Cheminformatics Models for Inhibitors of Schistosoma mansoni

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

Driving a decade of change: HIV/AIDS, patents and access to medicines for all

Since 2000, access to antiretroviral drugs to treat HIV infection has dramatically increased to reach more than five million people in developing countries. Essential to this achievement was the dramatic reduction in antiretroviral prices, a result of global political mobilization that cleared the way for competitive production of generic versions of widely patented medicines

TBrowse: an integrative genomics map of Mycobacterium tuberculosis

Tuberculosis is one of the major infectious diseases causing morbidity and mortality in the developing world. Genome-wide experiments on Mycobacterium tuberculosis particularly H37Rv and many other strains has revealed a wealth of information on the pathogen. This has been complemented by computational methods for the analysis of genomic sequence. This genome-level information is scattered in individual databases and supplementary material of publications and is not easily amenable to integrative analysis and visualization. TBrowse is an attempt to create a starting resource for integrative analysis of the M. tuberculosis genome. This comprehensive database contains more than half a million data-points of genomic data systematically culled from online resources and publications and is organized into hundred tracks. The resource is built based on the Generic Model Organism Database Genome Browser, thus making it readily interoperable with other genome browser installations. TBrowse is enabled with tools for programmatic data access and interoperability with other similar resources through Distributed Annotation System. In addition the resource is interfaced with sequence analysis servers maintained by the National Center for Biotechnology Information and the University of California Santa Cruz

Probing the structure of <i>Mycobacterium tuberculosis</i> MbtA: model validation using molecular dynamics simulations and docking studies

<div><p>Multidrug resistance capacity of <i>Mycobacterium tuberculosis</i> demands urgent need for developing new antitubercular drugs. The present work is on <i>M. tuberculosis-</i>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 <i>M. tuberculosis-</i>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 (<i>R</i>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 (<i>R</i>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.</p><p>An animated interactive 3D complement (I3DC) is available in Proteopedia at <a href="http://proteopedia.org/w/Journal:JBSD:33" target="_blank">http://proteopedia.org/w/Journal:JBSD:33</a></p></div

QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR

<div><p>Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC₅₀) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (<a href="http://osddlinux.osdd.net/" target="_blank">http://osddlinux.osdd.net/</a>) and Galaxy (<a href="http://osddlinux.osdd.net:8001" target="_blank">http://osddlinux.osdd.net:8001</a>) version of software. We hope our webserver (<a href="http://crdd.osdd.net/oscadd/ntegfr/" target="_blank">http://crdd.osdd.net/oscadd/ntegfr/</a>) will play a vital role in designing new anticancer drugs.</p></div

Open source drug discovery- A new paradigm of collaborative research in tuberculosis drug development

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. (C) 2011 Elsevier Ltd. All rights reserved

Computational screening for new inhibitors of M. tuberculosis mycolyltransferases antigen 85 group of proteins as potential drug targets

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 IC50 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 IC90 at 20 μg/ml (53.023 μM). These results will be helpful to the medicinal chemists for developing new antitubercular molecules for testin