A neural network model for the prediction of membrane-spanning amino acid sequences.
The architecture and weights of an artificial neural network model that predicts putative transmembrane sequences have been developed and optimized by the algorithm of structure evolution. The resulting filter is able to classify membrane/nonmembrane transition regions in sequences of integral human membrane proteins with high accuracy. Similar results have been obtained for both training and test set data, indicating that the network has focused on general features of transmembrane sequences rather than specializing on the training data. Seven physicochemical amino acid properties have been used for sequence encoding. The predictions are compared to hydrophobicity plots.
Gesellschaft zur Förderung angewandter Informatik (GFaI), Berlin, Germany.
R
R
Lohmann
G
G
Schneider
D
D
Behrens
P
P
Wrede
D000465:Algorithms; D015703:Antigens, CD; D001436:Bacteriorhodopsins / Q000737:chemistry; D015815:Cell Adhesion Molecules; D006023:Glycoproteins / Q000737:chemistry; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D012243:Rhodopsin / Q000737:chemistry; D017421:Sequence Analysis
D015703:Antigens, CD; C069847:CD66 antigens; D015815:Cell Adhesion Molecules; D006023:Glycoproteins; D008565:Membrane Proteins; D001436:Bacteriorhodopsins; D012243:Rhodopsin