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Titre de la Présentation :
Probabilistic Graphical Model for Protein Structure Prediction
Date / Heure / Emplacement:
Vendredi, 11 février 2011 – 16:00
Salle 232, Édifice Leacock
855 Sherbrooke Ouest
Toyota Technological Institute, U. of Chicago
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.
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 structures in the Protein Data Bank (PDB) as templates 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 performs extremely well, especially on hard template-based modeling targets and our template-free method is also very promising for mainly-alpha proteins.