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Vigil-Vásquez C, Schüller A. De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference. Int J Mol Sci 2022; 23:ijms23179666. [PMID: 36077062 PMCID: PMC9455815 DOI: 10.3390/ijms23179666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 12/01/2022] Open
Abstract
Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.
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Affiliation(s)
- Carlos Vigil-Vásquez
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Andreas Schüller
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Correspondence:
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2
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Computational Methods for Drug Repurposing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:119-141. [PMID: 35230686 DOI: 10.1007/978-3-030-91836-1_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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3
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Wan F, Zhu Y, Hu H, Dai A, Cai X, Chen L, Gong H, Xia T, Yang D, Wang MW, Zeng J. DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 17:478-495. [PMID: 32035227 PMCID: PMC7056933 DOI: 10.1016/j.gpb.2019.04.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 04/29/2019] [Indexed: 12/13/2022]
Abstract
Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.
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Affiliation(s)
- Fangping Wan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Yue Zhu
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Hailin Hu
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Antao Dai
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Xiaoqing Cai
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Ligong Chen
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Haipeng Gong
- School of Life Science, Tsinghua University, Beijing 100084, China
| | - Tian Xia
- Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Dehua Yang
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
| | - Ming-Wei Wang
- The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China.
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4
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Ding Y, Wang H, Zheng H, Wang L, Zhang G, Yang J, Lu X, Bai Y, Zhang H, Li J, Gao W, Chen F, Hu S, Wu J, Xu L. Evaluation of drug efficacy based on the spatial position comparison of drug–target interaction centers. Brief Bioinform 2019; 21:762-776. [DOI: 10.1093/bib/bbz024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/28/2019] [Accepted: 02/09/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug’s pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs’ efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug–target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug–amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug–target pairs. For the 1199 drug–target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug–target interaction center spatial position method better reflect the drug–target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.
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Affiliation(s)
- Yu Ding
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Hong Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Hewei Zheng
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Lianzong Wang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Guosi Zhang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jiaxin Yang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Xiaoyan Lu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Yu Bai
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Haotian Zhang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jing Li
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Wenyan Gao
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Fukun Chen
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Shui Hu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jingqi Wu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Liangde Xu
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin
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5
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Pabon NA, Xia Y, Estabrooks SK, Ye Z, Herbrand AK, Süß E, Biondi RM, Assimon VA, Gestwicki JE, Brodsky JL, Camacho CJ, Bar-Joseph Z. Predicting protein targets for drug-like compounds using transcriptomics. PLoS Comput Biol 2018; 14:e1006651. [PMID: 30532261 PMCID: PMC6300300 DOI: 10.1371/journal.pcbi.1006651] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 12/19/2018] [Accepted: 11/13/2018] [Indexed: 01/07/2023] Open
Abstract
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions. Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).
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Affiliation(s)
- Nicolas A. Pabon
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yan Xia
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Samuel K. Estabrooks
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Zhaofeng Ye
- School of Medicine, Tsinghua University, Beijing, China
| | - Amanda K. Herbrand
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Evelyn Süß
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Ricardo M. Biondi
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Victoria A. Assimon
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jason E. Gestwicki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jeffrey L. Brodsky
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Carlos J. Camacho
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
| | - Ziv Bar-Joseph
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
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6
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Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates. Structure 2018; 26:565-571.e3. [PMID: 29551288 PMCID: PMC5890617 DOI: 10.1016/j.str.2018.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 01/26/2018] [Accepted: 02/09/2018] [Indexed: 11/22/2022]
Abstract
There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures. We present PARITY, matching atoms in identical topology to gauge ligand similarity Bound-cognate ligand similarity is a useful metric for ranking PDB structures Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7 We provide rank-ordered lists of PDBs with the most biologically relevant ligands
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7
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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8
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Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds. Future Sci OA 2017; 3:FSO212. [PMID: 28884009 PMCID: PMC5583696 DOI: 10.4155/fsoa-2017-0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 04/26/2017] [Indexed: 12/31/2022] Open
Abstract
Aim: Computational exploration of small-molecule-based relationships between target proteins from different families. Materials & methods: Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data. Results: A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets. Conclusion: Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available. Relationships between proteins are usually studied by comparing their sequences and functions. However, in addition to biological relationships, chemical links between proteins can also be established by searching for active compounds they share. If proteins have active compounds in common, they are likely to interact with small molecules in similar ways, which provides important clues for drug discovery. Therefore, we have systematically searched for unexpected compound-based relationships between proteins. Shown here are exemplary small molecules that are active against two targets with different functions. Thus, these compounds establish an unexpected chemical/ligand-binding relationship between these targets.
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Gao L, Duan DD, Zhang JQ, Zhou YZ, Qin XM, Du GH. A Bioinformatic Approach for the Discovery of Antiaging Effects of Baicalein from Scutellaria baicalensis Georgi. Rejuvenation Res 2016; 19:414-422. [DOI: 10.1089/rej.2015.1760] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
| | - Dan-dan Duan
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
- College of Chemistry and Chemical Engineering, Shanxi University, Taiyuan, P.R. China
| | - Jian-qin Zhang
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
| | - Yu-zhi Zhou
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
| | - Xue-mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
| | - Guan-hua Du
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P.R. China
- Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, P.R. China
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10
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Martínez-Jiménez F, Marti-Renom MA. Should network biology be used for drug discovery? Expert Opin Drug Discov 2016; 11:1135-1137. [PMID: 27635856 DOI: 10.1080/17460441.2016.1236786] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Francisco Martínez-Jiménez
- a CNAG-CRG, Centre for Genomic Regulation (CRG) , Barcelona Institute of Science and Technology (BIST) , Barcelona , Spain.,b Gene Regulation, Stem Cells and Cancer Program , Centre for Genomic Regulation (CRG) , Barcelona , Spain.,c Universitat Pompeu Fabra (UPF) , Barcelona , Spain
| | - Marc A Marti-Renom
- a CNAG-CRG, Centre for Genomic Regulation (CRG) , Barcelona Institute of Science and Technology (BIST) , Barcelona , Spain.,b Gene Regulation, Stem Cells and Cancer Program , Centre for Genomic Regulation (CRG) , Barcelona , Spain.,c Universitat Pompeu Fabra (UPF) , Barcelona , Spain.,d ICREA, Pg. Lluís Companys , Barcelona , Spain
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11
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Berenstein AJ, Magariños MP, Chernomoretz A, Agüero F. A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases. PLoS Negl Trop Dis 2016; 10:e0004300. [PMID: 26735851 PMCID: PMC4703370 DOI: 10.1371/journal.pntd.0004300] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2015] [Accepted: 11/21/2015] [Indexed: 12/16/2022] Open
Abstract
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.
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Affiliation(s)
- Ariel José Berenstein
- Laboratorio de Bioinformática, Fundación Instituto Leloir, Buenos Aires, Argentina
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - María Paula Magariños
- Laboratorio de Genómica y Bioinformática, Instituto de Investigaciones Biotecnológicas–Instituto Tecnológico de Chascomús, Universidad de San Martín–CONICET, Sede San Martín, San Martín, Buenos Aires, Argentina
| | - Ariel Chernomoretz
- Laboratorio de Bioinformática, Fundación Instituto Leloir, Buenos Aires, Argentina
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Fernán Agüero
- Laboratorio de Genómica y Bioinformática, Instituto de Investigaciones Biotecnológicas–Instituto Tecnológico de Chascomús, Universidad de San Martín–CONICET, Sede San Martín, San Martín, Buenos Aires, Argentina
- * E-mail: ,
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12
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Peng L, Liao B, Zhu W, Li Z, Li K. Predicting Drug-Target Interactions With Multi-Information Fusion. IEEE J Biomed Health Inform 2015; 21:561-572. [PMID: 26731781 DOI: 10.1109/jbhi.2015.2513200] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.
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13
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Rebollo-Lopez MJ, Lelièvre J, Alvarez-Gomez D, Castro-Pichel J, Martínez-Jiménez F, Papadatos G, Kumar V, Colmenarejo G, Mugumbate G, Hurle M, Barroso V, Young RJ, Martinez-Hoyos M, González del Río R, Bates RH, Lopez-Roman EM, Mendoza-Losana A, Brown JR, Alvarez-Ruiz E, Marti-Renom MA, Overington JP, Cammack N, Ballell L, Barros-Aguire D. Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery. PLoS One 2015; 10:e0142293. [PMID: 26642067 PMCID: PMC4671658 DOI: 10.1371/journal.pone.0142293] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/19/2015] [Indexed: 12/12/2022] Open
Abstract
As a follow up to the antimycobacterial screening exercise and the release of GSK´s first Tres Cantos Antimycobacterial Set (TCAMS-TB), this paper presents the results of a second antitubercular screening effort of two hundred and fifty thousand compounds recently added to the GSK collection. The compounds were further prioritized based on not only antitubercular potency but also on physicochemical characteristics. The 50 most attractive compounds were then progressed for evaluation in three different predictive computational biology algorithms based on structural similarity or GSK historical biological assay data in order to determine their possible mechanisms of action. This effort has resulted in the identification of novel compounds and their hypothesized targets that will hopefully fuel future TB drug discovery and target validation programs alike.
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Affiliation(s)
| | - Joël Lelièvre
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
- * E-mail: (JL); (MAMR)
| | | | - Julia Castro-Pichel
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - Francisco Martínez-Jiménez
- Genome Biology Group, Centre Nacional d’Anàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
| | - George Papadatos
- European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, United Kingdom
| | - Vinod Kumar
- Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, Pennsylvania, United States of America
| | - Gonzalo Colmenarejo
- Centro de Investigación Básica, CSci Computational Chemistry, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - Grace Mugumbate
- European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, United Kingdom
| | - Mark Hurle
- Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, Pennsylvania, United States of America
| | - Vanessa Barroso
- Centro de Investigación Básica, Platform Technology & Science, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - Rob J. Young
- CSC Medicinal Chemistry, Medicines Research Centre, GlaxoSmithKline, Stevenage, Hertfordshire, United Kingdom
| | | | | | - Robert H. Bates
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | | | | | - James R. Brown
- Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, Pennsylvania, United States of America
| | - Emilio Alvarez-Ruiz
- Centro de Investigación Básica, Platform Technology & Science, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - Marc A. Marti-Renom
- Genome Biology Group, Centre Nacional d’Anàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- * E-mail: (JL); (MAMR)
| | - John P. Overington
- European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, United Kingdom
| | - Nicholas Cammack
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - Lluís Ballell
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
| | - David Barros-Aguire
- Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid, Spain
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