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PMID: 11717394 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Protein threading by learning.

Chang I, Cieplak M, Dima RI, Maritan A, Banavar JR

Abstract

By using techniques borrowed from statistical physics and neural networks, we determine the parameters, associated with a scoring function, that are chosen optimally to ensure complete success in threading tests in a training set of proteins. These parameters provide a quantitative measure of the propensities of amino acids to be buried or exposed and to be in a given secondary structure and are a good starting point for solving both the threading and design problems.

MeSH Terms
Amino Acid Sequence Drug Design Models, Chemical Models, Statistical Molecular Sequence Data Protein Folding Protein Structure, Secondary Proteins/chemistry
Chemicals
Proteins
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Chang I
Department of Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
Cieplak M
Dima R I
Maritan A
Banavar J R
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Article Info
Journal
Proceedings of the National Academy of Sciences of the United States of America
Abbr.
Proc Natl Acad Sci U S A
ISSN
0027-8424
Published
2001-12-04
Epub
2001-00-20
Pages
14350-5
Language
English
Region
United States
NLM ID
7505876
PMCID
PMC64685
Subset
IM
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