Areas of Interest
The goal of the Zhang lab is to understand the fundamental relations between protein sequence, structure and function. The major focus of the lab is to develop new bioinformatics algorithms to predict 3-dimensional protein structure from the amino acid sequence and deduce the biological function of proteins by comparing the predicted structures with the function databases.
A number of computational methods developed by the Zhang lab have been demonstrated in the CASP experiments to be the world's best for protein structure prediction and function annotation. The lab is currently working on extending the developed protein modeling algorithms for protein design and structure-based drug discovery. They are especially interested in modeling G protein-coupled receptors and the interactions with the associated ligands with the purpose of developing new drugs to regulate these interactions ( Read more on Zhang Lab research).
Honors & Awards
2013 Dean's Basic Science Research Award
2008 Alfred P. Sloan Award
2008 NSF Career Award
Changing the Apoptosis Pathway through Evolutionary Protein Design.
Shultis D, Mitra P, Huang X, Johnson J, Khattak NA, Gray F, Piper C, Czajka J, Hansen L, Wan B, Chinnaswamy K, Liu L, Wang M, Pan J, Stuckey J, Cierpicki T, Borchers CH, Wang S, Lei M, Zhang Y.
J Mol Biol. 2019 Jan 6.[Epub ahead of print]
COACH-D: improved protein-ligand binding sites prediction with refined ligand-binding poses through molecular docking.
Wu Q, Peng Z, Zhang Y, Yang J.
Nucleic Acids Res. 2018; 46(W1): W438-W442.
mTM-align: a server for fast protein structure database search and multiple protein structure alignment.
Dong R, Pan S, Peng Z, Zhang Y, Yang J.
Nucleic Acids Res. 2018; 46(W1): W380-W386.
Recent advances in automated protein design and its future challenges.
Setiawan D, Brender J, Zhang Y.
Expert Opin Drug Discov. 2018; 13: 587-604.
MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.
Zhang C, Zheng W, Freddolino PL, Zhang Y.
J Mol Biol. 2018; 430: 2256-65.
LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening.
Hu J, Liu Z, Yu DJ, Zhang Y.
Bioinformatics. 2018; 34: 2209-18.
For a list of publications, click HERE