A fast and accurate method for large-scale transmembrane beta barrel topology prediction

Abstract

Transmembrane beta barrel proteins (TMBs) play a major role in the normal functioning of the cell and are an important constituent of the translocation machinery of the outer membrane proteins in bacteria, mitochondria and chloroplast. Currently there are only around 36 experimental 3D structures available for TMBs in PDB (at 30% sequence identity). Furthermore, with large amounts of sequence data available from high-throughput methods, it is imperative to develop accurate and fast computational methods for their identification and topology prediction. Here, we present a concept for a fast method to identify TMBs and predict their topology. The method uses sparse encoded amino acid data as input and employs a Support Vector Machine (SVM) and a Hidden Markov Model (HMM) to generate accurate topologies. The topologies in the training phase are divided into pre-barrel state, outer-loop state, inner-loop state and the transmembrane beta-strand state. In the first stage, 4 separate SVMs are used to predict the local state preference for each residue. A profile generated from the probabilities thus obtained is used as input to the HMM stage to determine the overall topology. If the number of predicted strands is between 8 and 24, then the given sequence is identified as a TMB. We see the application of our method in the proteome-wide topology prediction of TMBs, where current methods might have a limitation due to the time consuming homologous sequence search step. Funding Christoph Peters: Travel Fellowship awarded by ISCB Student Council with financial support from EMBO, 2012 J-A Ekstroms travel stipend, 2012 A fast and accurate method for large-scale transmembrane beta barrel topology prediction