Office: 303 Baum, 5B- 5

Email: xibing.he@pitt.edu

Dr. Xibing He obtained his B.S. and M.S. of Chemistry from Peking University (China), and his Ph.D. of Chemistry from University of Pennsylvania. Then he worked as a postdoctoral researcher in University of Maryland (Baltimore), Temple University and Florida State University before he joined School of Pharmacy in University of Pittsburgh.

Dr. He has been trained in physical/computational chemistry and computational biophysics. He has extensive expertise of conducting molecular dynamics (MD) simulations and free energy calculations of biological systems and soft-matter systems, and developing various molecular mechanics force fields (MMFFs) including coarse-grained models, additive atomistic force fields and polarizable atomistic force fields. He also has substantial expertise in computer-aided drug design (CADD) using receptor-based and ligand-based methods.

Research Interest:

1. Development of molecular mechanics force fields.
A key to the success of molecular simulation studies and structure-based rational drug designs is the quality of molecular mechanics force fields (MMFFs). A force field (FF) is composed of a potential energy function and its associated parameters for various interactions in molecules: bonds, angles, dihedrals, van der Waals, electrostatic interactions, etc. Current popular FFs for biological systems include AMBER, CHARMM, OPLS, GROMOS, etc. For research areas of materials, soft-matter systems, other FFs (including coarse-grained models) may be more popular.

Dr. He has worked on the development of coarse-grained models, additive and polarizable CHARMM force fields. Currently He is focusing on the continue development of the general AMBER force field (GAFF) with Professor Junmei Wang, including: (a) expanding the chemical space coverage and improving the parameters of the second-generation of GAFF (GAFF2); (b) developing a new generation of general force field (GAFF3) which is based on new charge models; (c) evaluating GAFF2/GAFF3 and alternative general force fields by studying biomolecule-ligand interactions.

As an active FF developer, Dr. He assists local and external researchers on their projects using AMBER/GAFF, CHARMM/CGenFF and other force fields. In 2015, Dr. He converted the newest AMBER force field of proteins and nucleic acids (ff14SB) from the AMBER format to CHARMM format so that users can adopt AMBER ff14SB force field with the CHARMM simulation package. The converted RTF and PAR files can be found as "/toppar/non_charmm/parm14sb_all.rtf" and "/toppar/non_charmm/parm14sb_all.prm" under the CHARMM directory path in all official versions of the CHARMM package since 2016.

2. Development of methods and protocols for predicting biomolecule-ligand binding affinities.
A chief application of molecular simulations is to elucidate the molecular interactions between small molecule ligands and protein or nucleic acid targets, and then enhance or eradicate the functions of their targets via rational design of high-potent agonists or antagonists. However, nowadays it is still very challenging to accurately predict the free energies of such interactions and processes. Besides improving the quality of used force fields, various methods have been and are being developed to enhance the prediction of the receptor-ligand binding affinities, with different balances between accuracy and efficiency.

Dr. He has developed a new end-point free energy calculation method - Extended Linear Interaction Energy (ELIE). This new method is in the intermediate position in terms of efficiency and accuracy between the empirical docking & scoring methods and the alchemical free energy methods. Dr. He applied the ELIE in the 3rd Grand Challenge in 2017 held by Drug Design Data Resource (D3R), an NIH founded organization, and achieved relatively well performance compared to other participants.

Dr. He is also working on new protocols to facilitate the routine usage of alchemical free energy methods such as Thermodynamic Integration (TI), Free Energy Perturbations, etc., which are rigorous but computationally demanding to run and difficult to setup.

3. Computer-aided drug design/discovery.
Dr. He is also working with researchers both internal and external to the Pharmacy School regarding projects of computer-aided drug design/discovery (CADD), including large-scale virtual screening, lead identification, lead optimization of potential inhibitors for protein targets, using various methods such as quantitative structure–activity relationship (QSAR) models, docking & scoring methods, end-point methods and alchemical free energy calculations, etc.