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 about Zhang lab research.
Honors & Awards
DeLano Award for Computational Biosciences, ASBMB, 2020
Basic Science Research Award, University of Michigan Medical School, 2013
Alfred P. Sloan Award, 2008
CAREER Award, National Science Foundation, 2008
Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families.
Wang Y, Shi Q, Yang P, Zhang C, Mortuza SM, Xue Z, Ning K, Zhang Y.
Genome Biol. 2019; 20: 229.
Assembling multidomain protein structures through analogous global structural alignments.
Zhou X, Hu J, Zhang C, Zhang G, Zhang Y.
Proc Natl Acad Sci USA. 2019; 116: 15930-8.
LOMETS2: Improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins.
Zheng W, Zhang C, Wuyun Q, Pearce R, Li Y, Zhang Y.
Nucleic Acids Res. 2019; 47: W429-36.
EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function.
Pearce R, Huang X, Setiawan D, Zhang Y.
J Mol Biol. 2019; 431: 2467-76.
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; 431: 825-41.
DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions.
Quan L, Wu H, Lyu Q, Zhang Y.
J Mol Biol. 2019; 431: 2449-59.
DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism.
Bell EW, Zhang Y.
J Cheminform. 2019; 11: 40.
ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks.
Li Y, Hu J, Zhang C, Yu DJ, Zhang Y.
Bioinformatics. 2019; 35: 4647-55.
Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.
Wu J, Liu B, Chan WKB, Wu W, Pang T, Hu H, Yan S, Ke X, Zhang Y.
Bioinformatics. 2019; 35: i324-i332.
RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA.
Gong S, Zhang C, Zhang Y.
Bioinformatics. 2019; 35: 4459-61.
For a list of publications at Google Scholar, click HERE