Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery.
BACKGROUND
Short linear motifs (SLiMs) are important mediators of protein-protein interactions. Their short and degenerate nature presents a challenge for computational discovery. We sought to improve SLiM discovery by incorporating evolutionary information, since SLiMs are more conserved than surrounding residues.


RESULTS
We have developed a new method that assesses the evolutionary signal of a residue in its sequence and structural context. Under-conserved residues are masked out prior to SLiM discovery, allowing incorporation into the existing statistical model employed by SLiMFinder. The method shows considerable robustness in terms of both the conservation score used for individual residues and the size of the sequence neighbourhood. Optimal parameters significantly improve return of known functional motifs from benchmarking data, raising the return of significant validated SLiMs from typical human interaction datasets from 20% to 60%, while retaining the high level of stringency needed for application to real biological data. The success of this regime indicates that it could be of general benefit to computational annotation and prediction of protein function at the sequence level.


BACKGROUND
All data and tools in this article are available at http://bioware.ucd.ie/~slimdisc/slimfinder/conmasking/.
Davey
Norman E
NE
UCD Complex and Adaptive Systems Laboratory, UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland.
Shields
Denis C
DC
Edwards
Richard J
RJ
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D017124:Conserved Sequence; D030562:Databases, Protein; D019143:Evolution, Molecular; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins