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| Title: | Alignments grow, secondary structure prediction improves |
| Author: | Dariusz Przybylski & Burkhard Rost |
| Quote: | D Przybylski & B Rost (2002) Proteins, 46, 197-205 |
| Copyright © 2002 Wiley[Imprint], Inc. |
Using information from sequence alignments significantly improves protein secondary structure prediction. Typically, more divergent profiles yield better predictions. Lately, various groups have shown that accuracy can be improved markedly by using PSI-BLAST profiles to develop new prediction methods. Here, we focused on the influences of various alignment strategies on two 8-year old PHD methods. The following results stood out. (1) PHD using pairwise alignments predicts about 72% of all residues correctly in one of the three states helix, strand, other. Using larger databases and PSI-BLAST raised accuracy to 75%. (2) More than 60% of the improvement originated from the growth of current sequence databases; about 20% resulted from detailed changes in the alignment procedure (substitution matrix, thresholds, gap penalties). Another 20% of the improvement resulted from carefully using iterated PSI-BLAST searches. (3) Interestingly, we failed to improve prediction accuracy further when attempting to refine the alignment by dynamic programming (MaxHom and ClustalW). (4) Improvement through family growth appears to saturate at some point. However, most families have not reached this saturation. Hence, we anticipate that prediction accuracy will continue to rise with database growth.
Availability: PHD and our protocol for automatic iterated
PSI-BLAST searches are available through PredictProtein http://cubic.bioc.columbia.edu/predictprotein;
the filtered PSI-BLAST database and additional scripts are available
upon request from the authors.
Key words: protein structure prediction; solvent accessibility;
evolutionary information; profiles-based multiple alignments;
dynamic programming; neural networks; PSI-BLAST
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