MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search

Authors:
Noah M. Daniels;Andrew Gallant;Norman Ramsey;Lenore J. Cowen
Affiliations:
Dept. of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155;Dept. of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155;Dept. of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155;Dept. of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

We introduce MRFy, a tool for protein remote homology detection that captures beta-strand dependencies in the Markov random field. Over a set of 11 SCOP beta-structural superfamilies, MRFy shows a 14% improvement in mean Area Under the Curve for the motif recognition problem as compared to HMMER, 25% improvement as compared to RAPTOR, 14% improvement as compared to HHPred, and a 18% improvement as compared to CNFPred and RaptorX. MRFy was implemented in the Haskell functional programming language, and parallelizes well on multi-core systems. MRFy is available, as source code as well as an executable, from http://mrfy.cs.tufts.edu/.