We are computational scientists who focus on chemical biology, the interactions between small molecules and biological macromolecules. We develop and apply new methods that may be helpful for structure-based drug design.
Much, but not all, of our effort is based on implicit ligand theory (ILT), a theoretical framework for binding free energies which David derived in 2012. Most binding free energy calculations involve computationally expensive molecular simulations of flexible binding partners. David showed that, in theory, equally good results may be achieved by computing free energies between flexible ligands and multiple rigid receptor configurations.
Some of our achievements are described below.
Although it is well-known that ligands are polarized by proteins, the magnitude of this effect had not been quantified in many systems. We evaluated the ligand polarization energy for several hundred protein-ligand complexes and showed that it is a large and highly variable component of the binding energy.
The binding potential of mean force (BPMF), the binding free energy between a flexible ligand and a rigid receptor, is a critical ingredient for esimating binding free energies with implicit ligand theory. We have written software to precisely estimate this quantity and tested it on a diverse set of 85 protein-ligand complexes.
Although there are many ways to select representative snapshots from a molecular dynamics simulation to perform molecular docking, it has been unclear how to assess these methods. We pointed out that this procedure is an example of a statistical method, stratified sampling, and that the efficiency of stratification can be used to assess ensemble reduction methods.
We have identified the ubiquinone binding site in the bacterial ion pump NQR. This binding site is not obvious from the crystal structure. It is a possible target for structure-based drug design.
See article in IIT Today.
We have shown how to use constrained molecular dynamics, such as torsional dynamics, as a Monte Carlo move for molecular simulation. Previously, molecular simulations based on constrained dynamics would not sample from the appropriate distribution or not sample the entirely of configuration space.
See article in IIT Today.
This website contains information about: our research projects in fast binding free energy calculations, enhanced sampling methods, Bayesian analysis of binding experiments, and modeling metabolic enzymes from pathogenic bacteria; a complete list of our publications from the lab and David’s prior work; links to source code and data related to publications and classes; links to recommended software; a nascent scientific blog; and finally information about our members, alumni, and visiting or joining the lab.
Our research has been supported by
Welcome to Jennifier and Sivanujan, our new Ph.D. students!August 20, 2021
Congratulations, Ella, for being awarded the 2nd best chemistry poster at the Biology, Chemistry, Food Science and Nutrition, and Physics Departments Poster Day.July 2, 2021
We organized an international workshop on modeling biological macromolecules.June 15, 2021
Welcome to Jaycee, Joseph, Sophie, and Ella, our new Ph.D. students!July 16, 2020
Oscar Juarez, Karina Tuz, and David file a patent for CROWNase, a potential COVID-19 treatment. The technology transfer office has made a nonconfidential summary available.June 1, 2020
Congratulations, Jim, on passing your Ph.D. qualifying exam!March 1, 2020
Congratulations, Soohaeng, on your promotion to Research Assistant Professor!December 29, 2019
Congratulations, Bing, on your postdoctoral position at the NIH!September 11, 2019