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地址：深圳市光明区光侨路高科创新中心
电话：+86-755-86967710
邮箱：webmaster@szbl.ac.cn
电话：+86-755-86967710
邮箱：webmaster@szbl.ac.cn
Yaoqi Zhou, Ph.D./
Institute/Center
Institute of Systems and Physical Biology
Email
zhouyq@szbl.ac.cn
Research Direction
Computational Biology,Molecular Biology ,Microbiology
Homepage Link
Timeline
Areas
Results
Honors
Media
Recruitment
Papers
Timeline
2021~Present
Shenzhen Bay Laboratory, Shenzhen, China
Senior Principal Investigator
2013~2021
Griffith University, QLD, Australia
Professor
2006~2013
Indiana University Purdue University at Indianapolis, Indiana, USA
Tenured Full Professor
2004~2006
State University of New York at Buffalo, USA
Tenured Associate Professor
2000~2004
State University of New York at Buffalo, USA
Assistant Professor
1995~2000
Harvard University, MA, USA
Research Associate
1994~1995
North Carolina State University, NC, USA
Postdoctoral Fellow
1994~1994
State University of New York at Stony Brook, NY, USA
Postdoctoral Fellow
1990~1993
Applied P & Ch Laboratory, CA, USA
Scientist and Director
Research Areas
The research group led by Dr. Zhou primarily focuses on fundamental studies exploring the relationships between sequences, structures, and functions of RNA and proteins, as well as applied research in design, delivery, and drug development of these biological macromolecules. A distinctive feature of the group is the integration of dry and wet lab approaches—combining computational structural bioinformatics, AI-powered deep learning with modern high-throughput and automated directed-evolution biotechnologies—to achieve profound insights into the sequence-structure-function relationships. This research enables the multi-faceted application of biological macromolecules, including targeted drug design and delivery for precision medicine, as well as detection of personalized biomarkers. Current specific projects include, but are not limited to:
1. Protein structure and function prediction in the post-AlphaFold era
2. Protein design and directed evolution
3. RNA structure prediction and RNA language models
4. Targeted RNA delivery systems
研究成果
Pioneered prediction of continuous backbone dihedral angles using shallow and deep learning, enabling end-to-end protein structure prediction (Fig. 1). This development directly underpinned Nobel-prize-winning technique AlphaFold 2 for high-accuracy structure prediction.
First AI-driven methods for protein sequence design, achieving 30–34% sequence recovery (Fig. 2). Recognized as the inception of machine learning in protein design ([Ovchinnikov and Huang, Curr. Opin. Chem. Biol. 2021]), this paradigm shift (away from energy-based approaches) now dominates the field, enabling revolutionary advances in therapeutic protein & industrial enzyme design.
RNA-BRiQ: Developed a novel statistical potential enabling atomic-level RNA refinement. Powered AIChemy-RNA2 to win #1 in CASP15 (2022) for RNA structure prediction.
(Fig. 1)
(Fig. 2)
Honors
Top 2% Scientist Worldwide in Stanford/Elsevier’s career and annual rankings
20,000+ citations & an H-index of 79 (Google Scholar)
#1 Performances in International Competitions: CASP6 Template-based protein structure prediction 2004, CAGI Blind prediction of cell proliferation rate 2013, CAID Prediction of protein unstructured regions 2019, CASP15 RNA structure prediction 2022
Editorial Board, Nucleic Acids Research
Recruitment
Papers
1. J. Singh, J. Hanson, K. Paliwal, and Y. Zhou, RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning,Nature Communications10, 5407 (2019).
2. Z. Zhang, P. Xiong, T. Zhang, J. Wang, J. Zhan, and Y. Zhou, Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity,Nucleic Acids Research, 48:1451-1465 (2020).
3. J. Zhan, H. Jia, E. A. Semchenko, Y. Bian, A. M. Zhou, Z. Li, Y. Yang, J. Wang, S. Sarkar, M. Totsika, H. Blanchard, F. E.-C. Jen, Q. Ye, T. Haselhorst, M. P. Jennings, K. L. Seib, and Y. Zhou, Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria; a new strategy to generate antimicrobial peptides,FASEB J., 33: 2095–2104 (2019).
4. S. Xu, J. Zhan, B. Man, S. Jiang, W. Yue, S. Gao, C. Guo, H. Liu, Z. Li, J. Wang, and Y. Zhou, Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor,Nature Communications8, 14902 (2017).
5. Z. Li, Y. Yang, J. Zhan, L. Dai and Y. Zhou, Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects,Ann. Rev. Biophysics42, 315-335 (2013).
Contact
Address: Gaoke Innovation Center,Guangming District, Shenzhen
Phone: +86-755-86967710
Email: webmaster@szbl.ac.cn
Postal Code: 518132
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