title: Fast and accurate discovery of degenerate linear motifs in protein sequences.
abstract: Linear motifs mediate a wide variety of cellular functions, which makes their characterization in protein sequences crucial to understanding cellular systems. However, the short length and degenerate nature of linear motifs make their discovery a difficult problem. Here, we introduce MotifHound, an algorithm particularly suited for the discovery of small and degenerate linear motifs. MotifHound performs an exact and exhaustive enumeration of all motifs present in proteins of interest, including all of their degenerate forms, and scores the overrepresentation of each motif based on its occurrence in proteins of interest relative to a background (e.g., proteome) using the hypergeometric distribution. To assess MotifHound, we benchmarked it together with state-of-the-art algorithms. The benchmark consists of 11,880 sets of proteins from S. cerevisiae; in each set, we artificially spiked-in one motif varying in terms of three key parameters, (i) number of occurrences, (ii) length and (iii) the number of degenerate or "wildcard" positions. The benchmark enabled the evaluation of the impact of these three properties on the performance of the different algorithms. The results showed that MotifHound and SLiMFinder were the most accurate in detecting degenerate linear motifs. Interestingly, MotifHound was 15 to 20 times faster at comparable accuracy and performed best in the discovery of highly degenerate motifs. We complemented the benchmark by an analysis of proteins experimentally shown to bind the FUS1 SH3 domain from S. cerevisiae. Using the full-length protein partners as sole information, MotifHound recapitulated most experimentally determined motifs binding to the FUS1 SH3 domain. Moreover, these motifs exhibited properties typical of SH3 binding peptides, e.g., high intrinsic disorder and evolutionary conservation, despite the fact that none of these properties were used as prior information. MotifHound is available (http://michnick.bcm.umontreal.ca or http://tinyurl.com/motifhound) together with the benchmark that can be used as a reference to assess future developments in motif discovery.
authors.affiliation: Département de Biochimie and Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, Succursale Centre-Ville, Montreal, Quebec, Canada.
authors.forename: Abdellali
authors.initials: A
authors.lastname: Kelil
authors.affiliation: Département de Biochimie and Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, Succursale Centre-Ville, Montreal, Quebec, Canada.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Dubreuil
authors.affiliation: Département de Biochimie and Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, Succursale Centre-Ville, Montreal, Quebec, Canada.
authors.forename: Emmanuel D
authors.initials: ED
authors.lastname: Levy
authors.affiliation: Département de Biochimie and Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, Succursale Centre-Ville, Montreal, Quebec, Canada.
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Michnick
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism; D013997:Time Factors; D018909:src Homology Domains
chemical_list: C490024:FUS1 protein, S cerevisiae; D008565:Membrane Proteins; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins