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

ABSTRACT:

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: Single-sequence 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 (HSM

SUBMITTER: Aydin Z 

PROVIDER: S-EPMC1479840 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature