Himanshu A. Pandya

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models

The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure–activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ( or q2) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger–Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger–Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.

Prioritization of active antimalarials using structural interaction profile of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives

An empirical relationship between the experimental inhibitory activities of triclosan derivatives and its computationally predicted Plasmodium falciparum enoyl-acyl carrier protein (ACP) reductase (PfENR) dock poses was developed to model activities of known antimalarials. A statistical model was developed using 57 triclosan derivatives with significant measures (r = 0.849, q2 = 0.619, s = 0.481) and applied on structurally related and structurally diverse external datasets. A substructure-based search on ChEMBL malaria dataset (280 compounds) yielded only two molecules with significant docking energy, whereas eight active antimalarials (EC50 < 100 nM, tested on 3D7 strain) with better predicted activities (pIC50 ~ 7) from Open Access Malaria Box (400 compounds) were prioritized. Further, calculations on the structurally diverse rhodanine molecules (known PfENR inhibitors) distinguished actives (experimental IC50 = 0.035 μM; predicted pIC50 = 6.568) and inactives (experimental IC50 = 50 μM; predicted pIC50 = -4.078), which showed that antimalarials possessing dock poses similar to experimental interaction profiles can be used as leads to test experimentally on enzyme assays.

Compound prioritization from inverse docking experiment using receptor‐centric and ligand‐centric methods: a case study on Plasmodium falciparum Fab enzymes

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor‐centric and ligand‐centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge‐driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative‐structure activity relationship and multilayer perceptron models. Copyright

Development of pharmacophore similarity-based quantitative activity hypothesis and its applicability domain: applied on a diverse data-set of HIV-1 integrase inhibitors

Quantitative pharmacophore hypothesis combines the 3D spatial arrangement of pharmacophore features with biological activities of the ligand data-set and predicts the activities of geometrically and/or pharmacophoric similar ligands. Most pharmacophore discovery programs face difficulties in conformational flexibility, molecular alignment, pharmacophore features sampling, and feature selection to score models if the data-set constitutes diverse ligands. Towards this focus, we describe a ligand-based computational procedure to introduce flexibility in aligning the small molecules and generating a pharmacophore hypothesis without geometrical constraints to define pharmacophore space, enriched with chemical features necessary to elucidate common pharmacophore hypotheses (CPHs). Maximal common substructure (MCS)-based alignment method was adopted to guide the alignment of carbon molecules, deciphered the MCS atom connectivity to cluster molecules in bins and subsequently, calculated the pharmacophore similarity matrix with the bin-specific reference molecules. After alignment, the carbon molecules were enriched with original atoms in their respective positions and conventional pharmacophore features were perceived. Distance-based pharmacophoric descriptors were enumerated by computing the interdistance between perceived features and MCS-aligned ‘centroid’ position. The descriptor set and biological activities were used to develop support vector machine models to predict the activities of the external test set. Finally, fitness score was estimated based on pharmacophore similarity with its bin-specific reference molecules to recognize the best and poor alignments and, also with each reference molecule to predict outliers of the quantitative hypothesis model. We applied this procedure to a diverse data-set of 40 HIV-1 integrase inhibitors and discussed its effectiveness with the reported CPH model.

Evolutionary and molecular aspects of Indian tomato leaf curl virus coat protein.

Tomato leaf curl disease (ToLCD) is manifested by yellowing of leaf lamina with upward leaf curl, leaf distortion, shrinking of the leaf surface, and stunted plant growth caused by tomato leaf curl virus (ToLCV). In the present study, using computational methods we explored the evolutionary and molecular prospects of viral coat protein derived from an isolate of Vadodara district, Gujarat (ToLCGV-[Vad]), India. We found that the amino acids in coat protein required for systemic infection, viral particle formation, and insect transmission to host cells were conserved amongst Indian strains. Phylogenetic studies on Indian ToLCV coat proteins showed evolutionary compatibility with other viral taxa. Modeling of coat protein revealed a topology similar to characteristic Geminate viral particle consisting of antiparallel β-barrel motif with N-terminus α-helix. The molecular interaction of coat protein with the viral DNA required for encapsidation and nuclear shuttling was investigated through sequence- and structure-based approaches. We further emphasized the role of loops in coat protein structure as molecular recognition interface.

Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease

Alzheimer’s disease (AD) is one of the most significant neurodegenerative disorders and its symptoms mostly appear in aged people. Catechol-o-methyltransferase (COMT) is one of the known target enzymes responsible for AD. With the use of 23 known inhibitors of COMT, a query has been generated and validated by screening against the database of 1500 decoys to obtain the GH score and enrichment value. The crucial features of the known inhibitors were evaluated by the online ZINC Pharmer to identify new leads from a ZINC database. Five hundred hits were retrieved from ZINC Pharmer and by ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering by using FAF-Drug-3 and 36 molecules were considered for molecular docking. From the COMT inhibitors, opicapone, fenoldopam, and quercetin were selected, while ZINC63625100_413 ZINC39411941_412, ZINC63234426_254, ZINC63637968_451, and ZINC64019452_303 were chosen for the molecular dynamics simulation analysis having high binding affinity and structural recognition. This study identified the potential COMT inhibitors through pharmacophore-based inhibitor screening leading to a more complete understanding of molecular-level interactions.

Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions

Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure–activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.

Structural Insights into the Theoretical Model of Plasmodium falciparum NADH Dehydrogenase and its Interaction with Artemisinin and Derivatives: Towards Global Health Therapeutics

It is a continuing quest to uncover the principal molecular targets of malarial parasites to understand the antimalarial activity and mechanism of action of artemisinin, a potent antimalarial. A series of parasite proteins are experimentally validated as potential targets, such as translationally controlled tumor protein (TCTP) and sarco/endoplasmic reticulum membrane calcium ATP-ase (SERCA). The present study addressed the development of a theoretical model of Plasmodium falciparum NADH dehydrogenase with inference from artemisinin in vivo inhibitory activity. We report here the predicted binding modes of artemisinin and its derivatives. The modeled protein resembled the structural architecture of flavoproteins and oxidoreductases, consisting of two Rossmann folds and dedicated binding sites for its cofactors. Docked poses of the ligand dataset revealed its interactions at or near the si face, indicating being activated. This may aid in generation of reactive oxygen species, thereby disrupting the membrane potential of parasite mitochondria and leading to the clearance from the blood. These observations open up new strategies for development of novel therapeutics, or improvement of existing pharmacotherapies against malaria, a major burden for global health.

Epitope-Based Immunoinformatics and Molecular Docking Studies of Nucleocapsid Protein and Ovarian Tumor Domain of Crimean–Congo Hemorrhagic Fever Virus

Crimean–Congo hemorrhagic fever virus (CCHFV), the fatal human pathogen is transmitted to humans by tick bite, or exposure to infected blood or tissues of infected livestock. The CCHFV genome consists of three RNA segments namely, S, M, and L. The unusual large viral L protein has an ovarian tumor (OTU) protease domain located in the N terminus. It is likely that the protein may be autoproteolytically cleaved to generate the active virus L polymerase with additional functions. Identification of the epitope regions of the virus is important for the diagnosis, phylogeny studies, and drug discovery. Early diagnosis and treatment of CCHF infection is critical to the survival of patients and the control of the disease. In this study, we undertook different in silico approaches using molecular docking and immunoinformatics tools to predict epitopes which can be helpful for vaccine designing. Small molecule ligands against OTU domain and protein–protein interaction between a viral and a host protein have been studied using docking tools.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints

Abstract It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r2 = 0.99) and validated (q2 = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.