title: 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.
authors.affiliation: Department of Biochemistry and Molecular Biophysics, The Columbia University College of Physicians and Surgeons, Columbia University, Box 36, 630 West 168th Street, New York, NY 10032, USA.
authors.forename: Oleg
authors.initials: O
authors.lastname: Trott
authors.forename: Keri
authors.initials: K
authors.lastname: Siggers
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
authors.forename: Arthur G
authors.initials: AG
authors.lastname: Palmer
mesh_terms: D016571:Neural Networks, Computer; D009587:Nitrogen Isotopes; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D009587:Nitrogen Isotopes; D011506:Proteins