Deep Learning Prediction of Protein Structure and Interaction

About this Class

Abstract: Deep learning is revolutionizing the prediction of protein tertiary structure and is close to solve this grand challenge hanging over the scientific world for many years. In this talk, I will describe how this technology emerged in the field, how it overcame various technical hurdles to reach a high accuracy of predicting protein contacts/distances and tertiary structures, and where it is going now. I will use the development of our MULTICOM protein structure prediction system ranked among top methods in the last two rounds of CASP protein folding competition as well as several other state-of-the-art methods as examples to illustrate the process. Moreover, I will present our latest development of deep learning methods to tackle another grand scientific challenge – prediction of protein-protein interactions and quaternary structures of protein complexes, for which revolutionary deep learning technologies will likely emerge in the next few years as what had happened in the field of protein tertiary structure prediction. Bio: Dr. Jianlin Cheng is the Thompson Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri - Columbia, USA. He earned his PhD in computer science from the University of California, Irvine in 2006. His research is focused on bioinformatics and machine learning. Dr. Cheng has authored or co-authored 157 journal articles (https://scholar.google.com/citations?user=t9MY6lwAAAAJ&hl=en&oi=ao), which have been cited 13,000 times and have an h-index of 51. His protein structure prediction method – MULTICOM – was consistently ranked among the top methods in the last seven rounds of Critical Assessments of Structure Prediction (CASP8-14) from 2008 to 2020. His research has been supported by the National Institutes of Health (NIH), National Science Foundation (NSF) and Department of Energy (DoE). Dr. Cheng was a recipient of a 2012 NSF CAREER award. Join Zoom Meeting https://umd.zoom.us/j/97941931766 Meeting ID: 979 4193 1766 One tap mobile +13017158592,,97941931766# US (Washington DC) +13126266799,,97941931766# US (Chicago)