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Structurre, function and Dynamics study of Cancer causing Protein and G Protein Coupled Receptors (GPCRs).
Computational Chemistry: MD simulation study of proteins, enzyme–substrate complexes, protein-metal complexes and RNA-peptide complexes. pH dependent MD simulation study of enzyme to mimic experimental methods. Water molecular dynamics of macromolecules. Identification of hydration or water sites in protein using Inhomogeneous Fluid Solvation theory (IFST). thermodynamic and structural dynamic parameters of water molecules and analysis of their correlation. Conformational dynamics of RNA-peptide complexes. Loop-flap molecular dynamics study of protein. Binding free energy calculation of ligand by MM (GB) PB and LIE-D methods. Thermal denaturation of ribozyme. Force filed parameterization of new ligands.
Computational Drug Discovery: Salt Bridge based drug discovery. Conserved water based drug discovery using Inhomogeneous Fluid Solvation theory (IFST). Isoform specific drug discovery, Structure based drug discovery, Enzymatic mechanism based drug discovery. Virtual screening from ligand database, QSAR and ADMET Properties Prediction. Statistical analysis for drug discovery.
Computational Biophysics: Protein engineering, Prediction of 3D structure of protein and loop of the protein structure by homology modeling, energy minimization and molecular dynamics simulation study. Modeling the ternary complexe of metallo proteins. Inter domain recognition process of macromolecules. Identifying and modeling the key water molecules in protein and protein-ligand complexes. Prediction the flexibility and functional role of water molecules. Methods for pocket monitoring during MD simulation.
Computational Biochemistry: Purine nucleotide biosynthesis pathway analysis. Activity and mechanism of enzyme. Conformational transition of enzyme from ligand free to ligand bound state. Prediction the functional role of non catalytic residues of enzyme – substrate complexes. Identification of allosteric site of the enzyme. Subsite site recognition of ligands.
Molecular Modeling and Docking study: Prediction the model structure of new ligand and modification of existing ligand on the basis of ligand–water interaction. Database development of new ligands. Molecular docking study of protein-protein, protein-inhibitor, protein-ligand and protein-drugs.
Functional Biology
Omics Data Analysis: Omics data analysis involves the study and interpretation of large-scale biological datasets generated through high-throughput technologies, such as genomics, transcriptomics, proteomics, and metabolomics. These data provide comprehensive insights into biological systems by capturing molecular profiles across genes, proteins, metabolites, etc., helping to identify biomarkers, disease mechanisms, and potential therapeutic targets.
Biomarker Identification from Microarray and RNA-Seq Data: In functional biology, biomarker identification using microarray or RNA-sequencing (RNA-Seq) data involves analyzing gene expression patterns to pinpoint molecules that can indicate disease states, predict responses to treatment, or track disease progression. Differential expression analysis helps identify genes that are significantly up- or downregulated in conditions of interest, serving as potential biomarkers.
RNA Regulatory Network: An RNA regulatory network refers to the complex interactions between RNA molecules (such as mRNA, microRNA, and lncRNA) and their targets (genes and proteins). These interactions regulate gene expression at the transcriptional and post-transcriptional levels. Understanding these networks provides insights into gene regulation mechanisms and their roles in cellular processes, such as development, differentiation, and disease.
Database Development: Database development in functional biology involves creating structured systems to store, manage, and analyze large biological datasets, such as genomic sequences, gene expression profiles, and protein interactions. Well-designed databases facilitate the retrieval, sharing, and integration of data, enabling researchers to explore complex biological questions and support discoveries in genomics, systems biology, and personalized medicine.
Machine Learning and artificial intelligence in biology
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