The Molecular Bioinformatics Center (MBC) was established in 2006
as a national resource for bioinformatics and computational
systems biology in Taiwan. MBC performs research in computational
biology, develops software tools for analyzing biological data
and creates public databases. More
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Protein structure prediction provides valuable insights into function,
and comparative modeling is one of the most reliable methods to
predict 3D structures directly from amino acid sequences. However,
critical problems arise during the selection of the correct templates
and the alignment of query sequences therewith. We have developed
an automatic protein structure prediction server, (PS)2, which
uses an effective consensus strategy both in template selection,
which combines PSI-BLAST and IMPALA, and target-template alignment
integrating PSI-BLAST, IMPALA and T-Coffee. (PS)2 was evaluated
for 47 comparative modeling targets in CASP6 (Critical Assessment
of Techniques for Protein Structure Prediction). For the benchmark
dataset, the predictive performance of (PS)2, based on the mean
GTD_TS score, was superior to 10 other automatic servers. Our method
is based solely on the consensus sequence and thus is considerably
faster than other methods that rely on the additional structural
consensus of templates. Our results show that (PS)2, coupled with
suitable consensus strategies and a new similarity score, can significantly
improve structure prediction. Our approach should be useful in
structure prediction and modeling. The (PS)2 is available through
the website at http://ps2.life.nctu.edu.tw/.