• Substrate specificity of biosynthetic proteins & identification of novel metabolic pathways
• Prediction of protein-peptide interaction & identification of protein interaction networks
• Systems biology approach for identification of conserved network modules & deciphering their dynamic features

Current Focus Areas

• Machine learning-based methods are being developed for in silico identification and functional annotation of prokaryotic small ORFs and prediction of the functional consequence of the disease associated mutations in smORFs found in human genome.
• Microsecond scale MD simulations are being used to understand how binding of allosteric inhibitors to the mutant kinases can potentially shift the population of conformers from active to inactive state. Machine learning based scoring function is being developed for prediction of allosteric inhibitors for EGFR.
• AI/ML-based method has been developed for prediction of genotypic drug resistance (DR) for M. tb using whole genome sequences and identification of novel DR associated mutations. Structural modelling of DR associated mutant proteins is being carried out to decipher the structural basis of drug resistance.
• In order to evaluate the prediction accuracy of the AI/ML-based methods for prediction of oligomeric complexes of proteins & host-virus PPIs, systematic benchmarking of Alphafold2 and ESMFold have been carried out on newly released protein structures which lack high homology with known structures.
• Peptide-specific (individual model for each epitope) and pan-specific (single model for all epitopes) machine learning models have been developed to predict the recognition specificities of TCR-pMHCs by utilizing only epitope and CDR3β sequences obtained from public domain TCRSeq data.