Research
Self-assembled nanomaterials
We are interested in exploring different ways in which tunable self-assemblies of nanomaterials can be designed. We are interested to use the biological environment of a living cell i.e. the crowded environment as a tool to design programmable self-assemblies of functionalized gold nanoparticles and nanorods.
Machine learning methods in drug and nanomaterial design
We are interested to employ machine learning methods for predicting the ligand binding to proteins for drug discovery and design. One part of work is involved in generating ultra-large training dataset for such problems using molecular dynamics simulations. The other part of work is involved in developing ML models for accurate prediction of binding energies of these complexes. Also, interested in developing ML models for designing nanoparticle self-assemblies. This work is in collaboration with The Technology Innovation Hub for Data, IIIT-Hyderabad.
Molecular biosensors
We explore the effects and factors that affect the self-assembly of cyanine dyestuffs that are known to aggregate and flouresce in presence of crowded environment. These self-assembled fibrils are similar to those formed by intrinsically disordered proteins. We are interested to understand the molecular mechanisms underlying the effects of the crowded environment that can help in designing these aggregates that finds applications in designing biosensors.
Biomolecular self-assemblies
We are interested in exploring the role of crowded and confined environment on the self-assembly of biomolecules and biopolymers. We are interested in understanding how a living cell environment- crowded with large macromolecules- affects the fibrillation of amyloid-forming intrinsically disordered proteins.
Smart responsive polymers
We examine the role of cosolvent and crowded environment on the conformational preferences of stimuli (thermo)-responsive polymers using molecular dynamics simulations. The preference of such polymers to be in either extended or collapsed state finds interesting applications in designing smart materials. We employ different solvation theories to understand the thermodynamic driving force underlying such stimuli effects.
Developing models for accurate representation of crowded environment
Currently available force-field models of macromolecules like proteins, polymers over-represent the attractive intermolecular interactions between multiple chains of macromolecules in a solution. This leads to aggregation of (macro)molecules at high concentrations. We are interested to improve the parameterization of the force-fields for accurate moelcular modeling of the crowded environment in simulations.
Active particles
We are interested in modeling and designing responsive porous nanoparticles for triggered release of guest molecules. Using molecular dynamics simulations, we are interested to explore the role of chemical environment in determining the sealing of pores functionalized by stimuli-responsive polymers.
Intracellular water
Water constitutes a large proportion of a living cell. However, water inside a living cell shows much different behaviour in terms of solvation, diffusion and viscosity as compared to the water in a dilute solution. We explore the role of water as a solvent under the confined and crowded conditions inside a living cell- with the aim to explore the interesting thermodynamic and kinetic properties that make water a biological solvent, essential for the existence of life.