Molecular mechanics is one aspect of molecular modelling, as it involves the use of classical mechanics (Newtonian mechanics) to describe the physical basis behind the models. Molecular models typically describe atoms (nucleus and electrons collectively) as point charges with an associated mass. The interactions between neighbouring atoms are described by spring-like interactions (representing chemical bonds) and Van der Waals forces.
The Lennard-Jones potential is commonly used to describe the latter. The electrostatic interactions are computed based on Coulomb’s law. Atoms are assigned coordinates in Cartesian space or in internal coordinates, and can also be assigned velocities in dynamical simulations. The atomic velocities are related to the temperature of the system, a macroscopic quantity. The collective mathematical expression is termed a potential function and is related to the system internal energy (U), a thermodynamic quantity equal to the sum of potential and kinetic energies. Methods which minimize the potential energy are termed energy minimization methods (e.g., steepest descent and conjugate gradient), while methods that model the behaviour of the system with propagation of time are termed molecular dynamics.
Professor Ranabir Das
Microbes hijack the host machinery to their own advantage. During infection, several host defense mechanisms are destabilized by the microbes using the ubiquitin-proteasome pathway. We study the interactions between microbial factors and host proteins to understand this process.
Dr. Shruthi Viswanath
We are a new interdisciplinary research group at IITCC in the area of computational structural biology. Broadly, we seek to understand details of biological processes by characterizing the structures of proteins in binary complexes, macromolecular assemblies, and nanoscale architectures. These structures hint at basic architectural design principles in biology, and are key to understanding how molecular machines function, how they evolved, how they assemble, and how they are regulated.
Our main focus is in developing rigorous methods (and software) for computational modeling of protein organization at the above scales.
These methods make structure determination more accurate and efficient by improving upon approaches that are ¬ad hoc, semi-automated, based on trial-and-error, and/or require manual expert intervention.
For this purpose, we are inspired by algorithms from computational statistics, statistical physics, machine learning, computer vision, and graph theory.
Dr. Sabarinathan Radhakrishnan
The broad research interest of our group is the application of computational and functional genomics approaches to understand the genetic and molecular basis of human genetic diseases (especially cancer). Briefly, cancer is a genetic disease caused by the accumulation of genetic alterations (mutations), which can be either acquired (somatic) or inherited (germline), in the genes that usually control cell growth and division. Somatic mutations are mostly acquired through different mutational processes (which includes exposure to endogenous or exogenous factors, DNA replication errors or inefficient DNA repair) acting throughout the lifetime of an individual. However, only a few of these somatic mutations (referred to as drivers) are capable of conferring a selective growth advantage to somatic cells, and thus establish a tumor. In order to treat and prevent cancer, it is paramount to understand what are the different mutational processes operative in somatic cells and to identify which mutations are cancer drivers (their potency and mechanisms of action). We are interested in addressing these questions through the analysis of large-scale cancer genomics datasets (whole genome/exome and transcriptome) generated by the national and international cancer genome projects, and with a particular focus on Indian population by sequencing and analysis of tumors from cancer patients.
