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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

Author(s): Alejandro Speck-Planche, Valeria V. Kleandrova, Feng Luan and M. Natalia D.S. Cordeiro

Volume 15, Issue 8, 2012

Page: [666 - 673] Pages: 8

DOI: 10.2174/138620712802650487

Price: $65


Mycobacterium tuberculosis (MTB) is the principal pathogen which causes tuberculosis (TB), a disease that remains as one of the most alarming health problems worldwide. An active area for the search of new anti-TB therapies is concerned with the use of computational approaches based on Chemoinformatics and/or Bioinformatics toward the discovery of new and potent anti-TB agents. These approaches consider only small series of structurally related compounds and the studies are generally realized for only one target like a protein. This fact constitutes an important limitation. The present work is an effort to overcome this problem. We introduce here the first chemo-bioinformatic approach by developing a multi-target (mt) QSAR discriminant model, for the in silico design and virtual screening of anti-TB agents against six proteins in MTB. The mt-QSAR model was developed by employing a large and heterogeneous database of compounds and substructural descriptors. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. Some fragments were extracted from the molecules and their contributions to anti-TB activity through inhibition of the six proteins, were calculated. Several fragments were identified as responsible for anti-TB activity and new molecular entities were designed from those fragments with positive contributions, being suggested as possible anti-TB agents.

Keywords: Anti-TB activity, bioinformatics, chemoinformatics, fragment contributions, linear discriminant analysis, mt- QSAR, inhibitors, protein sequence, tuberculosis, anti-TB drugs

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