CBRC Faculty Seminar by Xin Gao | Predicting Binding Preference of RNA Constituents on Protein Surface

Feb 23 2020 12:00 PM - Feb 23 2020 02:00 PM


Protein-RNA interaction is ubiquitous in cells and serves as the main mechanism for post-transcriptional regulation. Base-dominant interaction and backbone-dominant interaction categorize the two main modes of the way RNA interacts with proteins. Despite the advances in experimental technologies and computational methods to capture protein-RNA interactions, estimating binding preference of RNA backbone constituents and different bases on any location of a given protein surface is beyond the capacity of existing techniques.

In this talk, I will introduce our recent work on developing a deep learning model, NucleicNet, to predict these attributes from the local physicochemical characteristics of the protein structure surface. NucleicNet is able to predict the preference of non-site, phosphate, ribose, and four different bases on any location of any given protein structure. For known RNA binding proteins (RBPs), NucleicNet can help design the binding RNA. For proteins with unknown RNA binding function, NucleicNet can be used to predict if the protein is an RBP, and if so, where the binding pocket is and what RNA is expected to bind to.


Dr. Xin Gao is an associate professor of computer science at KAUST. He is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University. He earned his bachelor degree in Computer Science in 2004 from Tsinghua University and his Ph.D. degree in Computer Science in 2009 from the University of Waterloo.

Dr. Gao's research interest lies at the intersection between computer science and biology. In the field of computer science, he is interested in developing machine-learning theories and methodologies related to deep learning, probabilistic graphical models, kernel methods and matrix factorization. In the field of bioinformatics, his group works on building computational models, developing machine learning techniques, and designing efficient and effective algorithms to tackle key open problems along the path from biological sequence analysis, to 3D structure determination, to function annotation, to understanding and controlling molecular behaviors in complex biological networks, and, recently, to biomedicine and healthcare.

He has published more than 190 papers in the fields of bioinformatics and machine learning. He is the associate editor of Genomics, Proteomics & Bioinformatics, BMC Bioinformatics, and Quantitative Biology, and the guest editor-in-chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Methods, and Frontiers in Genetics.