Research

In literature, several techniques are available to enhance the molecular dynamics simulations. Metadynamics (MTD) is one such technique but due to its self-guiding property, the method has been successful in studying complex reactions and conformational changes. In MTD sampling bias potential is used to fill the free energy basins and thus for cases with flat, broad, and unbound free energy wells, the computational time to sample them becomes very large. To overcome this problem, we proposed a method called Well Sliced MTD, which combines the standard Umbrella Sampling (US) technique with MTD to sample orthogonal collective variables (CVs) in a simultaneous manner. This approach is shown successful for controlled sampling of a CV in a MTD simulation, making it computationally efficient in sampling flat, broad, and unbound free energy surfaces. In order to handle large numebr of CVs, this approach was further improved by integrating Well-Sliced MTD with temperature accelerated MD scheme. This approach was named as Temperature Accelerated Sliced Sampling (TASS) allows one to sample large number of orthogonal collective coordinates simultaneously.

Developing a new drug is very expensive in terms of money as well as time. Efforts aimed at reducing the cost and time to drug approval are of paramount importance. In addition to the traditional drug development methods, new approaches based on molecular dynamics can help to reduce both the cost and time it takes to develop and evaluate new therapies. Molecular dynamics simulations are capable of resolving molecular recognition processes and catalytic mechanism with chemical accuracy, but their practical application is often limited to the time-scale accessible to a single simulation, which is far below biological timescales. Thus the use of enhanced sampling techniques is unavoidable. We used the recently developed enhanced sampling techniques (TASS) to study the drug binding for β-lactamases. To study the catalytic mechanism of drug hydrolysis at the molecular level for β-lactamases, we used enhanced sampling techniques in hybrid QM/MM framework.

RNA folding into stable tertiary structures depends upon the concentrations and types of cations present. An understanding of the physical basis of ion-RNA interactions is therefore of paramount importance. Among ions, Magnesium ion is particularly interesting because of its high charge and small radius. Magnesium ions can be both directly and indirectly bound to RNA. Over the past few decades several computational as well as experimental efforts have been made to study the Magnesium-RNA interaction. Due to the high energetic barriers involved in Magnesium-RNA and Magnesium-water interaction, enhanced sampling methods coupled with classical force-field based molecular dynamics simulations are desired.

In recent years, due to increase in global energy demand, developing energy storage systems with higher energy densities are an urgent need. Rechargeable Li-ion batteries (LIBs) emerge as an important power sources for energy storage device for portable electronics and are being continuously investigated and developed for use in automobile electric vehicles (EV). However, during the cycling of LIBs, due to the decomposition of electrolyte, a passivation layer called solid electrolyte interphase (SEI) is formed on electrode surface. Although, SEI formation is vital in LIBs for long term performance, it leads to irreversible loss of lithium and thus, storage capacity of battery. While several studies have been performed in the past few decades on SEI, it is difficult to control its formation and growth, as the chemical composition, and stability of SEI depend on a list of factors. These factors include the electrode material, electrolyte composition, electrochemical conditions, and cell temperature. The perfect SEI would be a fast forming, flexible, stable, and contains insoluble species with low electronic and high ionic conductivity. Thus, the formation of SEI and the electrochemical stability of SEI components should be a primary topic of investigation in future development of LIBs. I am interested in investigating the SEI layer using atomistic simulations in combination with enhanced sampling techniques.

Antibiotics are the drugs which are used to treat bacterial infections. Resistance to antibiotics is now a real and worrying threat, as bacteria mutates to become immune to its effect. Due to the increased bacterial resistance and the high cost of treatment, traditional antibiotic drugs are becoming less popular and less effective in the treatment of bacterial infections. Thus, there is an urgent need to discover new materials which can be used as antibiotics. In past few years, 2D nanomaterials have shown great potential in antibacterial applications which is due to their unique surface physical and chemical properties, and it has been reported that a variety of 2D nanomaterials exhibit excellent antibacterial activities. To understand the antimicrobial process and mechanical insight, the interaction of 2D nanomaterials and bacterial membranes has attracted a lot of attention. I am interested in studying the effect of various nanomaterials on the bacterial membrane using all atom simulation and computing the free energy associated with the process.