Research Interests
Modeling Biomolecular Phase Separation
Spatiotemporal organisation of the sub-cellular space is vital to the way key cellular processes function. While the presence of membrane-bound organelles in compartmentalizing biochemical reactions is well know, recent advances in cell biology have identified droplet-like, high-density clusters of proteins and RNA which form and dissolve in response specific triggers. The key focus of my postdoctoral research is to understand this phenomenon of liquid-liquid phase separation of bio-molecules in mechanistic detail using computational models of different resolutions.
Computational modeling of Protein Aggregation and Amyloid Self-assembly
Amyloid aggregation is a phenomenon in which proteins/peptides self-assemble into highly ordered structures that are associated with diseases like Alzheimers` and Parkinson’s as well as several native biological functions. A wide range of proteins/peptides can form these structures while displaying remarkable phenomenological similarities, both structural and kinetic. Additionally, various factors, both at the sequence level (mutations) and in solution can modulate the aggregation behavior of these proteins/peptides. There are thus various points of interest while studying protein aggregation, viz the early stage interactions, the kinetic profiles and the morphologies of mature fibrils. My doctoral work primarily focuses on providing a broad minimalistic framework to explain the phenomenon of protein self-assemby into amyloids. Using a coarse-grained description of polypeptide chains, we probed whether a modulation of two fundamental intrinsic features of any polymer chain, viz the backbone flexibility and the self-interaction propensity could capture the diversity in protein aggregates. Our results strengthen the claim that amyloid formation could be an intrinsic feature of (m)any self-assembling peptide chain and not a phenomenon restricted to any class of proteins or any conserved set of sequences. The second aspect of the study is targeted at understanding the longer length and time-scale (kinetic) signatures of amyloid self-assembly. Using the phenomenological understanding of factors that could govern protein aggregation, we developed a minimalistic kinetic model for protein aggregation which is analytically solvable and can be simulated using computationally less expensive simulations. Additionally, we use atomistic simulations to identify the early interactions and factors that drive proteins/peptides to aggregate. The molecular simulations help us identify the residue specific and structural aspects of protein aggregation as well as the role of other modulating factors in regulating the phenomenon.