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      Drug repurposing to target Ebola virus replication and virulence using structural systems pharmacology

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          Abstract

          Background

          The recent outbreak of Ebola has been cited as the largest in history. Despite this global health crisis, few drugs are available to efficiently treat Ebola infections. Drug repurposing provides a potentially efficient solution to accelerating the development of therapeutic approaches in response to Ebola outbreak. To identify such candidates, we use an integrated structural systems pharmacology pipeline which combines proteome-scale ligand binding site comparison, protein-ligand docking, and Molecular Dynamics (MD) simulation.

          Results

          One thousand seven hundred and sixty-six FDA-approved drugs and 259 experimental drugs were screened to identify those with the potential to inhibit the replication and virulence of Ebola, and to determine the binding modes with their respective targets. Initial screening has identified a number of promising hits. Notably, Indinavir; an HIV protease inhibitor, may be effective in reducing the virulence of Ebola. Additionally, an antifungal (Sinefungin) and several anti-viral drugs (e.g. Maraviroc, Abacavir, Telbivudine, and Cidofovir) may inhibit Ebola RNA-directed RNA polymerase through targeting the MTase domain.

          Conclusions

          Identification of safe drug candidates is a crucial first step toward the determination of timely and effective therapeutic approaches to address and mitigate the impact of the Ebola global crisis and future outbreaks of pathogenic diseases. Further in vitro and in vivo testing to evaluate the anti-Ebola activity of these drugs is warranted.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-016-0941-9) contains supplementary material, which is available to authorized users.

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          Most cited references57

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          Protein homology detection by HMM-HMM comparison.

          Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolution. We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all comparison of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%.Sensitivity: When the predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile-profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1.7 and 3.3 times more good alignments ('balanced' score >0.3) than the next best method (COMPASS), and 1.6, 2.9 and 9.4 times more than PSI-BLAST, at the family, superfamily and fold level, respectively.Speed: HHsearch scans a query of 200 residues against 3691 domains in 33 s on an AMD64 2GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than COMPASS.
            • Record: found
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            A critical assessment of docking programs and scoring functions.

            Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.
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              A method to identify protein sequences that fold into a known three-dimensional structure.

              The inverse protein folding problem, the problem of finding which amino acid sequences fold into a known three-dimensional (3D) structure, can be effectively attacked by finding sequences that are most compatible with the environments of the residues in the 3D structure. The environments are described by: (i) the area of the residue buried in the protein and inaccessible to solvent; (ii) the fraction of side-chain area that is covered by polar atoms (O and N); and (iii) the local secondary structure. Examples of this 3D profile method are presented for four families of proteins: the globins, cyclic AMP (adenosine 3',5'-monophosphate) receptor-like proteins, the periplasmic binding proteins, and the actins. This method is able to detect the structural similarity of the actins and 70- kilodalton heat shock proteins, even though these protein families share no detectable sequence similarity.

                Author and article information

                Contributors
                lxie@iscb.org
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                18 February 2016
                18 February 2016
                2016
                : 17
                : 90
                Affiliations
                [ ]High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, P. R. China
                [ ]National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
                [ ]The Graduate Center, The City University of New York, New York, USA
                [ ]Department of Chemistry, Hunter College, The City University of New York, New York, USA
                [ ]Office of the Director, National Institutes of Health, Bethesda, MD USA
                [ ]Department of Computer Science, Hunter College, The City University of New York, New York, USA
                Article
                941
                10.1186/s12859-016-0941-9
                4757998
                26887654
                8ab2844e-f743-4106-9609-d3cf07bf9cde
                © Zhao et al. 2016

                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
                : 22 February 2015
                : 10 February 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health (US);
                Award ID: R01-LM011986
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Bioinformatics & Computational biology
                drug repositioning,infectious disease,indinavir,sinefungin,binding site similarity,rna-directed rna polymerase,vp24

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