Novel computational methods towards understanding nucleic acid – protein interactions


  Biological molecules perform their functions through interaction with other molecules. Nucleic acid (DNA and RNA) – protein interaction is behind the majority of biological processes, such as DNA replication, transcription, post-transcription regulation, and translation. In this talk, I will introduce our work on developing two novel computational methods towards understanding nucleic acid – protein interactions. The first one is a structural alignment method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures to detect their structural similarity. The second one is a deep learning-based computational framework, NucleicNet, that predicts the binding specificity of different RNA constituents on the protein surface, based only on the structural information of the protein.


  Dr. Xin Gao is an associate professor of computer science in the Computer, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo, Canada. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University, U.S.. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University, China, and his Ph.D. degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo, Canada.

  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. In the field of bioinformatics, he 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, and to understanding and controlling molecular behaviors in complex biological networks. He has co-authored more than 170 research articles in the fields of bioinformatics and machine learning.

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