Using evolutionary information for the query and target improves fold recognition.
In this study, we show that it is possible to increase the performance over PSI-BLAST by using evolutionary information for both query and target sequences. This information can be used in three different ways: by sequence linking, profile-profile alignments, and by combining sequence-profile and profile-sequence searches. If only PSI-BLAST is used, 16% of superfamily-related protein domains can be detected at 90% specificity, but if a sequence-profile and a profile-sequence search are combined, this is increased to 20%, profile-profile searches detects 19%, whereas a linking procedure identifies 22% of these proteins. All three methods show equal performance, but the best combination of speed and accuracy seems to be obtained by the combined searches, because this method shows a good performance even at high specificity and the lowest computational cost. In addition, we show that the E-values reported by all these methods, including PSI-BLAST, underestimate the true rate of false positives. This behavior is seen even if a very strict E-value cutoff and a limited number of iterations are used. However, the difference is more pronounced with a looser E-value cutoff and more iterations.
Wallner
Björn
B
Stockholm Bioinformatics Center, Stockholm University, Stockholm, Sweden.
Fang
Huisheng
H
Ohlson
Tomas
T
Frey-Skött
Johannes
J
Elofsson
Arne
A
D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D012680:Sensitivity and Specificity; D012984:Software; D013997:Time Factors
D011506:Proteins