Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Aug 19;5(8):e12276.
doi: 10.1371/journal.pone.0012276.

Partitioning of Minimotifs Based on Function With Improved Prediction Accuracy

Affiliations
Free PMC article

Partitioning of Minimotifs Based on Function With Improved Prediction Accuracy

Sanguthevar Rajasekaran et al. PLoS One. .
Free PMC article

Abstract

Background: Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.

Methodology/principal findings: Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components.

Conclusions/significance: Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is approximately 4.6 times that of random minimotifs. For the molecular function filter this ratio is approximately 2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. ROC curves for minimotif filters.
ROC curves for the molecular (A) and cellular (B) function filters, as well as the frequency score filter are shown. Analysis was with the minimotifs in the MnM 2 database that have known molecular and cellular functions in the GO database (A,B).
Figure 2
Figure 2. ROC curve for the combined filters.
Combination of molecular function and frequency score filters (A) and combination of cellular function and frequency score filters (B) are shown. These ROC curves have been obtained by combining the two pairs of filters on an either-or basis.
Figure 3
Figure 3. Image of the filter selector on the MnM website.
All filters in this paper are now included as part of the MnM website. The option to select minimotifs that have similar or dissimilar functions is implemented.

Similar articles

See all similar articles

Cited by 6 articles

See all "Cited by" articles

References

    1. Balla S, Thapar V, Verma S, Luong T, Faghri T, et al. Minimotif Miner: a tool for investigating protein function. Nat Methods. 2006;3:175–177. - PubMed
    1. Rajasekaran S, Balla S, Gradie P, Gryk MR, Kadaveru K, et al. Minimotif miner 2nd release: A database and web system for motif search. Nucleic Acids Res. 2009;37:D185–D190. - PMC - PubMed
    1. Puntervoll P, Linding R, Gemünd C, Chabanis-Davidson S, Mattingsdal M, et al. ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res. 2003;31:3625–3630. - PMC - PubMed
    1. Gould CM, Diella F, Via A, Puntervoll P, Gemünd C, et al. Elm: the status of the 2010 eukaryotic linear motif resource. Nucl. Acids Res. 2009;38:D167–D180. - PMC - PubMed
    1. Yaffe MB, Leparc GG, Lai J, Obata T, Volinia S, et al. A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol. 2001;19:348–353. - PubMed

Publication types

LinkOut - more resources

Feedback