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BioMed Central Page 1 of 15

Summary: BioMed Central
Page 1 of 15
(page number not for citation purposes)
BMC Bioinformatics
Open AccessResearch article
Protein secondary structure prediction for a single-sequence using
hidden semi-Markov models
Zafer Aydin1, Yucel Altunbasak1 and Mark Borodovsky*2
Address: 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA and 2School of Biology,
the Wallace H. Coulter Department of Biomedical Engineering and the Center for Bioinformatics and Computational Biology, Georgia Institute
of Technology, Atlanta, GA 30332-0230, USA
Email: Zafer Aydin - aydinz@ece.gatech.edu; Yucel Altunbasak - yucel@ece.gatech.edu; Mark Borodovsky* - mark@amber.biology.gatech.edu
* Corresponding author
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


Source: Altunbasak, Yucel - School of Electrical and Computer Engineering, Georgia Institute of Technology
Aydin, Zafer - Department of Genome Sciences, University of Washington at Seattle
Borodovsky, Mark - School of Biology, Georgia Institute of Technology


Collections: Biology and Medicine; Biotechnology; Engineering; Environmental Sciences and Ecology