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      Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects

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          Abstract

          Background

          Promiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects.

          Methods

          We perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects.

          Results

          We observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases.

          Conclusions

          The detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40360-017-0128-7) contains supplementary material, which is available to authorized users.

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          Most cited references39

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          PISCES: a protein sequence culling server.

          PISCES is a public server for culling sets of protein sequences from the Protein Data Bank (PDB) by sequence identity and structural quality criteria. PISCES can provide lists culled from the entire PDB or from lists of PDB entries or chains provided by the user. The sequence identities are obtained from PSI-BLAST alignments with position-specific substitution matrices derived from the non-redundant protein sequence database. PISCES therefore provides better lists than servers that use BLAST, which is unable to identify many relationships below 40% sequence identity and often overestimates sequence identity by aligning only well-conserved fragments. PDB sequences are updated weekly. PISCES can also cull non-PDB sequences provided by the user as a list of GenBank identifiers, a FASTA format file, or BLAST/PSI-BLAST output.
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            Gene Ontology Annotations and Resources

            The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new ‘phylogenetic annotation’ process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.
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              Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets

              Summary Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.
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                Author and article information

                Contributors
                matthieu.chartier@usherbrooke.ca
                louis-philippe.morency@usherbrooke.ca
                maria.ines.zylber@usherbrooke.ca
                rafael.najmanovich@umontreal.ca
                Journal
                BMC Pharmacol Toxicol
                BMC Pharmacol Toxicol
                BMC Pharmacology & Toxicology
                BioMed Central (London )
                2050-6511
                28 April 2017
                28 April 2017
                2017
                : 18
                : 18
                Affiliations
                [1 ]ISNI 0000 0000 9064 6198, GRID grid.86715.3d, Department of Biochemistry, Faculty of Medicine and Health Sciences, , Université de Sherbrooke, ; Québec, Canada
                [2 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, Department of Pharmacology and Physiology, Faculty of Medicine, , Université de Montréal, ; Québec, Canada
                Article
                128
                10.1186/s40360-017-0128-7
                5408384
                28449705
                d783c112-25a3-403c-b291-c9ddd827557e
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 6 September 2016
                : 28 February 2017
                Funding
                Funded by: Natural Sciences and Engineering Research Council of Canada (CA)
                Award ID: RGPIN-2014-05766
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Toxicology
                molecularinteraction field similarities,binding-site similarities,drug repurposing,side-effects,cross-reactivity,promiscuity,large-scale analysis

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