Honors & Awards
INSPIRE Award, Department of Science and Technology, INDIA (2014-2019)
Research Excellence Award, Indian Institute of Technology, Bombay, INDIA (2012)
Research Internship Award, Indian Institute of Technology, Bombay, INDIA (2011-2012)
Gold Medal, Yogi Vemana University, INDIA (2012)
Doctor of Philosophy, Indian Institute of Technology, Madras (2021)
Master of Science, Unlisted School (2012)
Ph.D., Indian Institute of Technology, Madras, IITMadras, Cancer Therapeutics (2021)
Integrated M.Sc, Yogi Vemana University, India, Biotechnology and Bioinformatics (2012)
Current Research and Scholarly Interests
I am interested in elucidating molecular mechanisms of MYC-driven drug resistance and immune evasion in cancer using computational and experimental models.
- Novel BH4-BCL-2 Domain Antagonists Induce BCL-2-Mediated Apoptosis in Triple-Negative Breast Cancer Cancers 2022
Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery
ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY, VOL 121
2020; 121: 25-47
In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.
View details for DOI 10.1016/bs.apcsb.2019.11.013
View details for Web of Science ID 000610798800002
View details for PubMedID 32312424
Amarogentin, a secoiridoid glycoside, activates AMP- activated protein kinase (AMPK) to exert beneficial vasculo-metabolic effects
BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS
2019; 1863 (8): 1270-1282
AMP-activated protein kinase (AMPK) is a drug target for treatment of metabolic and cardiovascular complications. Extracts of Gentianaceace plants exhibit anti-diabetic and anti-atherosclerotic effects, however, whether their phyto-constitutents activate AMPK remains to be determined.Molecular docking of Gentiana lutea constituents was performed with crystal structure of human α2β1γ1 trimeric AMPK (PDB ID: 4CFE). Binding of Amarogentin (AG) to α2 subunit was confirmed through isothermal titration calorimetry (ITC) and in vitro kinase assays were performed. L6 myotube, HUH7 and endothelial cell cultures were employed to validate in silico and in vitro observations. Lipid lowering and anti-atherosclerotic effects were confirmed in streptozotocin induced diabetic mice via biochemical measurements and through heamatoxylin and eosin, Masson's trichrome and Oil Red O staining.AG interacts with the α2 subunit of AMPK and activates the trimeric kinase with an EC50 value of 277 pM. In cell culture experiments, AG induced phosphorylation of AMPK as well as its downstream targets, acetyl-coA-carboxylase (ACC) and endothelial nitric oxide synthase (eNOS). Additionally, it enhanced glucose uptake in myotubes and blocked TNF-α induced endothelial inflammation. Oral supplementation of AG significantly attenuated diabetes-mediated neointimal thickening, and collagen and lipid deposition in the aorta. It also improved circulating levels of lipids and liver function in diabetic mice.In conclusion, AG exerts beneficial vasculo-metabolic effects by activating AMPK.Amarogentin, a naturally occurring secoiridoid glycoside, is a promising lead for design and synthesis of novel drugs for treatment and management of dyslipidemia and cardiovascular diseases.
View details for DOI 10.1016/j.bbagen.2019.05.008
View details for Web of Science ID 000471356900005
View details for PubMedID 31125678
Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer
CURRENT TOPICS IN MEDICINAL CHEMISTRY
2019; 19 (6): 457-466
Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects.In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer.Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs.The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges.Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.
View details for DOI 10.2174/1568026619666190304143354
View details for Web of Science ID 000466710100006
View details for PubMedID 30836917
Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors
CURRENT TOPICS IN MEDICINAL CHEMISTRY
2019; 19 (13): 1162-1172
Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately.In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2).We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching.We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds.Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.
View details for DOI 10.2174/1568026619666190618142432
View details for Web of Science ID 000483383900007
View details for PubMedID 31210110
Drug-Target Interactions: Prediction Methods and Applications
CURRENT PROTEIN & PEPTIDE SCIENCE
2018; 19 (6): 537-561
Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined.
View details for DOI 10.2174/1389203718666161108091609
View details for Web of Science ID 000430104200003
View details for PubMedID 27829350
Importance of functional groups in predicting the activity of small molecule inhibitors for Bcl-2 and Bcl-xL
CHEMICAL BIOLOGY & DRUG DESIGN
2017; 90 (2): 308-316
Evasion of apoptosis owing to aberrant expression of Bcl-2 (B-cell lymphoma-2) anti-apoptotic proteins is a promising hallmark of cancer. These proteins are associated with resistance to chemotherapy and radiation. Currently available QSAR models are limited to a set of inhibitors corresponding to a particular chemical scaffold, and unified models are required to identify the differential specificity of diverse compounds toward inhibiting these targets. In this study, we predicted the factors driving differential activity and specificity implementing multiplexed QSAR analysis for a dataset of 1,649 reported inhibitors of Bcl-2 (B-cell lymphoma-2) and Bcl-xL (B-cell lymphoma-extra large). We developed QSAR models for seven diverse scaffolds and critically analyzed the chemical space with coupling factors. The correlation values of QSAR models for Bcl-2 and Bcl-xL range from 0.95 to 0.985. The MAE and sMAPE of the models were in the range of 0.052-5.4 nm and 0.41%-10%, respectively, signifying model robustness. The crucial descriptors and moieties accounting for the activity were benchmarked against experimentally determined binding patterns. The comprehensive analysis made in the study explores latent features of the chemical space in a broad perspective. Further, we have developed a user-friendly Web server for predicting a specific/dual inhibitor of Bcl-2 and Bcl-xL [http://www.iitm.ac.in/bioinfo/APPLE/].
View details for DOI 10.1111/cbdd.12952
View details for Web of Science ID 000405102900014
View details for PubMedID 28112863
- Ligand-Based Pharmacophore Modeling and Virtual Screening of RAD9 Inhibitors JOURNAL OF CHEMISTRY 2013; 2013