Dataset Information

Yeast two-hybrid junk sequences contain selected linear motifs.

ABSTRACT: Yeast two-hybrid (Y2H) screenings result in identification of many out-of-frame (OOF) clones that code for short (2-100 amino acids) peptides with no sequence homology to known proteins. We hypothesize that these peptides can reveal common short linear motifs (SLiMs) responsible for their selection. We present a new protocol to address this issue, using an existing SLIM detector (TEIRESIAS) as a base method, and applying filters derived from a mathematical model of SLiM selection in OOF clones. The model allows for initial analysis of likely presence of SLiM(s) in a collection of OOF sequences, assisting investigators with the decision of whether to invest resources in further analysis. If SLiM presence is detected, it estimates the length and number of amino acid residues involved in binding specificity and the amount of noise in the Y2H screen. We demonstrate that our model can double the prediction sensitivity of TEIRESIAS and improve its specificity from 0 to 1.0 on simulated data and apply the model to seven sets of experimentally derived OOF clones. Finally, we experimentally validate one SLiM found by our method, demonstrating its utility.

SUBMITTER: Liu Y

PROVIDER: S-EPMC3201885 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Yeast two-hybrid junk sequences contain selected linear motifs.

Liu Yun Y Woods Nicholas T NT Kim Dewey D Sweet Michael M Monteiro Alvaro N A AN Karchin Rachel R

Nucleic acids research 20110723 19

Yeast two-hybrid (Y2H) screenings result in identification of many out-of-frame (OOF) clones that code for short (2-100 amino acids) peptides with no sequence homology to known proteins. We hypothesize that these peptides can reveal common short linear motifs (SLiMs) responsible for their selection. We present a new protocol to address this issue, using an existing SLIM detector (TEIRESIAS) as a base method, and applying filters derived from a mathematical model of SLiM selection in OOF clones. ...[more]

PMID: 21785140

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Project description: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.

Dataset Information

Yeast two-hybrid junk sequences contain selected linear motifs.

Publications

Yeast two-hybrid junk sequences contain selected linear motifs.

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