Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction.

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Research Abstract Details 

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Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Abstract Text:

Wei Chu,Zoubin Ghahramani,Alexei Podtelezhnikov,David L Wild,

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in beta-sheets, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/-wild/bsm.html.

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Publishing Authors By Initials

W Chu,Z Ghahramani,A Podtelezhnikov,DL Wild,

For similar investigative techniques: genetic techniques: sequence alignment research abstracts see: investigative techniques: genetic techniques: sequence alignment research

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MEDLINE DATE:

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: IEEE/ACM transactions on computational biology and

VOLUME: 3

Page Numbers: 98-113

Journal Abbreviation:

ISSN: 1545-5963

DAY: 3

MONTH: 12

YEAR: 2007

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 101196755

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Keywords Mesh Terms:

KEYWORDS: Sequence Alignment

MESH TERMS: methods

Chemical & Substance for Abstract: Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Information

Substance Name: dendrotoxin

Registry Number: 74811-93-1

Grant and Affiliation Information for Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction.

AFFILIATION: Gatsby Computational Neuroscience Unit, University College London, London, UK. chuwei@gatsby.ucl.ac.uk

Country: United States

AGENCY: United States NIGMS

GRANT: 1 P01 GM63208

ACRONYM: GM

MEDLINETA: IEEE/ACM Trans Comput Biol Bio

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