[Show abstract][Hide abstract] ABSTRACT: Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets.
The Scientific World Journal 11/2014; 2014:957107. DOI:10.1155/2014/957107 · 1.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We describe the genome sequencing and analysis of a clinical isolate of the multidrug-resistant Mycobacterium tuberculosis Uganda I genotype (OSDD515) from India
[Show abstract][Hide abstract] ABSTRACT: We describe the genome sequencing and analysis of a multidrug-resistant (MDR) clinical isolate of Mycobacterium tuberculosis, strain OSDD105 from India, belonging to a novel spoligotype.
[Show abstract][Hide abstract] ABSTRACT: Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollars and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.
Current Topics in Medicinal Chemistry 05/2013; In Press(10). DOI:10.2174/1568026611313100005 · 3.40 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Multidrug resistance capacity of Mycobacterium tuberculosis demands urgent need for developing new antitubercular drugs. The present work is on M. tuberculosis-MbtA, an enzyme involved in the biosynthesis of siderophores, having a critical role in bacterial growth and virulence. The molecular models of both holo and apo forms of M. tuberculosis-MbtA have been constructed and validated. A docking study with a series of 42 5'-O-[N-(salicyl) sulfamoyl] adenosine derivatives, using GOLD software, revealed significant correlation (R (2) = 0.8611) between Goldscore and the reported binding affinity data. Further, binding energies of the docked poses were calculated and compared with the observed binding affinities (R (2) = 0.901). All-atom molecular dynamics simulation was performed for apo form, holo form without ligand and holo form with ligands. The holo form without ligand on molecular dynamics simulation for 20 ns converged to the apo form and the apo form upon induced fit docking of the natural substrate, 2,3-dihydroxybenzoic acid-adenylate, yielded the holo structure. The molecular dynamics simulation of the holo form with ligands across the time period of 20 ns provided with the insights into ligand-receptor interactions for inhibition of the enzyme. A thorough study involving interaction energy calculation between the ligands and the active site residues of MbtA model identified the key residues implicated in ligand binding. The holo model was capable to differentiate active compounds from decoys. In the absence of experimental structure of MbtA, the homology models together with the insights gained from this study will promote the rational design of potent and selective MbtA inhibitors as antitubercular therapeutics. An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:33.
[Show abstract][Hide abstract] ABSTRACT: The group of antigen 85 proteins of Mycobacterium tuberculosis is responsible for converting trehalose monomycolate to trehalose dimycolate, which contributes to cell wall stability. Here, we have used a serial enrichment approach to identify new potential inhibitors by searching the libraries of compounds using both 2D atom pair descriptors and binary fingerprints followed by molecular docking. Three different docking softwares AutoDock, GOLD, and LigandFit were used for docking calculations. In addition, we applied the criteria of selecting compounds with binding efficiency close to the starting known inhibitor and showing potential to form hydrogen bonds with the active site amino acid residues. The starting inhibitor was ethyl-3-phenoxybenzyl-butylphosphonate, which had IC(50) value of 2.0 μM in mycolyltransferase inhibition assay. Our search from more than 34 million compounds from public libraries yielded 49 compounds. Subsequently, selection was restricted to compounds conforming to the Lipinski rule of five and exhibiting hydrogen bonding to any of the amino acid residues in the active site pocket of all three proteins of antigen 85A, 85B, and 85C. Finally, we selected those ligands which were ranked top in the table with other known decoys in all the docking results. The compound NIH415032 from tuberculosis antimicrobial acquisition and coordinating facility was further examined using molecular dynamics simulations for 10 ns. These results showed that the binding is stable, although some of the hydrogen bond atom pairs varied through the course of simulation. The NIH415032 has antitubercular properties with IC(90) at 20 μg/ml (53.023 μM). These results will be helpful to the medicinal chemists for developing new antitubercular molecules for testing.