Systematic and Fully Automated Identi cation of Protein Sequence Patterns

We present an efŽ cient algorithm to systematically and automatically identify patterns in protein sequence families. The procedure is based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical signiŽ cance of patterns. We demonstrate its application to the fully automated discovery of patterns in 974 PROSITE families (the complete subset of PROSITE families which are deŽ ned by patterns and contain DR records). Splash generates patterns with better speciŽ city and undiminished sensitivity, or vice versa, in 28% of the families; identical statistics were obtained in 48% of the families, worse statistics in 15%, and mixed behavior in the remaining 9%. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with the corresponding PROSITE pattern. The procedure is sufŽ ciently rapid to enable its use for daily curation of existing motif and proŽ le databases. Third, our results show that the statistical signiŽ cance of discovered patterns correlates well with their biological signiŽ cance. The trypsin subfamily of serine proteases is used to illustrate this method’s ability to exhaustively discover all motifs in a family that are statistically and biologically signiŽ cant. Finally, we discuss applications of sequence patterns to multiple sequence alignment and the training of more sensitive scorebased motif models, akin to the procedure used by PSI-BLAST. All results are available at http://www.research.ibm.com/spat/.