Proteins are fundamental to biological processes, serving as the molecular machinery of life. While traditionally viewed as rigid structures with defined three-dimensional shapes, many proteins exhibit significant structural flexibility. Among these are intrinsically disordered proteins (IDPs), which lack a fixed, ordered structure and exist as dynamic ensembles of conformations. This inherent flexibility is not a limitation but a functional feature that enables IDPs to adapt their shapes for diverse interactions. IDPs play critical roles in cellular processes such as signaling and regulation. They are particularly versatile due to their ability to undergo disorder-to-order transitions upon binding to other molecules or form "fuzzy complexes" where they remain partially disordered. However, this flexibility also links IDPs to various diseases, including Alzheimer’s, type 2 diabetes mellitus, and Parkinson’s disease.

Our lab has developed advanced computational tools to model the kinetics of IDPs, providing detailed insights into their dynamic behavior. The primary tool used here is molecular dynamics simulations, which opens up a way to study the structure and dynamics of molecules at high resolution. In this project, we aim to apply these tools to study the dynamics of  IDPs implicated in diabetes and other diseases. These methods will open up ways to develop targeted therapies.

This is a computational project and involves performing and analysing molecular dynamics trajectories using in-house developed codes. The student will also be trained in performing python coding and will use machine learning techniques.