Research

The broader goal of my research is to study how proteins behave in different environments. I have developed computational tools to predict the properties of membrane proteins that are present in the cellular membrane, proteins in the presence of polymers, or soluble proteins. Effectively I will ask which methods can be used to study a type of protein and validate their performance through experimental collaborations or previous experimental evidence. Some of the focus areas are as follows: 

Predict the molecular characteristics of proteins and polymers for the desired phase behavior: Protein and charged polymer complexes are used for stabilizing enzymes in biosensors, drug delivery, and the most prominent being that of chromosomes (DNA and Histones). The goal of this project is to predict the properties of proteins and polymers required to get the desired phase behavior of protein+polymer complex using a combination of first principles and deep-learning-based frameworks.  

Membrane Associated Protein Docking: The oligomerization of protein macromolecules on cell surfaces plays a fundamental role in regulating cellular function, including signal transduction and the immune response. Despite their importance, MPs represent only 2% of all protein structures in the protein data bank (PDB), and MP complexes are even scarce. Thus, the objective of this project is to develop robust, accurate, and efficient computational tools for modeling MP interfaces and predicting complex structures.

Structure prediction and design of membrane proteins: Membrane proteins participate in many life processes. They constitute 30% of the human proteome and are targets for over 60% of pharmaceuticals. Accurate and accessible computational tools to design membrane proteins will transform the platform to engineer membrane proteins for therapeutic, sensor, and separation processes. While soluble protein design has advanced, membrane protein design remains challenging due to the difficulties in modeling the lipid bilayer. The objective of this project is to develop first principle and machine-learning-based models to accurately capture the membrane features and thereby its interaction with proteins.