Polamarasetty Aparoy

Bio: Polamarasetty Aparoy is an academic researcher from Central University of Himachal Pradesh. The author has contributed to research in topics: Docking (molecular) & Homology modeling. The author has an hindex of 13, co-authored 28 publications receiving 692 citations. Previous affiliations of Polamarasetty Aparoy include Indian Institute of Petroleum & University of Hyderabad.

Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study.

Abstract: Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.

An analysis of hydrophobic interactions of thymidylate synthase with methotrexate: Free energy calculations involving mutant and native structures bound to methotrexate

Abstract: Since the human body for many reasons can adapt and become resistant to drugs, it is important to develop and validate computer aided drug design (CADD) methods that could help predict binding affinity changes that can result from these resistant enzymes. The free energy perturbation (FEP) methodology is the most accurate means of estimating relative binding affinities between inhibitors and protein variants. In this paper, we describe the role played by hydrophobic residues lining the active site region, particularly 79 Ile and 176 Phe, in the binding of methotrexate to the Escherichia coli (E. coli) thymidylate synthase (TS) enzyme, using the thermodynamic cycle perturbation (TCP) approach. The computed binding free energy differences on the binding of methotrexate to the native and some mutant E. coli TS structures have been compared with experimental results. Computationally, four different ‘mutations’ have been simulated on the TS enzyme with methotrexate (MTX): 79 Ile → 79 Val; 79 Ile → 79 Ala; 79 Ile → 79 Leu; and 176 Phe → 176 Ile. The calculated results indicate that in each of these cases, the native residues ( 79 Ile and 176 Phe) interact more favorably with methotrexate than the mutant residues and these results are corroborated by experimental measurements. Binding preference to wild type residues can be rationalized in terms of their better hydrophobic contacts with the phenyl ring of methotrexate.

Understanding the Dual Inhibition of COX-2 and Carbonic Anhydrase-II by Celecoxib and CG100649 Using Density Functional Theory Calculations and other Molecular Modelling Approaches.

Abstract: Recent developments in the dual inhibition studies of cyclooxygenase-2 (COX-2) and carbonic anhydrase (CA-II) imply a promising platform for the development of new generations of nonsteroidal anti-inflammatory drugs (NSAIDs). CG100649 is such a molecule that got recently approved by Korean Ministry of Food and Drug safety (MFDS) and is being marketed by the name polmacoxib for the treatment of osteoarthritis. CG100649 significantly inhibits CA-II in blood and COX-2 in inflammatory tissues. However, the mechanism of CG100649 dual inhibition of COX-2/CA-II is not well understood. In this study, we employed well known methods like pharmacophore modelling, a DFT based quantum chemical descriptors analysis, and molecular docking to explore the chemical features and to understand the binding behaviour of CG100649 along with other COX-2/CA-II dual inhibitors. The HOMO-LUMO and docking results indicated the prominent role of aryl sulphonamide in CG100649. The aryl sulphonamide moiety formed T-shaped Π…Π interactions with His94 in the CA-II active site, which was not observed in the case of celecoxib. Other crucial interactions were also observed which may aid in further understanding the action of dual inhibitors of this class.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Computational methods in drug discovery.

Abstract: The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.