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“I, a universe of atoms, an atom in the universe.” ― Richard P. Feynman
"Changes are inevitable, seen everywhere, and only constant. I work on understanding the physics of those changes in biomolecules using computer simulations at the molecular level. Insights into the transition mechanism can help us to modulate them as per our requirements."
Protein conformational changes
My research interest is in studying the complex conformational changes in proteins. Conformational changes in proteins, the most abundant biomolecule found in all living organisms, are ubiquitous and triggered by several factors essential for protein function. A molecular-level understanding of these changes is essential for diagnosing diseases stemming from protein dysfunction and devising effective therapeutic treatments.
Membrane proteins: Pore-forming bacterial toxins and Antimicrobial peptides
I focus on comprehending the fundamental principles underlying membrane protein mechanisms and the interplay between membrane components and protein dynamics crucial for cellular homeostasis. My work has encompassed studying the interaction of pore-forming toxins (PFTs) with mammalian membranes, as well as elucidating the mechanism by which antimicrobial peptides (AMPs) insert into bacterial membranes to mitigate the bacterial-mediated infection.
Thermodynamic and kinetics study of fibrillation (protein misfolding)
Amyloid fibrils, which are associated with ageing, are formed through various factors and contribute to neurodegenerative diseases and cancer. My research focuses on investigating the fibrillation mechanism, the implications of conformational heterogeneity, capturing conformational changes, and understanding the thermodynamic and kinetic behaviour of multidomain proteins using advanced sampling methods and specialised structure-based models.
Advanced sampling method development and machine learning techniques
While molecular dynamics (MD) simulation serves as the ultimate atomic microscope, simulating trajectories longer than a few microseconds is computationally expensive. However, interesting crucial phenomena, such as protein conformational changes and nucleation, occur on a longer time scale. To bridge the gap between real-time events and MD limitations, I developed a string method-based approach to capture complex conformational changes in proteins. This approach has been successfully applied to study membrane-driven conformational changes in a variety of proteins.
Enhanced simulation techniques that rely on low-dimensional manifolds, also known as reaction coordinates (RCs), within a high-dimensional change can be as effective as selected RCs. In this context, my work involves identifying RCs using machine learning techniques such as PCA, tICA, and autoencoder, and exploring their strengths and weaknesses in studying complex systems.
Coarse-grained simulations
Molecular dynamics (MD) simulations of system with a large number of atoms is computationally expensive and often require a coarse-grained (CG) system. I have worked with the Martini 2 CG model in the past to study the protein conformational changes. I work with CG techniques if the atomic details are not necessary and we are only interested in the bulk behavior of the system.
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