An hierarchical artificial neural network system for the classification of transmembrane proteins

Claude Pasquier (1) , Stavros Hamodrakas (1)

1 NKUA - National and Kapodistrian University of Athens

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2

Mots clés: Protein structure prediction

Domaines: Bio-informatique [q-bio.QM]

Claude Pasquier, Stavros Hamodrakas. An hierarchical artificial neural network system for the classification of transmembrane proteins. Protein Engineering, Design and Selection, 1999, 12 (8), pp.631-634. ⟨hal-00170718v3⟩