Homology-based method for identification of protein repeats using statistical significance estimates

Abstract

Short protein repeats, frequently with a length between 20 and 40 residues, represent a significant fraction of known proteins. Many repeats appear to possess high amino acid substitution rates and thus recognition of repeat homologues is highly problematic. Even if the presence of a certain repeat family is known, the exact locations and the number of repetitive units often cannot be determined using current methods. We have devised an iterative algorithm based on optimal and sub-optimal score distributions from profile analysis that estimates the significance of all repeats that are detected in a single sequence. This procedure allows the identification of homologues at alignment scores lower than the highest optimal alignment score for non-homologous sequences. The method has been used to investigate the occurrence of eleven families of repeats in Saccharomyces cerevisiae, Caenorhabditis elegans and Homo sapiens accounting for 1055, 2205 and 2320 repeats, respectively. For these examples, the method is both more sensitive and more selective than conventional homology search procedures. The method allowed the detection in the SwissProt database of more than 2000 previously unrecognised repeats belonging to the 11 families. In addition, the method was used to merge several repeat families that previously were supposed to be distinct, indicating common phylogenetic origins for these families.