Statistical Learning Machines for Protein Structure Prediction
Jinbo Xu, Toyota Technological Institute at Chicago
Monday, April 29, 2013
Brickyard (BYENG) 510 [map]
If we know the primary sequence of a protein, can we predict its 3-D structure by computational methods? This is one of the most important and challenging problems in computational molecular biology and has tremendous implications for the understanding of life process, diseases and drug discovery. Depending on whether or not there is one solved structure similar to the protein sequence under consideration, computational methods for protein folding can be classified into two categories: template-based and template-free modeling. The former uses similar solved structures as templates to predict the structure of a protein while the latter does not. This talk will demonstrate how statistical learning methods especially probabilistic graphical models can be applied to address some fundamental challenges facing template-based and template-free protein folding by taking advantage of high-throughput sequencing and protein structure initiatives.
Jinbo Xu is an associate professor at the Toyota Technological Institute at Chicago, a computer science research and educational institute located at the University of Chicago, and a research affiliate of the MIT Computer Science and Artificial Intelligence Laboratory. His research lies in machine learning, optimization and computational biology (especially protein bioinformatics and biological network analysis). He has developed several popular bioinformatics programs such as the CASP-winning RAPTOR/RaptorX for protein structure prediction and IsoRank for comparative analysis of protein interaction networks. Xu has received Alfred P. Sloan Research Fellowship, NSF CAREER award and a number of NSF and NIH R01 grants.