Faculty & Research

Education

Post-doctorate, Computational Biophysics, Harvard University, Boston, MA (1995-2000)
Post-doctorate, Chemical Engineering, North Carolina State University, Raleigh, NC (1994-1995)
Ph.D. Chemical Physics, State University of New York, Stony Brook (1990)
B.S. Chemical Physics, University of Science and Technology of China, Anhui, China (1984)

Research Interests

Our group's research focuses on developing and utilizing statistical mechanics theories, bioinformatic, and simulation methods to explain and predict the behavior of biologically interesting macromolecules.

The current research program aimed at the understanding of the molecular mechanisms and bioinformatic studies of protein folding, stability, and binding of ligands, peptides, and DNA. The long-term goal of the proposed research is to elucidate the relations between the sequence, structure, and function of proteins and to uncover the molecular mechanisms underlying various diseases. This will be accomplished by designing simple (yet realistic) models and by developing statistical mechanics theories and bioinformatic tools.

Trained as a theoretical physicist in a chemistry department, Dr. Zhou's research area moved to chemical engineering and computational biophysics during postdoctoral studies, and to bioinformatics when he became an independent researcher as an assistant professor at State University of New York at Buffalo in 2000.

His multidisciplinary training allows him to approach bioinformatic problems from the angle of physics. A recent example is the development of a knowledge-based energy function (called DFIRE) for proteins using the principle of physics rather than pure statistical information of protein structures. His group developed many freely available bioinformatic tools including SPARKS and SP3 for fold recognition and structure prediction, SPEM for multiple sequence alignment, SPINE for secondary and accessible surface area prediction, PINUP for binding-site prediction, MC2 for module identification from network of protein-protein interactions, and THUMBUP for topology prediction of transmembrane helical proteins.