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Protein Threading by Learning

Chang, Iksoo ; Cieplak, Marek ; Dima, Ruxandra I. ; Maritan, Amos ; Banavar, Jayanth R.

Proceedings of the National Academy of Sciences - PNAS, 2001-12, Vol.98 (25), p.14350-14355 [Periódico revisado por pares]

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Título:
Protein Threading by Learning
Autor: Chang, Iksoo ; Cieplak, Marek ; Dima, Ruxandra I. ; Maritan, Amos ; Banavar, Jayanth R.
Assuntos: Amino Acid Sequence ; Amino acids ; Applied statistics ; Biochemistry ; Biological Sciences ; Biology ; Drug Design ; Mathematical functions ; Mathematical independent variables ; Models, Chemical ; Models, Statistical ; Molecular Sequence Data ; Physics ; Probabilities ; Protein Folding ; Protein Structure, Secondary ; Proteins ; Proteins - chemistry ; Studies ; Training
É parte de: Proceedings of the National Academy of Sciences - PNAS, 2001-12, Vol.98 (25), p.14350-14355
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
To whom reprint requests should be addressed. E-mail: jayanth@phys.psu.edu.
Edited by Peter G. Wolynes, University of California at San Diego, La Jolla, CA, and approved September 20, 2001
Descrição: 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.
Editor: United States: National Academy of Sciences
Idioma: Inglês

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