We introduce a novel method for the secondary structure prediction of
proteins, PREDICT (PRofile Enumeration DICTionary), in which
the nearest-neighbor method is applied to a pattern space. For a given
protein sequence, the PSI-BLAST is used to generate a profile that
defines patterns for amino acid residues and their local sequence
environments. By applying the PSI-BLAST to protein sequences with
known secondary structures, we have constructed pattern databases.
The secondary structure of a query residue of a protein with
unknown structure can be determined by comparing the query pattern
with those in the pattern databases and selecting the patterns
close to the query pattern. We have tested the PREDICT on the CB513 set (a set of
513 non-homologous proteins) in
three different ways. The first test is based on a pattern
database derived from 7777 proteins in the PDB, including those
homologous to proteins in the CB513 set, which gave the average Q3
score of 78.8 % per chain.
In the second test, in order to carry out a more stringent benchmark
test on the CB513 set, we have removed, from the 7777 proteins, all proteins
homologous to the CB513 set, leaving 4330 proteins.
Pattern databases were constructed based on these proteins, and the
average Q3 score was 74.6 %.
In the third test, we have selected one query
protein among the CB513 set and built pattern databases using
the remaining 512 proteins. This procedure is repeated for each of
the 513 proteins and the average Q_3 score was 73.1 %.
Finally, we participated in the CASP5 (group ID: 531) where we employed
the first-layer database based on the 7777 proteins
and the second-layer database based on the CB513 set.
The PREDICT gave quite promising results with the average Q_3 (Sov)
score of 78.1 (77.4) % on 55 CASP5 targets.