The Machine Learning Center and the School of Computational Science and Engineering invite you to a lecture by Jinbo Xu, a professor at Toyota Technological Institute at Chicago.
The lecture will be held at 2:00 on Tuesday, December 3 in Marcus Nanotechnology Room 1117-1118.
Title: Progress on Protein Structure Prediction by Deep Learning
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. As such, computational structure prediction is often resorted. Predicting the structure of a protein without similar experimental structures is very challenging and usually needs a large amount of computing power. This talk will present the deep learning method (i.e., deep convolutional residual neural network) we have invented for protein contact and distance prediction that won the CASP (Critical Assessment of Structure Prediction) in both 2016 and 2018 in the category of contact prediction. In this talk we show that by using this powerful deep learning technique, even with only a personal computer we can predict the structure of a protein much more accurately than ever before. In particular, we predicted correct folds for the 3 largest hard targets (~350 amino acids) in CASP13 (2018) and generated the best 3D models for two of them among all the human and server groups including DeepMind's AlphaFold. Inspired by our success in CASP12 in 2016, this deep learning technique has been adopted widely by the structure prediction community and thus, resulted in the widespread, largest progress in the history of CASP, which will also be discussed in this talk.
Bio: Dr. Jinbo Xu is a full professor at the Toyota Technological Institute at Chicago, a computer science research and educational institute located at the University of Chicago. Dr. Xu’s research lies in machine learning, optimization, and computational biology. He has developed several popular bioinformatics programs such as the CASP-winning RaptorX (http://raptorx.uchicago.edu) for protein structure prediction and IsoRank/HubRank for interaction network analysis. The deep learning method initiated by him for protein contact/distance prediction has been widely adopted by the community and resulted in the largest progress in the history of protein structure prediction, due to which he was invited to give a keynote talk at the 2019 3DSIG session of ISMB, the largest bioinformatics conference in the world. Dr. Xu is an Associate Editor of the Bioinformatics journal and has received many awards, including Alfred P. Sloan Research Fellowship, NSF CAREER award, RECOMB's test-of-time award (2019), RECOMB best paper award (2014) and PLoS Computational Biology Research Prize (2018). His work has also been reported by Science, The Economist, and other media.