Document Detail


Partitioning of minimotifs based on function with improved prediction accuracy.
MedLine Citation:
PMID:  20808856     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Sanguthevar Rajasekaran; Tian Mi; Jerlin Camilus Merlin; Aaron Oommen; Patrick Gradie; Martin R Schiller
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2010-08-19
Journal Detail:
Title:  PloS one     Volume:  5     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2010  
Date Detail:
Created Date:  2010-09-02     Completed Date:  2010-11-04     Revised Date:  2013-05-28    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e12276     Citation Subset:  IM    
Affiliation:
Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America. rajasek@engr.uconn.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Amino Acid Motifs*
Computational Biology / methods*
Proteins / chemistry*,  metabolism*
ROC Curve
Grant Support
ID/Acronym/Agency:
GM079689/GM/NIGMS NIH HHS
Chemical
Reg. No./Substance:
0/Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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