Proteins are the molecular machinery of the cell and are used to perform nearly every cellular function. Mutations have the potential to affect folding, stability and binding to its partners (other proteins, ligands, DNA/RNA). Our lab uses computational tools and techniques to address research problems in this area.


  1. Jemimah, S., Yugandhar, K., and Gromiha, M. M. (2017). PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics. Bioinformatics (in press).
  2. Gromiha, M. M., Yugandhar, K., and Jemimah, S. (2016) Protein-protein interactions: scoring schemes and binding affinity. Curr. Opin. Struct. Biol., 44: 31-38.
  3. Chaudhary, P., Naganathan, A. N. and Gromiha, M. M. (2015) Folding RaCe: A Robust Method for Predicting Changes in Protein Folding Rates upon Point Mutations. Bioinformatics, 31(13): 2091-7.
  4. Chaudhary, P., Naganathan, A. N. and Gromiha, M. M. (2016) Prediction of change in protein unfolding rates upon point mutations in two state proteins. Biochim. Biophys. Acta, Proteins Proteomics, 1864(9): 1104-9.
  5. Gromiha, M. M. and Yugandhar, K. (2017) Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes. Prog. Biophys. Mol. Biol. (in press).
  6. Gromiha, M. M., Anoosha, P., and Huang, L.-T. (2016) Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants. Methods Mol. Biol., 1415: 71-89.
  7. Magyar, C., Gromiha, M. M., Pujadas, G., Tusnády, G. E. and Simon I. (2005). SRide: a server for identifying stabilizing residues in proteins. Nucleic Acids Res., 33: W303-W305.
  8. Kulandaisamy, A., Lathi, V., ViswaPoorani, K., Yugandhar, K., and Gromiha, M. M., (2017). Important amino acid residues involved in folding and binding of protein-protein complexes. Int. J. Biol. Macromol., 94: 438-44.


Protein aggregation has been implicated in many human disorders including Alzheimer's, Parkinson's, Huntington's, prion disease and Type II diabetes. Generally, a buildup of misfolded/unfolded proteins (i.e. a protein aggregate) is sequestered in inclusion bodies in the cell. This process may be disrupted for various reasons (such as mutations, stress and aging), resulting in the accumulation of protein aggregates within or outside the cell, leading to the formation of amyloids. These amyloids are associated with various disorders, particularly neurodegenerative diseases. Several in-vitro experiments have shown that even small environmental changes (pH, temperature, ionic concentration and additives) or substitutions in the protein sequence can have drastic effects on the rate of aggregation. Aggregation propensity depends on various factors, particularly the presence of aggregation prone regions (APRs) within the protein. These regions are capable of forming the cross-beta steric zipper motif which forms the stabilizing hydrophobic core of amyloid fibrils. We have developed a database and algorithms for the purpose of investigating protein aggregation.


  1. Thangakani, A. M., Kumar, S., Nagarajan, R., Velmurugan, D. and Gromiha, M. M. (2014) GAP: Towards almost hundred percent prediction for β-strand mediated aggregating peptides with distinct morphologies. Bioinformatics, 30(14): 1983-90.
  2. Kumar, S., Thangakani, A. M., Nagarajan, R., Singh, S. K., Velmurugan, D. and Gromiha, M. M. (2016) Autoimmune Responses to Soluble Aggregates of Amyloidogenic Proteins Involved in Neurodegenerative Diseases: Overlapping Aggregation Prone and Autoimmunogenic regions. Sci. Rep., 6: 22258.
  3. Prabakaran, R., Goel, D., Kumar, S. and Gromiha, M. M. (2017) Aggregation Propensity of Human Proteome: Insights from large-scale data analyses. Proteins (in press).
  4. Thangakani, A. M., Nagarajan, R., Kumar, S., Sakthivel, R., Velmurugan, D. and Gromiha, M. M. (2016) CPAD, Curated Protein Aggregation Database: A Repository of Manually Curated Experimental Data on Protein and Peptide Aggregation. PLoS One, 11(4): e0152949.


Rapid progress in next-generation DNA sequencing technology has generated vast amounts of SNP (Single Nucleotide Polymorphism) data. SNPs occurring in gene-coding regions translate into missense mutations in proteins, with the potential to affect folding, stability, function and interaction. Mutations have been implicated in a number of human diseases and are of special interest in our lab. We use computational techniques to conduct large-scale analyses of mutations and also focus on mutations in proteins involved in specific diseases.


  1. Anoosha, P., Sakthivel, R. and Gromiha, M. M. (2016) Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets. Biochim. Biophys. Acta, Mol. Basis Dis., 1862(2): 155-65.
  2. Anoosha, P., Huang, L.-T., Sakthivel, R., Karunagaran, D. and Gromiha, M. M. (2015) Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer. Mutat. Res. Fund. Mol. Mech. Mut., 780: 24-34.
  3. Michael Gromiha, M. M. and Ou Y-Y. (2014) Bioinformatics approaches for functional annotation of membrane proteins. Brief. Bioinf, 15(2), 155-168.


Structure-based drug design is a major area of research in our lab.


  1. Anusuya, S. and Gromiha, M. M. (2017) Quercetin derivatives as non-nucleoside inhibitors for dengue polymerase: molecular docking, molecular dynamics simulation and binding free energy calculation, J. Biomol. Struct. Dyn., DOI: 10.1080/07391102.2016.1234416.
  2. Anusuya, S., Velmurugan, D. and Gromiha, M. M. (2016) Identification of Dengue Viral RNA Dependent RNA Polymerase Inhibitor using Computational Fragment Based Approaches and Molecular Dynamics Study. J. Biomol. Struct. Dyn., 34(7): 1512-32.
  3. V. Kanakaveti, R. Sakthivel, Suresh K. Rayala and M. Michael Gromiha (2017) Importance of functional groups in predicting the activity of small molecule inhibitors of Bcl-2 and Bcl-xL. Chem. Biol. Drug Des. (in press).
  4. Ramakrishnan, C., Thangakani, A. M., Velmurugan, D., Krishnan, D. A., Sekijima, M., Akiyama, Y. and Gromiha, M. M. (2017) Identification of type I and type II inhibitors of c-Yes kinase using in silico and experimental techniques. J. Biomol. Struct. Dyn., 1-11.
  5. Chiba, S. et al. (2015) Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Sci. Rep., 5: 17209.


NGS and deep learning represent significant advances in technology and will have a profound effect on research in computational biology.