We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block
for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual
secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods.
There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and
train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models.
These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii)
seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was
observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input
features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for
PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible
to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and
predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through
Keywords: Local protein structure, position specific scoring matrix, protein block, secondary structure, support vector machine.
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