A NEURAL-NETWORK MODEL FOR THE PREDICTION OF MEMBRANE-SPANNING AMINO-ACID-SEQUENCES

The architecture and weights of an artificial neural network model tha t predicts putative transmembrane sequences have been developed and op timized by the algorithm of structure evolution. The resulting filter is able to classify membrane/nonmembrane transition regions in sequenc es of integral human membrane proteins with high accuracy. Similar res ults have been obtained for both training and test set data, indicatin g that the network has focused on general features of transmembrane se quences rather than specializing on the training data. Seven physicoch emical amino acid properties have been used for sequence encoding. The predictions are compared to hydrophobicity plots.