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Given the amino acid sequence of a protein, researchers often infer itsstructure and function by finding homologous, or evolutionarily-related,proteins of known structure and function. Since structure is typically moreconserved than sequence over long evolutionary distances, recognizing remoteprotein homologs from their sequence poses a challenge.  We first consider all proteins of known three-dimensional structure, andexplore how they cluster according to different levels of homology. Anautomatic computational method reasonably approximates a human-curatedhierarchical organization of proteins according to their degree of homology.  Next, we return to homology prediction, based only on the one-dimensionalamino acid sequence of a protein. Menke, Berger, and Cowen proposed a Markovrandom field model to predict remote homology for beta-structural proteins, buttheir formulation was computationally intractable on many beta-strandtopologies.  We show two different approaches to approximate this random field, both ofwhich make it computationally tractable, for the first time, on all proteinfolds. One method simplifies the random field itself, while the other retainsthe full random field, but approximates the solution through stochastic search.Both methods achieve improvements over the state of the art in remote homologydetection for beta-structural protein folds.

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