A Probabilistic Graphical Model for Ab Initio Folding

  • Authors:

  • Feng Zhao;Jian Peng;Joe Debartolo;Karl F. Freed;Tobin R. Sosnick;Jinbo Xu

  • Affiliations:

  • Toyota Technological Institute at Chicago, Chicago, IL 60637;Toyota Technological Institute at Chicago, Chicago, IL 60637;Department of Biochemistry and Molecular Biology, the University of Chicago, Chicago, IL 60637;Department of Chemistry, the University of Chicago, Chicago, IL 60637;Department of Biochemistry and Molecular Biology, the University of Chicago, Chicago, IL 60637;Toyota Technological Institute at Chicago, Chicago, IL 60637

  • Venue:
  • RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
  • Year:
  • 2009

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Abstract

Despite significant progress in recent years, ab initio folding is still one of the most challenging problems in structural biology. This paper presents a probabilistic graphical model for ab initio folding, which employs Conditional Random Fields (CRFs) and directional statistics to model the relationship between the primary sequence of a protein and its three-dimensional structure. Different from the widely-used fragment assembly method and the lattice model for protein folding, our graphical model can explore protein conformations in a continuous space according to their probability. The probability of a protein conformation reflects its stability and is estimated from PSI-BLAST sequence profile and predicted secondary structure. Experimental results indicate that this new method compares favorably with the fragment assembly method and the lattice model.