title: Prediction of ATP/GTP-binding motif: a comparison of a perceptron type neural network and a consensus sequence method [corrected].
abstract: Neural networks have been applied to a number of protein structure problems. In some applications their success has not been substantiated by a comparison with the performance of a suitable alternative statistical method on the same data. In this paper, a two-layer feed-forward neural network has been trained to recognize ATP/GTP-binding [corrected] local sequence motifs. The neural network correctly classified 78% of the 349 sequences used. This was much better than a simple motif-searching program. A more sophisticated statistical method was developed, however, which performed marginally better (80% correct classification) than the neural network. The neural network and the statistical method performed similarly on sequences of varying degrees of homology. These results do not imply that neural networks, especially those with hidden layers, are not useful tools, but they do suggest that two-layer networks in particular should be carefully tested against other statistical methods.
authors.affiliation: Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, Lincoln's Inn Fields, London, UK.
authors.forename: J D
authors.initials: JD
authors.lastname: Hirst
authors.forename: M J
authors.initials: MJ
authors.lastname: Sternberg
mesh_terms: D000255:Adenosine Triphosphate; D001665:Binding Sites; D016384:Consensus Sequence; D016571:Neural Networks, Computer; D011485:Protein Binding; D011983:Receptors, Purinergic / Q000737:chemistry*; D012689:Sequence Homology, Nucleic Acid
chemical_list: D011983:Receptors, Purinergic; D000255:Adenosine Triphosphate