Jagannath Mondal

Tata Institute of Fundamental Research, Hyderabad (TIFR-H)

E-mail: jmondal[at]tifrh.res.in

Brief Bio-sketch:

Dr. Jagannath Mondal is a computational biophysicist. He earned his PhD at University of Wisconsin Madison, USA (Research Advisor: Arun Yethiraj) in 2011. After a postdoctoral stint at Columbia University (Research Advisor: Bruce J. Berne), he joined Tata Institute of Fundamental Research, Hyderabad and currently serving as a reader there since 2015. He is also a Ramanujan fellow and associate of Indian Academy of Science. His research interest involves computer simulation of chemically and biologically relevant processes. The current research project occasionally leans on diverse topics on biomolecular recognition at real time, optimization of collective variables and cellular biological processes.

OPTIMIZATION AND ASSESSMENT OF COLLECTIVE VARIABLES FOR BIOPHYSICAL PROCESSES

Collective variables (CV), when chosen judiciously, can play an important role in recognizing rate-limiting processes and rare events in any biomolecular systems. However, high dimensionality and inherent complexities associated with such biochemical systems render the identification of an optimal CV a challenging task, which in turn precludes the elucidation of underlying conformational landscape in sufficient details. Here, We show how an optimized CV can shed light into two relevant systems: Folding of a 16-residue GB1 protein and biomolecular recognition of a ligand to hydrophobic cavity. Expressed as a linear combination of a number of traditional CVs, the optimal CV for each of these system is derived by employing recently introduced Time-structured Independent Component Analysis (TICA) approach on a large number of independent unbiased simulations. The projected simulation trajectories along the optimized CVs are able to render a well-resolved free energy landscape for both the systems. Furthermore, a quantitative sensitivity analysis of each constituent in the optimized CV provided key insights on the relative contributions of the constituent CVs in the overall free energy landscapes. Finally, the kinetic pathways connecting these metastable states, constructed using a Markov State Model, provide an optimum description of underlying processes. Taken together, this work offers a quantitatively robust approach towards comprehensive mapping of the underlying processes.

References:

N. Ahalawat and J. Mondal, J.Chem.Phys. 149, 094101 (2018)

N. Ahalwat and J.Mondal, J. Am. Chem. Soc. 140, 17743 (2018)

N. Ahalawat, S. Bandyopadhyay and J. Mondal, Manuscript in preparation.

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