Literature DB >> 23889525

Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach.

Ivani Pauli1, Ricardo N dos Santos, Diana C Rostirolla, Leonardo K Martinelli, Rodrigo G Ducati, Luís F S M Timmers, Luiz A Basso, Diógenes S Santos, Rafael V C Guido, Adriano D Andricopulo, Osmar Norberto de Souza.   

Abstract

Mycobacterium tuberculosis InhA (MtInhA) is an attractive enzyme to drug discovery efforts due to its validation as an effective biological target for tuberculosis therapy. In this work, two different virtual-ligand-screening approaches were applied in order to identify new InhA inhibitors' candidates from a library of ligands selected from the ZINC database. First, a 3-D pharmacophore model was built based on 36 available MtInhA crystal structures. By combining structure-based and ligand-based information, four pharmacophoric points were designed to select molecules able to satisfy the binding features of MtInhA substrate-binding cavity. The second approach consisted of using four well established docking programs, with different search algorithms, to compare the binding mode and score of the selected molecules from the aforementioned library. After detailed analyses of the results, six ligands were selected for in vitro analysis. Three of these molecules presented a satisfactory inhibitory activity with IC50 values ranging from 24 (±2) μM to 83 (±5) μM. The best compound presented an uncompetitive inhibition mode to NADH and 2-trans-dodecenoyl-CoA substrates, with Ki values of 24 (±3) μM and 20 (±2) μM, respectively. These molecules were not yet described as antituberculars or as InhA inhibitors, making its novelty interesting to start efforts on ligand optimization in order to identify new effective drugs against tuberculosis having InhA as a target. More studies are underway to dissect the discovered uncompetitive inhibitor interactions with MtInhA.

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Year:  2013        PMID: 23889525     DOI: 10.1021/ci400202t

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  23 in total

1.  Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods.

Authors:  Sean Ekins; Nadia K Litterman; Christopher A Lipinski; Barry A Bunin
Journal:  Pharm Res       Date:  2015-08-27       Impact factor: 4.200

2.  A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA.

Authors:  Alexander L Perryman; Weixuan Yu; Xin Wang; Sean Ekins; Stefano Forli; Shao-Gang Li; Joel S Freundlich; Peter J Tonge; Arthur J Olson
Journal:  J Chem Inf Model       Date:  2015-02-17       Impact factor: 4.956

3.  LBVS: an online platform for ligand-based virtual screening using publicly accessible databases.

Authors:  Minghao Zheng; Zhihong Liu; Xin Yan; Qianzhi Ding; Qiong Gu; Jun Xu
Journal:  Mol Divers       Date:  2014-09-03       Impact factor: 2.943

Review 4.  The future for early-stage tuberculosis drug discovery.

Authors:  Edison S Zuniga; Julie Early; Tanya Parish
Journal:  Future Microbiol       Date:  2015       Impact factor: 3.165

5.  Rethinking the MtInhA tertiary and quaternary structure flexibility: a molecular dynamics view.

Authors:  Lucas Santos Chitolina; Osmar Norberto de Souza; Luiz Augusto Basso; Luís Fernando Saraiva Macedo Timmers
Journal:  J Mol Model       Date:  2022-05-10       Impact factor: 1.810

6.  Elucidating the structural basis of diphenyl ether derivatives as highly potent enoyl-ACP reductase inhibitors through molecular dynamics simulations and 3D-QSAR study.

Authors:  Pharit Kamsri; Auradee Punkvang; Patchareenart Saparpakorn; Supa Hannongbua; Stephan Irle; Pornpan Pungpo
Journal:  J Mol Model       Date:  2014-06-17       Impact factor: 1.810

7.  Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Authors:  Sean Ekins; Peter B Madrid; Malabika Sarker; Shao-Gang Li; Nisha Mittal; Pradeep Kumar; Xin Wang; Thomas P Stratton; Matthew Zimmerman; Carolyn Talcott; Pauline Bourbon; Mike Travers; Maneesh Yadav; Joel S Freundlich
Journal:  PLoS One       Date:  2015-10-30       Impact factor: 3.240

8.  Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands.

Authors:  Yunierkis Perez-Castillo; Aliuska Morales Helguera; M Natalia D S Cordeiro; Eduardo Tejera; Cesar Paz-Y-Mino; Aminael Sanchez-Rodriguez; Fernanda Borges; Maykel Cruz-Monteagudo
Journal:  Curr Neuropharmacol       Date:  2017-11-14       Impact factor: 7.363

9.  An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features.

Authors:  Renata De Paris; Christian V Quevedo; Duncan D A Ruiz; Osmar Norberto de Souza
Journal:  PLoS One       Date:  2015-07-28       Impact factor: 3.240

10.  New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.

Authors:  Alex M Clark; Malabika Sarker; Sean Ekins
Journal:  J Cheminform       Date:  2014-08-04       Impact factor: 5.514

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