Transmembrane protein topology prediction using support vector machines

By Timothy Nugent, David T. Jones. BMC Bioinformatics, 2009

446 citations recorded by the source corpus.

BackgroundAlpha-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.ResultsWe 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/.ConclusionThe 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f502dbf04615754dfdbe1e7d7cc68a212239193

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Citations in the corpus

CCTOP: a Consensus Constrained TOPology prediction web server (2015, methodology): “Ten methods were selected based on their availability and performance on different benchmark sets: HMMTOP (28,40), MemBrain (41), MEMSAT-SVM (42), Octopus (43), Philius (44), Phobius (37), Pro-and Prodiv-TMHMM (45), Scampi-MSA (38) and TMHMM (2,39).”
ProFET: Feature engineering captures high-level protein functions (2015, background): “Examples for such predictions include protein–protein interactions (Bock and Gough, 2001; Cheng and Baldi, 2007), discriminating outer membrane proteins (Gromiha and Suwa, 2005), membrane topology (Nugent and Jones, 2009), subcellular localization (Hua and Sun, 2001) and more.”
Protein annotation and modelling servers at University College London (2010, methodology): “The smaller domain has a large exposed beta sheet, this type of conformation is not common in extracellular domains and this lends some support to the MEMSAT-SVM annotation that predicts two transmembrane helices, which in turn places the N-terminal domain in the cytosolic region.”
Prediction of the human membrane proteome (2010, methodology): “Many of the latest topology prediction methods are based on machine learning techniques, where the predictor is trained on a set of membrane proteins with known topology and ‘‘learns’’ to predict the topology of other proteins based on patterns extracted from the training set [14–18].”
The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides (2015, background): “Predictors that contain specialized sub-models for signal peptides and TM segments (14,15,18,21,22,30) are thus more useful for proteome-wide analyses.”
The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis (2016, background): “It accepts MSAs as input, predicts the secondary structure of the constituent sequences with PSIPRED (35) and their membrane propensity with MEMSAT2 (36), and maps the results onto the MSA.”
Scalable web services for the PSIPRED Protein Analysis Workbench (2013, background): “The PSIPRED workbench makes available the following sequence and structure annotation methods: PSIPRED, GenTHREADER, pGenTHREADER, pDomTHREAD-ER, MEMSAT-SVM/MEMSAT3, MEMPACK, BioSerf, MetSite, HSPred, DISOPRED2, DomPred and FFPred.”
Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis (2012, unknown): “PCij 1⁄4 Scontact ij − S̄ ði−Þ S̄ contact ð−jÞ S̄ ; [3]”
Amino acid coevolution reveals three-dimensional structure and functional domains of insect odorant receptors (2015, unknown): “Transmembrane topology was predicted using PolyPhobius 49 and validated by consensus prediction using TOPCONS 50 and MEMSAT-SVM 51 .”