Abstract
In this paper, based on the 340 protein sequences and their corresponding secondary structures got from the protein data bank (PDB), we group the 20 different amino acids into f (former), b (breaker) and n (neutral) according to their occurring frequencies in the three-state secondary structures (/spl alpha/-helix, /spl beta/-sheets and coil), which reflect the intrinsic preference of that amino acid for a given type of secondary structure. Then we use this information to improve the protein secondary structure prediction (SSP) accuracy and get a better performance than the previous methods.
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