COM2-04-14
660 11241

https://zhanggroup.org

ZHANG Yang

Professor
Department Of Computer Science, School of Computing
Cancer Science Institute of Singapore
Department of Biochemistry, Yong Loo Lin School of Medicine


Dr Yang Zhang is a Professor in the Department of Computer Science, the School of Computing, National University of Singapore (NUS). He also serves as a Professor in the Cancer Science Institute of Singapore, and the Department of Biochemistry at the Yong Loo Lin School of Medicine, NUS. Prior to joining NUS, Dr Yang Zhang worked as a Professor in the Department of Computational Medicine & Bioinformatics, the Department of Biological Chemistry, and the Department of Macromolecular Science & Engineering, University of Michigan. The research interests of the Zhang Lab are in artificial intelligence and deep neural network learning, protein folding and structure prediction, and protein design and engineering. The I-TASSER algorithm (https://zhanggroup.org/I-TASSER/) developed in his laboratory was ranked as the No 1 most accurate method for automated protein structure prediction in the community-wide CASP experiments nine times in a row since 2006. The honour that Dr Zhang received includes the Alfred P Sloan Award, the US National Science Foundation Career Award, and the University of Michigan Basic Science Research Award. He was selected as the Thomson Reuters/Clarivate Analytics Highly Cited Researcher for six times since 2015.

RESEARCH AREAS

Computational Biology
  • Bioinformatics Algorithms
Artificial Intelligence
  • Machine Learning

RESEARCH INTERESTS

  • Protein folding and structure prediction

  • Protein design

  • AI-based drug discovery

RESEARCH PROJECTS

Protein folding and protein structure prediction

Determining structure and function of proteins is a cornerstone of modern biology and medicine. We are developing novel computational methods, built on cutting-edge AI technique and physics-based force field, to accurately model the structure and function of proteins. One goal of the study is to reveal the fundamental relationship between sequence, structure, and function of proteins.


AI-based protein design and drug discovery

Proteins in nature were generated following billions of years of evolution and therefore possess limited structural folds and biological functions. This project aims to design new protein sequences with novel structure and function beyond nature proteins. The computationally designed proteins and peptides can be used as drugs to treat various human diseases such as cancer and Alzhelmer's disease.


RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

  • (A full list of Dr. Yang Zhang's publications can be seen at https://scholar.google.com/citations?user=MtBs-kMAAAAJ)
  • R Pearce, X Huang, GS Omenn, Y Zhang. De novo protein fold design through sequence-independent fragment assembly simulations. PNAS, 120: e2208275120 (2023).
  • C Zhang, M Shine, AM Pyle, Y Zhang. US-align: Universal structure alignment of proteins, nucleic acids and macromolecular complexes. Nature Methods, 19: 1109-1115 (2022).
  • X Zhou, W Zheng, Y Li, R Pearce, C Zhang, EW Bell, G Zhang, Y Zhang. I-TASSER-MTD: A deep-learning based platform for multi-domain protein structure and function prediction. Nature Protocols, 17: 2326-2353 (2022).
  • X Zhou, Y Li, C Zhang, W Zheng, G Zhang, Y Zhang. Progressive assembly of multi-domain protein structures from cryo-EM density maps. Nature Computational Science, 2: 265-275 (2022).
  • X Zhang, B Zhang, PL Freddolino, Y Zhang. CR-I-TASSER: Assemble protein structures from cryo-EM density maps using deep convolutional neural networks. Nature Methods, 19: 195-204 (2022).
  • SM Mortuz, W Zheng, C Zhang, Y Li, R Pearce, Y Zhang. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions. Nature Communications, 12: 5011 (2021).
  • P Yang, W Zheng, K Ning, Y Zhang. Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction. PNAS, 118: e2110828118 (2021).
  • W Zheng, C Zhang, Y Li, R Pearce, EW Bell, Y Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021).
  • Y Wang, Q Shi, P Yang, C Zhang, SM Mortuza, Z Xue, K Ning, Y Zhang. Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families. Genome Biology, 20: 229 (2019).
  • X Zhou, J Hu, C Zhang, G Zhang, Y Zhang. Assembling multidomain protein structures through analogous global structural alignments. PNAS, 116: 15930-15938 (2019).

AWARDS & HONOURS

MODULES TAUGHT