Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

Background: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Singlesequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results: In this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We a...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Zafer Aydin, Yucel Altunbasak, Mark Borodovsky
• Keywords: Algorithm, BMCBI 2006, Protein Secondary Structure, Secondary Structure Prediction