatters
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
| | - Anne-Claude Camproux
- INSERM, UMRS-973, MTi,35, rue Hélène Brion, 75205, PARIS CEDEX 13.,University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi
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50
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Identification of Histamine H 3 Receptor Ligands Using a New Crystal Structure Fragment-based Method. Sci Rep 2017; 7:4829. [PMID: 28684785 PMCID: PMC5500575 DOI: 10.1038/s41598-017-05058-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/23/2017] [Indexed: 01/14/2023] Open
Abstract
Virtual screening offers an efficient alternative to high-throughput screening in the identification of pharmacological tools and lead compounds. Virtual screening is typically based on the matching of target structures or ligand pharmacophores to commercial or in-house compound catalogues. This study provides the first proof-of-concept for our recently reported method where pharmacophores are instead constructed based on the inference of residue-ligand fragments from crystal structures. We demonstrate its unique utility for G protein-coupled receptors, which represent the largest families of human membrane proteins and drug targets. We identified five neutral antagonists and one inverse agonist for the histamine H3 receptor with potencies of 0.7-8.5 μM in a recombinant receptor cell-based inositol phosphate accumulation assay and validated their activity using a radioligand competition binding assay. H3 receptor antagonism is of large therapeutic value and our ligands could serve as starting points for further lead optimisation. The six ligands exhibit four chemical scaffolds, whereof three have high novelty in comparison to the known H3 receptor ligands in the ChEMBL database. The complete pharmacophore fragment library is freely available through the GPCR database, GPCRdb, allowing the successful application herein to be repeated for most of the 285 class A GPCR targets. The method could also easily be adapted to other protein families.
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