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Protein conformational flexibility prediction using machine learning.

ABSTRACT: Using a data set of 16 proteins, a neural network has been trained to predict backbone 15N generalized order parameters from the three-dimensional structures of proteins. The final network parameterization contains six input features. The average prediction accuracy, as measured by the Pearson's correlation coefficient between experimental and predicted values of the square of the generalized order parameter is >0.70. Predicted order parameters for non-terminal amino acid residues depends most strongly on the local packing density and the probability that the residue is located in regular secondary structure.

SUBMITTER: Trott O 

PROVIDER: S-EPMC2413295 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Protein conformational flexibility prediction using machine learning.

Trott Oleg O   Siggers Keri K   Rost Burkhard B   Palmer Arthur G AG  

Journal of magnetic resonance (San Diego, Calif. : 1997) 20080201 1

Using a data set of 16 proteins, a neural network has been trained to predict backbone 15N generalized order parameters from the three-dimensional structures of proteins. The final network parameterization contains six input features. The average prediction accuracy, as measured by the Pearson's correlation coefficient between experimental and predicted values of the square of the generalized order parameter is >0.70. Predicted order parameters for non-terminal amino acid residues depends most s  ...[more]

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