Partitioning of minimotifs based on function with improved prediction accuracy.
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.


RESULTS
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
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.
Rajasekaran
Sanguthevar
S
Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, United States of America. rajasek@engr.uconn.edu
Mi
Tian
T
Merlin
Jerlin Camilus
JC
Oommen
Aaron
A
Gradie
Patrick
P
Schiller
Martin R
MR
D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012372:ROC Curve
D011506:Proteins
Nucleic Acids Res. 2009 Jan;37(Database issue):D185-90
18978024
Nucleic Acids Res. 2003 Jul 1;31(13):3625-30
12824381
Nat Methods. 2006 Mar;3(3):175-7
16489333
Sci STKE. 2003 Jul 15;2003(191):RE12
12865499
Nat Struct Mol Biol. 2004 Jun;11(6):519-25
15133503
Methods Mol Biol. 1998;87:87-98
9523263
Nat Genet. 2000 May;25(1):25-9
10802651
Methods Enzymol. 2000;328:157-70
11075345
Nat Biotechnol. 2001 Apr;19(4):348-53
11283593
BMC Genomics. 2009 Aug 05;10:360
19656396
Nucleic Acids Res. 2003 Jul 1;31(13):3635-41
12824383
Nucleic Acids Res. 2010 Jan;38(Database issue):D167-80
19920119
BMC Bioinformatics. 2009 Oct 24;10:351
19852836
Front Biosci. 2008 May 01;13:5905-15
18508631