title: The importance of larger data sets for protein secondary structure prediction with neural networks.
abstract: A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32-fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10-20 times as fast as the standard steepest descent algorithm.
authors.affiliation: Biophysics Program, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Chandonia
authors.forename: M
authors.initials: M
authors.lastname: Karplus
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary