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Improved prediction of trans-membrane spans in proteins using an artificial neural network

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3 Author(s)
Koehler, J. ; Center for Struct. Biol., Vanderbilt Univ., Nashville, TN ; Mueller, R. ; Meiler, J.

Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. Computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane spans from the sequence of a protein. The novelty of the approach presented here is the simultaneous prediction of trans-membrane spanning alpha-helices and beta-strands within a single tool. An artificial neural network was trained on databases of 102 membrane proteins and 3499 soluble proteins. Prediction accuracies of up to 92% for soluble residues, 75% for residues in the interface, and 73% for TM residues are achieved. On average the algorithm predicts 79% of the residues correctly which is a substantial improvement from a previously published implementation which achieved 57% accuracy (Koehler et al., Proteins: Structure, Function, and Bioinformatics, 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both alpha-helical bundles and beta-barrels.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology, 2009. CIBCB '09. IEEE Symposium on

Date of Conference:

March 30 2009-April 2 2009