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

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 -helices and -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 e...

• Type: Conference
• Year: 2009
• Where: CIBCB
• Authors: Julia Koehler, Ralf Mueller, Jens Meiler
• Keywords: CIBCB 2009, Computational Biology, Membrane Proteins, Prediction, Trans-membrane Spans