Introduction
During the last one decade, we have been witnessing exciting advances in AI/ML based methods for elucidating the relationship among sequence, structure, function and disease, in proteins and their complexes. This special issue is aimed at providing the latest developments to understand/predict the structurally and functionally important residues in proteins and their complexes, as well as thermodynamics of folding and binding. These investigations include folding and stability of globular and membrane proteins, stabilizing, aggregation prone, disorder, hotspot and binding site residues, folding and binding free energy, and the influence of mutations on structure, function and diseases. Specifically, it covers the interactions of proteins with other molecules such as proteins, nucleic acids, carbohydrates, peptides and ligands as well as different methods based on machine learning, LLM and AI. Further, recent developments on understanding and predicting protein structural and thermodynamic parameters, molecular modeling, molecular dynamics simulations, structure based drug design, change in free energy upon mutation and detecting disease-causing mutations in proteins will be included in the special issue.
Keywords
Protein Folding, Protein Stability, Binding Site Residues, Binding Affinity, Structure Prediction, Protein Aggregation, AI/ML-Based Methods, Mutation
Sub-topics
- Folding and stability of globular, membrane and thermophilic proteins
- Aggregation, disorder, folding rate, binding sites and binding affinity of proteins and their complexes
- Prediction of residue wise properties, secondary and tertiary structures of proteins and complexes
- Mutational effects on change in stability, binding affinity and disease-causing mutations in proteins and
- their complexes
- Database analysis and statistical, AI, LLM and machine learning-based methods
- QSAR, Molecular modeling and molecular dynamics simulations
