Research

Computational Modeling of Protein Conformational Landscapes

Research overview

Proteins are dynamic macromolecules whose conformational changes drive biological function, signaling, and disease. Understanding these transitions at the molecular level is essential for deciphering biological processes and developing therapeutics.

My research focuses on computational modeling of protein conformational landscapes to study functional transitions, membrane interactions, and aggregation-prone states. I investigate diverse systems, including bacterial toxins, viral proteins, and antimicrobial peptides, with emphasis on how different environments (e.g., lipid membranes, protein complexes) reshape conformational dynamics. To bridge biological length- and timescales with computational feasibility, I develop novel models and methods that combine advanced sampling, machine learning approaches, and both coarse-grained and all-atom molecular dynamics simulations. This integrated framework efficiently explores high-dimensional free energy landscapes and captures rare but functionally critical transitions. The left-hand figure illustrates the diverse types of protein conformational changes driven by inter- and intramolecular interactions. Current projects include membrane fusion mechanisms, protein aggregation pathways related to aging and neurodegeneration, and antimicrobial peptide dynamics against resistant bacteria.

Computational method development

Advanced sampling methods 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, 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. However, this approach still relies on the putative identification of reaction coordinates (RCs).

Enhanced simulation techniques that rely on low-dimensional manifolds (RCs) within a high-dimensional space can be as effective as selecting specific RCs. In this context, my work involves identifying RCs using machine learning techniques such as PCA, tICA, and an autoencoder, and exploring their strengths and weaknesses in studying complex systems.

Computational model development

Coarse-grained simulations: Martini-based and structure-based modeling

Molecular dynamics (MD) simulations of large atomic systems are computationally intensive and therefore often rely on coarse-grained (CG) modeling. I use CG techniques, such as structure-based modeling (SBM) and the Martini force field, to study the large-scale behavior of complex systems.

Building on the principle of minimal frustration, SBMs have traditionally been used to study protein folding. I expand their applicability to conformational transitions, demonstrating their utility through studies of the monomer-to-protomer transition in the pore-forming toxin ClyA as shown below.

Research projects

The classical view of protein structure-function assumes that a protein’s structure dictates its function. However, increasing evidence shows that conformational transitions in proteins, such as metamorphic, switchable, and disordered proteins, can directly trigger functional changes. My research focuses on understanding these transition-to-function relationships, particularly in the context of protein-membrane interactions. I am currently engaged in the following research projects:

Membrane fusion: Membrane fusion is a fundamental process involved in viral entry, vesicular trafficking (exosomes and endosomes), neurotransmission, and other cellular processes. Due to its complex and multiscale nature, capturing the full fusion pathway at molecular detail remains computationally challenging. My research focuses on developing computational models to study membrane fusion mechanisms.
Protein aggregation: Protein aggregation associated with aging is a key contributor to neurodegenerative diseases. Simulating protein misfolding processes using conventional or advanced molecular dynamics techniques remains computationally demanding. My work aims to develop effective models and gain mechanistic insights into protein misfolding.
Antimicrobial peptides: The rapid rise of antimicrobial resistance is a critical global health challenge, as highlighted by the World Health Organization, with India being one of the most affected regions. My research involves simulating novel antimicrobial peptides to elucidate their mechanisms of action, with the long-term goal of designing broad-spectrum antimicrobial agents.

Membrane proteins: Bacterial toxins, Viral proteins, and Antimicrobial peptides

Pore forming toxins

My research focuses on elucidating the fundamental principles that govern membrane protein mechanisms and the interplay between membrane components and protein dynamics, which are essential for cellular homeostasis. Specifically, my work includes investigating the interactions of pore-forming toxins (PFTs) with mammalian membranes, elucidating the mechanisms by which antimicrobial peptides (AMPs) insert into bacterial membranes to combat bacterial infections, and characterizing the prefusion-to-postfusion conformational transition of the SARS-CoV-2 spike protein.

Class I viral fusion (spike) proteins play a central role in mediating viral membrane fusion with host cell membranes. Fusion is triggered by large-scale conformational changes in the spike protein that bridge the viral and host membranes, enabling the transfer of viral genetic material into the host cell.

My work focuses on capturing the conformational changes of the spike protein and the associated membrane remodeling. This is an extremely complex problem, as it involves multiple molecular components, resulting in large system sizes and long timescales. I am actively developing novel computational models and methods to investigate the entire membrane fusion process, from spike protein conformational transitions to the final pore formation between the two membranes.

SARS-CoV-2 spike protein transition and membrane fusion

Protein aggregation

Protein misfolding: Thermodynamic and kinetic study of fibrillation

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.

“I, a universe of atoms, an atom in the universe.” ― Richard P. Feynman

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