Motif neural network design for large-scale protein family identification

This paper describes an application of artificial neural networks for the sequence analysis and management of the large and rapidly growing molecular databases. The neural network system, based on a motif identification neural design (MOTIFIND) that incorporates both global and motif sequence information, has been implemented for large-scale protein family identification. More than nine hundred backpropagation networks were trained, one for each protein family. The protein families were defined collectively by the ProSite and PIR databases. As a part of an integrated protein family identification system, the neural networks were used as filters to quickly detect potential new members in comprehensive searches against the two major protein sequence databases, SwissProt and PIR. The integrated system identified a large number of false negative members missed by both ProSite and PIR. The speed, sensitivity, general applicability, together with its capability to learn existing protein classification schemes, make the MOTIFIND neural network system an ideal tool for the full-scale protein family classification effort