Transmembrane protein topology prediction using support vector machines.
BACKGROUND
Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.


RESULTS
We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.


CONCLUSIONS
The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.
Nugent
Timothy
T
Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK. t.nugent@cs.ucl.ac.uk
Jones
David T
DT
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008565:Membrane Proteins / Q000737:chemistry* / Q000235:genetics; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D017510:Protein Folding; D021382:Protein Sorting Signals / Q000235:genetics; D017433:Protein Structure, Secondary / Q000235:genetics; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results
D008565:Membrane Proteins; D021382:Protein Sorting Signals