Department of Computer Science

Computational Structural Biology Laboratory

Research interests

With an interdisciplinary focus, research in our computational structural biology laboratory involves knowledge from multiple disciplines, such as physics, robotics, computer science, and biology. The long-term goal of our research is to understand the functional mechanisms of proteins, and to identify the roles of protein structure and dynamics in the realization of protein function. One of the primary ways in which this goal is being pursued is by developing novel computational methods that are inspired and transferred from research results in other disciplines, such as robotics.


Research projects

Efficient Mapping of Ligand Migration Channel Networks in Dynamic Proteins

In this project, we develop a novel robotic motion planning inspired approach that can map the complete ligand migration channel network in a dynamic protein. The method combines an efficient spatial mapping of protein inner space with a temporal exploration of protein structural heterogeneity, which is represented by a structure ensemble. Efficient geometric mapping method is applied to each conformation in the ensemble to produce a partial map of protein inner cavities and their inter-connectivity. These maps are then merged to form a super map that contains all the channels that open dynamically.

Determine the Populations of Protein Conformation States Using Experimental Residual Dipolar Coupling Data

In this project, we develop a novel computational method that can derive the relative populations of the structures within an ensemble using the Residual Dipolar Coupling (RDC) data. Our results demonstrate that adding relative populations to the ensemble helps significantly improve the accuracy in reproducing experimental RDC data.

Motion Correlation Network for protein allostery

Allosteric regulation can be described as the binding of an effector at one site switches the functionality of another site, often at distance. Although a wide variety of models have been proposed, the underlying mechanism of the allosteric communication remains unclear. In this work, we hypothesize that the allosteric communication between the allosteric site and catalytic site should be carried out along pathways of residues that have strongly correlated motions, so that information such as conformation change can be quickly transduced from one site to another.

Spring Tensor Model for protein dynamics

In this project, we develop a generalized spring tensor model (STeM) that is able to bridge this gap. Based a physically more accurate potential, STeM is able to singly predict well both mean-square fluctuations and conformation changes. STeM also reveals the importance of three-body and four-body interactions in properly modeling protein dynamics.