I completed my B.Sc. in Chemistry from University of Delhi, India and M.Sc. in Chemistry from Indian Institute of Technology, India. During this time, I performed undergraduate research under the guidance of Prof. Charusita Chakravarty where I performed atomistic simulation to study the influence of small peptides on structure and entropy of water. I received prestigious Summer Research Fellowship from the Indian Academy of Sciences for this work.
I moved to the United States in 2009 and joined The Ohio State University as a graduate student, where I worked on developing a novel solid-state NMR spectroscopic technique to identify cis-peptides in proteins. A key motivation for this work was that most algorithms for protein structure determination has inherent bias against cis-peptides. This work got me interested in the computational methods of structure determination.
I moved to West Virginia University in 2014 and specialized the molecular dynamics (MD) simulation, one of the most accurate methods of studying protein structure. My research at WVU was focused on a pH-sensitive cell-penetrating peptide, pHLIP, which is uniquely suited to become the next generation of cancer diagnostic and theraputic agent. I extensively used high-performance computing and performed analysis of large and complex data (mostly through scripting in Python, bash shell and Tcl) for this work. I completed my PhD under the guidance of Prof. Blake Mertz in 2018.
In August 2018, I started working as a postdoctoral scholar at the Center of Applied Structural Discovery (CASD) at the Biodesign Institute of Arizona State University. In my current role, I am employing large scale atomistic simulation to study respiratory complexes (like complex I and ATP synthase) that converts chemical energy into mechanical energy with nearly 100% efficiency. Along with the exciting science, this study paves the way for desiging bio-inspired energy devices.
Additionally, I am involved with development of new technology for de novo protein folding from cryo EM data. We use MD simulation to refine the medium-to-low resolution cryo EM images. The biggest advantage of our method is that it completely obviates the use of any initial (homology) model. Thus, unavailability of a homology model and/or possible poor choice of initial model are no longer a concern for structure refinement with this protocol.
Efficient harvesting of solar energy will have far-reaching impact towards current energy needs. This makes bioenergetics the focus of modern energy research. Particularly, mitochondrial bioenergetics is receiving more importance than ever before. Mitochondria are the power-plant of most eukaryotic cells which employ electrons from NADH to synthesize ATP. The long-range goal of my research is to understand the mechanism of action of the respirasome consisting of membrane-embedded enzymes, named Complex I, III, IV and V. Recent discovery of the atomic model of Complex I, the first and the largest protein complex in the mitochondrial respiratory chain, has provided a fantastic opportunity for such study. My current focus is on the chemomechanical coupling in Complex I, where electrons from NADH are utilized to reduce quinone while pumping protons across the membrane.
Complex I is an L-shaped protein where one of the arms is soluble and the other is transmembrane. The transmembrane domain is responsible for pumping protons across the membrane. The soluble domain houses the binding site for NADH as well as quinone. However, these two sites are far from each other. This raises the obvious question, how does the electrons from NADH reach the quinone? Other related questions are, how is the quinone traffick and the quinone-binding site influenced by presence of electrons (from NADH)? Many of the answers are tied to chemomechanical coupling: mechanical motion of protein domains arising out of chemical phenomenon (electron uptake). I am using large-scale MD simulation to understand the reduction-induced chemomechanical coupling in the soluble domain of Complex I.
Multiple approaches exist for hybrid modeling, which combine MD simulation with experimental methods like NMR, X-ray crystallography and cryo EM data. Each approach uses molecular replacement, where an initial search model is fitted to the experimental data. This has two consequences:
To overcome these problems, we are developing a a protein modeling pipeline that interactively combines a minimam spanning tree based backbone tracing tool (MAINMAST), a bayesian-likelihood based protein-folding method (MELD), and a molecular dynamics fitting protocol (ReMDFF). Primary sequence information and cryo EM images are the only input required, and images with resolution of as low as 5 angstrom are sufficient to get a structural model.
To make it easier to use, I am developing a GUI for this pipeline. I am using python's Kivy framework as well as kivy language for this purpose. Once ready, the protocol is expected to significantly push the boundaries of de novo protein folding.