Speaker: Xu Jinbo
Host: Xie Xiaoliang
Time: 14:00-15:30, October 27, 2021 (GMT+8)
Theme: Artificial Intelligence and Protein Folding
Venue:
Offline: Room B101, Second Gymnasium, Yenching Academy, Peking University
Online: PKU LiveClass Platform
Please scan the QR to join the lecture.
Abstract
Accurate description of protein structure and function is a fundamental step towards understanding biological life and highly relevant in the development of therapeutics. Although greatly improved, experimental protein structure determination is still low-throughput and costly, especially for membrane proteins. Computational methods have the potential to solve protein structures faster than wet-lab techniques, but computationally predicting the structure of a protein from its amino acid sequence is very challenging and usually needs a large amount of computing power. This talk will present modern artificial intelligence (AI) methods (i.e., deep convolutional residual neural network and Transformers) that have revolutionized protein structure prediction, showing that even with only a personal computer AI may predict the structure of a protein much more accurately than ever before.
Biography
Jinbo Xu received his BS in Computer Science from the University of Science and Technology of China in 1996, his MSc from the Chinese Academy of Sciences in 1999, and his PhD from the University of Waterloo in 2003. He spent the following year as a research assistant professor at the University of Waterloo, and one year as a postdoctoral fellow in the Departments of Mathematics and Computer Science and the AI Laboratory at the Massachusetts Institute of Technology. He is now an Associate Professor at the Toyota Technological Institute at Chicago. Xu's primary research interest is computational biology and bioinformatics including homology search, protein structure prediction, and protein interaction prediction. He has developed several protein structure prediction tools, such as RAPTOR, ACE, and SCATD.
Source: PKU New (Chinese)
