A Shannon entropy-based filter improves the detection of high quality profile-profile alignments in remote-homologous searching.
Emidio Capriotti1, Ivan Rossi2, Piero Fariselli and Rita Casadio
1emidio@biocomp.unibo.it, CIRB Biocomputing Unit & BioDec srl; 2ivan@biocomp.unibo.it, CIRB Biocomputing Unit & BioDec srl
Detection of homologous proteins with low sequence identity to a given target (remote homologues) is routinely performed with alignment algorithms that take advantage of sequence profile. The efficacy of different alignment procedures for the task at hand has been investigated on a set of 185 protein pairs with similar structures, but low sequence similarity. Criteria based on the SCOP label detection and MaxSub scores are adopted to score the results. We confirm that with profile-profile alignments the results are better then with other procedures.
In addition we report that the selection of the results out of the profile-profile alignments can be improved by using Shannon entropy, indicating that this parameter is important to recognize good profile-profile alignments among a plethora of meaningless pairs. By this, we enhance the global search accuracy without loosing sensitivity and filter out most of the erroneous alignments. We also show that when the entropy-filtering is adopted, the quality of the resulting alignments is comparable to that computed for the target and template structures with CE, a structural alignment program.