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      Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

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          Abstract

          De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

          Author Summary

          The emergence of multi-resistant bacterial strains and the existing void in the discovery and development of new classes of antibiotics is a growing concern. Indeed, some bacterial strains are now resistant to last-line antibiotics and considered untreatable. Drug repositioning has been suggested as a strategy to minimize time and cost expenses until the drug reaches the market, compared to traditional drug design. Drug-target interactions (DTIs) are the basis of rational drug design and thus, we proposed a computational approach to predict DTIs solely based on the primary sequence of the protein and the simplified molecular-input line-entry system of the ligand. In addition, network metrics are used to identify vital putative drug-targets in bacteria. Molecular docking experiments were performed to compare the binding affinities between a given ligand and a putative drug-target, as well as with their original targets. According to the docking results, the predicted DTIs have better or similar binding activities than the ligand and their real target, indicating the validity of the proposed model.

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            AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

            We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. (c) 2009 Wiley Periodicals, Inc.
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              The I-TASSER Suite: protein structure and function prediction.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                28 November 2016
                November 2016
                : 12
                : 11
                : e1005219
                Affiliations
                [1 ]Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal
                [2 ]Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, Portugal
                University of Houston, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: EDC JPA JLO.

                • Data curation: EDC.

                • Formal analysis: EDC JPA JLO.

                • Funding acquisition: EDC JPA JLO.

                • Investigation: EDC JPA JLO.

                • Methodology: EDC JPA JLO.

                • Project administration: EDC JPA JLO.

                • Resources: JLO.

                • Software: EDC.

                • Supervision: EDC JPA JLO.

                • Validation: EDC JPA JLO.

                • Visualization: EDC JPA JLO.

                • Writing – original draft: EDC JPA JLO.

                • Writing – review & editing: EDC JPA JLO.

                Author information
                http://orcid.org/0000-0003-4937-2334
                Article
                PCOMPBIOL-D-16-01212
                10.1371/journal.pcbi.1005219
                5125559
                27893735
                0ef19733-6701-40c4-b027-82a06f7faa0a
                © 2016 Coelho et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 July 2016
                : 21 October 2016
                Page count
                Figures: 3, Tables: 4, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: SFRH/BD/86343/2012
                Award Recipient :
                Fundação para a Ciência e Tecnologia ( http://www.fct.pt/) funded EDC under grant SFRH/BD/86343/2012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Pharmacology
                Drug Interactions
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobial Resistance
                Medicine and Health Sciences
                Pharmacology
                Antimicrobial Resistance
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobial Resistance
                Antibiotic Resistance
                Medicine and Health Sciences
                Pharmacology
                Antimicrobial Resistance
                Antibiotic Resistance
                Computer and Information Sciences
                Network Analysis
                Centrality
                Medicine and Health Sciences
                Pharmacology
                Drugs
                Antimicrobials
                Antibacterials
                Biology and Life Sciences
                Microbiology
                Microbial Control
                Antimicrobials
                Antibacterials
                Medicine and Health Sciences
                Pharmacology
                Drug Research and Development
                Drug Discovery
                Biology and life sciences
                Organisms
                Bacteria
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Biology and life sciences
                Microbiology
                Medical microbiology
                Microbial pathogens
                Bacterial pathogens
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Medicine and health sciences
                Pathology and laboratory medicine
                Pathogens
                Microbial pathogens
                Bacterial pathogens
                Staphylococcus
                Staphylococcus aureus
                Methicillin-resistant Staphylococcus aureus
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Custom metadata
                The proposed classification model and related data files are available at http://bioinformatics.ua.pt/software/dtipred/.

                Quantitative & Systems biology
                Quantitative & Systems biology

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