Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network.

Koehler J, Mueller R, Meiler J
IEEE Symp Comput Intell Bioinforma Comput Biol Proc. 2009 2009: 68-74

PMID: 27747315 · PMCID: PMC5065243 · DOI:10.1109/CIBCB.2009.4925709

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 α-helices and β-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 , , 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both α-helical bundles and β-barrels.

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