Protein conformational flexibility prediction using machine learning.

TitleProtein conformational flexibility prediction using machine learning.
Publication TypeJournal Article
Year of Publication2008
AuthorsTrott, O, Siggers, K, Rost, B, Palmer, AG
JournalJ Magn Reson
Date Published2008 May
KeywordsNeural Networks (Computer), Nitrogen Isotopes, Nuclear Magnetic Resonance, Biomolecular, Protein Conformation, Protein Structure, Secondary, Proteins

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.

Alternate JournalJ. Magn. Reson.
PubMed ID18313957
PubMed Central IDPMC2413295
Grant ListGM50291 / GM / NIGMS NIH HHS / United States
LM007329 / LM / NLM NIH HHS / United States
R01 GM050291-14 / GM / NIGMS NIH HHS / United States