Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements

TLDR: The use of composition-based statistics is particularly beneficial for large-scale automated applications of PSI-BLAST, and the use, for each database sequence, of a position-specific scoring system tuned to that sequence's amino acid composition.

Abstract: PSI-BLAST is an iterative program to search a database for proteins with distant similarity to a query sequence. We investigated over a dozen modifications to the methods used in PSI-BLAST, with the goal of improving accuracy in finding true positive matches. To evaluate performance we used a set of 103 queries for which the true positives in yeast had been annotated by human experts, and a popular measure of retrieval accuracy (ROC) that can be normalized to take on values between 0 (worst) and 1 (best). The modifications we consider novel improve the ROC score from 0.758± 0.005 to 0.895 ± 0.003. This does not include the benefits from four modifications we included in the ‘baseline’ version, even though they were not implemented in PSI-BLAST version 2.0. The improvement in accuracy was confirmed on a small second test set. This test involved analyzing three protein families with curated lists of true positives from the non-redundant protein database. The modification that accounts for the majority of the improvement is the use, for each database sequence, of a positionspecific scoring system tuned to that sequence’s amino acid composition. The use of compositionbased statistics is particularly beneficial for largescale automated applications of PSI-BLAST.