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      Identification of inhibitors against α-Isopropylmalate Synthase of Mycobacterium tuberculosis using docking-MM/PBSA hybrid approach

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          Abstract

          α-Isopropylmalate Synthase (α-IPMS) encoded by leuA in Mycobacterium tuberculosis (M.tb) is involved in the leucine biosynthesis pathway and is extremely critical for the synthesis of branched-chain amino acids (leucine, isoleucine and valine). α-IPMS activity is required not only for the proliferation of M.tb but is also indispensable for its survival during the latent phase of infection. It is absent in humans and is widely regarded as one of the validated drug targets against Tuberculosis (TB). Despite its essentiality, any study on designing of potential chemical inhibitors against α-IPMS has not been reported so far. In the present study, in silico identification of putative inhibitors against α-IPMS exploring three chemical databases i.e. NCI, DrugBank and ChEMBL is reported through structurebased drug design and filtering of ligands based on the pharmacophore feature of the actives. In the absence of experimental results of any inhibitor against α-IPMS, a stringent validation of docking results is done by comparing with molecular mechanics/Poisson- Boltzmann surface area (MM/PBSA) calculations by investigating two more proteins for which experimental results are known.

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          Most cited references19

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          Genes required for mycobacterial growth defined by high density mutagenesis.

          Despite over a century of research, tuberculosis remains a leading cause of infectious death worldwide. Faced with increasing rates of drug resistance, the identification of genes that are required for the growth of this organism should provide new targets for the design of antimycobacterial agents. Here, we describe the use of transposon site hybridization (TraSH) to comprehensively identify the genes required by the causative agent, Mycobacterium tuberculosis, for optimal growth. These genes include those that can be assigned to essential pathways as well as many of unknown function. The genes important for the growth of M. tuberculosis are largely conserved in the degenerate genome of the leprosy bacillus, Mycobacterium leprae, indicating that non-essential functions have been selectively lost since this bacterium diverged from other mycobacteria. In contrast, a surprisingly high proportion of these genes lack identifiable orthologues in other bacteria, suggesting that the minimal gene set required for survival varies greatly between organisms with different evolutionary histories.
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            Benchmarking sets for molecular docking.

            Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.
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              Further development and validation of empirical scoring functions for structure-based binding affinity prediction.

              New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes. X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.
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                Author and article information

                Journal
                Bioinformation
                Bioinformation
                Bioinformation
                Bioinformation
                Biomedical Informatics
                0973-2063
                2017
                31 May 2017
                : 13
                : 5
                : 144-148
                Affiliations
                [1 ]School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, INDIA 110067
                Author notes
                [* ]Andrew M. Lynn andrew@ 123456jnu.ac.in
                [* ]Pradipta Bandyopadhyay praban07@ 123456gmail.com
                Article
                97320630013144
                10.6026/97320630013144
                5498780
                c7fdca8d-e45e-410b-b694-b5c5bfcf319f
                © 2017 Biomedical Informatics

                This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.

                History
                : 18 April 2017
                : 21 April 2017
                Categories
                Hypothesis

                Bioinformatics & Computational biology
                α-isopropylmalate synthase,mycobacterium tuberculosis,docking-mm/pbsa hybrid

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