10/21 – Jinbo Xu, Toyota Technological Institute at Chicago

Speaker: Jinbo Xu

Toyota Technological Institute at Chicago

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.