We are primarily a computational chemistry group. Our research program centers on developing and applying computational chemistry methods to build and simulate models of crowded cell environments. Currently, we are applying our models to study protein misfolding and aggregation pathways, enzyme dynamics and mechanisms, and ensembles of intrinsically disordered proteins. Using our "cells-on-computers", we aim to address the current challenges in characterizing biomolecular "hidden states" that arise from limitations of spatial-temporal resolutions of traditional experiments and in silico methods.

Life is sustained by the actions of various biomolecules along key metabolic and cellular pathways in our cells. The conformers and interactions of these biomolecules can be challenging to study, due to their transient natures and rapidly changing dynamics, often affected by various intermolecular forces within the crowded cell environments. This leads to the phenomenon of biomolecular "hidden states" – states not revealed by traditional experiments and in silico methods.  These "hidden states" include those of mis-folded proteins, and ensembles of enzyme conformers.

Major focuses of ongoing projects in our group include mechanisms of red blood cell diseases, and metabolic pathways in cancer cells. We are constructing platforms to simulate structural mechanisms of cell-specific diseases, including disorders of red blood cells, such as sickle-cell anemia. We are also deconstructing how glycolytic enzymes communicate in cancer cells by forming islands of transient multi-enzyme complexes known as metabolons.

Characterizing biophysical properties, especially at the atomic level, of these rapidly evolving biomolecular states can lead to health advances. Our program’s long-term focus is to develop effective platforms for both investigating structural mechanisms of cell-specific human diseases and developing targeted drugs.