Probabilistic Graphical Model for Protein Structure Prediction

Speaker:	Dr. Jinbo XU
		Toyota Technological Institute at Chicago

Title:		"Probabilistic Graphical Model for Protein Structure
		 Prediction"

Date:		Friday, 17 December 2010

Time:		11:00am - 12 noon

Venue:		Lecture Theater H (near lifts 27/28), HKUST

Abstract:

If we know the primary sequence of a protein, can we predict its
three-dimensional structure by computational methods? This is one of the
most important and difficult problems in computational molecular biology
and has tremendous implications for protein functional study and drug
discovery.

Roughly speaking, existing computational methods for protein structure
prediction can be broadly classified into two categories: template-based
modeling (i.e, protein threading/homology modeling) and template-free
modeling (i..e, ab initio folding). Template-based modeling predicts
structure of a protein using experimental structure in the Protein Data
Bank (PDB) as a template while template-free modeling predicts protein
structure without depending on a template.

This talk will present new probabilistic graphical models for
knowledge-based protein structure prediction. In particular, this talk
will present a regression-tree-based Conditional Random Fields (CRF)
method for template-based modeling and a Conditional Random
Fields/Conditional Neural Fields (CRF/CNF) method for template-free
modeling. Experimental results indicate that our template-based method
outperforms all the other methods of same category, especially on
low-homology proteins and our template-free method compares favorably to
the popular fragment assembly method.

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Biography:

Dr. Jinbo Xu currently is an assistant professor at the Toyota
Technological Institute at Chicago (a computer science institute on the
campus of the University of Chicago). He is also an adjunct professor at
the University of Waterloo and a visiting scientist at the CSAIL of the
Massachusetts Institute of Technology. Dr. Xu received his PhD in Computer
Science from the University of Waterloo and then spent one year as a
Postdoctoral Fellow in the Department of Mathematics, MIT. Dr. Xu's
primary research interest is computational biology and bioinformatics
including biological sequence analysis, protein/RNA structure analysis and
prediction and biological network analysis. His RaptorX/RAPTOR programs
have been ranked very top in recent CASP (Critical Assessment of Structure
Prediction) competitions and he was also invited to speak at the CASP
meetings and publish papers in the CASP special issues.