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Protein threading by learning.


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.

SUBMITTER: Chang I 

PROVIDER: S-EPMC64685 | biostudies-literature | 2001 Dec

REPOSITORIES: biostudies-literature

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Protein threading by learning.

Chang I I   Cieplak M M   Dima R I RI   Maritan A A   Banavar J R JR  

Proceedings of the National Academy of Sciences of the United States of America 20011120 25


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. ...[more]

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