Title
A computational strategy for the prediction of functional linear peptide motifs in proteins.
Abstract
Short linear peptide motifs mediate protein-protein interaction, cell compartment targeting and represent the sites of post-translational modification. The identification of functional motifs by conventional sequence searches, however, is hampered by the short length of the motifs resulting in a large number of hits of which only a small portion is functional.We have developed a procedure for the identification of functional motifs, which scores pattern conservation in homologous sequences by taking explicitly into account the sequence similarity to the query sequence. For a further improvement of this method, sequence filters have been optimized to mask those sequence regions containing little or no linear motifs. The performance of this approach was verified by measuring its ability to identify 576 experimentally validated motifs among a total of 15 563 instances in a set of 415 protein sequences. Compared to a random selection procedure, the joint application of sequence filters and the novel scoring scheme resulted in a 9-fold enrichment of validated functional motifs on the first rank. In addition, only half as many hits need to be investigated to recover 75% of the functional instances in our dataset. Therefore, this motif-scoring approach should be helpful to guide experiments because it allows focusing on those short linear peptide motifs that have a high probability to be functional.
Year
DOI
Venue
2007
10.1093/bioinformatics/btm524
Bioinformatics
Keywords
Field
DocType
short linear peptide motif,conventional sequence search,functional linear peptide motif,sequence similarity,sequence region,functional instance,functional motif,sequence filter,linear motif,computational strategy,query sequence,protein sequence,post translational modification,protein protein interaction
Short linear motif,Computer science,Peptide,Bioinformatics,Homologous Sequences
Journal
Volume
Issue
ISSN
23
24
1367-4811
Citations 
PageRank 
References 
8
0.61
7
Authors
2
Name
Order
Citations
PageRank
Holger Dinkel11658.54
Heinrich Sticht2514.49