Abstract:
Understanding the molecular mechanism of any complex biophysical or chemical processes requires tracking of the dynamics on the underlying (free) energy landscape. For all practical purposes, this requires projection from a higher dimensional landscape (3N for N particle system) onto a few “collective variables” or order parameters or reaction coordinates. The accuracy of the computed free energy landscape or kinetic parameters often strongly depends on this choice of collective variables to project on. In this talk, we shall discuss a few representative case studies to elucidate the complexity or difficulty of this choice. We shall discuss classic order parameters based on chemical or physical intuition along with machine learned (artificial neural network based) reaction coordinates for complex processes where intuition may fail. In addition, we shall showcase some enhanced sampling methods developed in our group to compute the kinetics of certain rare events using an order of magnitude less computational power.
Brief Biosketch:
Dr. Suman Chakrabarty has broad research interests in the area of computational (bio)physical chemistry. He obtained B.Sc. in Chemistry from Presidency College, Kolkata (2002) and MS (2005)/PhD (2010) from Indian Institute of Science (IISc), Bangalore. After a postdoctoral research stint at the University of Southern California, USA (2009-2012), he joined CSIR-NCL, Pune as a Ramanujan Fellow (2013-2017). He is presently an Associate Professor at the S. N. Bose National Centre for Basic Sciences, Kolkata. His group studies the structure, function, and dynamics of complex molecular systems, including large-scale biomolecular machinery, using theoretical and computational methods based on statistical mechanics and machine learning. His group has contributed towards understanding the molecular mechanism of allosteric regulation, biomolecular recognition/signalling, and development of enhanced sampling methods towards computing the underlying free energy landscape of rare events.