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* Corresponding author 1 Biophysics and Bioinformatics Laboratory

Abstract : 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

Keywords : protein structure membrane proteins neural network prediction





Author: Claude Pasquier - Stavros Hamodrakas -

Source: https://hal.archives-ouvertes.fr/



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