Abstract for 'Title'

Membrane proteins are crucial for many biological functions and have become attractive targets for pharmacological agents. The importance is reflected by the observation that about 10-30% of all proteins contains membrane spanning helices. Despite recent successes, high-resolution structures for membrane proteins remain exceptional. The gap between known sequences and known structures calls for finding solutions through bioinformatics. While many methods predict membrane helices, very few predict membrane strands. The good news is that most methods for helical membrane proteins are available and are more often right than wrong. The best current prediction methods appear to correctly predict all membrane helices for about 50-70% of all proteins and to falsely predict membrane helices for about 310% of all globular proteins. The bad news is that developers have seriously over-estimated the accuracy of their methods. In particular, while simple hydrophobicity scales identify many membrane helices, they frequently and incorrectly predict membrane helices in globular proteins. Additionally, all methods tend to confuse signal peptides with membrane helices. Nonetheless, wet-lab biologists can reach into an impressive toolbox for membrane protein predictions. However, for the computational biologists, they will have to improve their methods considerably before they reach the levels of accuracy they claimed.

Key words: genome sequence analysis, protein structure prediction, multiple alignments, transmembrane helices, transmembrane prediction.

Abbreviations used: ALOM2, hydrophobicity-based prediction of membrane helices using a discriminant function [xxx 1]; DAS, dense alignment surface method predicting membrane helices [xxx 2]; GPCR, G-protein coupled receptor: family of proteins with seven transmembrane helices; HMM, Hidden Markov model (statistical algorithm from machine learning); HMMTOP, Hidden Markov model predicting transmembrane helices [xxx 3]; KD, Kyte and Doolittle [xxx 4]; KKD, application of discriminant function to the KD hydropathy [xxx 1]; MEMSAT, dynamic-programming based prediction of transmembrane helices [xxx 5]; META-PP, internet service allowing to access a variety of bioinformatics tools through one single interface [xxx 6]; OM, outer membrane; PHDhtm, profile-based neural network prediction of transmembrane helices [xxx 7; 8; 9]; ; PHDpsihtm, PSI-BLAST profile-based neural network prediction of transmembrane helices [xxx 7; 8; 9]; PP (PredictProtein), internet server for protein sequence analysis and protein structure prediction [xxx 10; 8; 11]; PRED-TMR, propensity optimised hydropathy prediction of membrane helices [xxx 12]; PSI-BLAST, position specific iterated database search [xxx 13]; SOSUI, hydrophobicity and amphiphilicity based transmembrane helix prediction [xxx 14]; SPLIT, transmembrane helix prediction [xxx 15]; SRS, Sequence Retrieval System, i.e. a portal to simultaneously access most existing data bases [xxx 16; 17]; TM, transmembrane; TMAP, alignment-based prediction of transmembrane helices [xxx 18]; TMFinder, multiple hydrophobicity-scale-based prediction of membrane helices [xxx 19]; TMH, transmembrane helix; TMHMM, Trans-Membrane prediction using Hidden Markov Models [xxx 20]; TMpred, membrane prediction based on statistical preferences [xxx 21]; TopPred, hydrophobicity-based membrane helix prediction [xxx 22]; URL, Uniform Resource Locator, i.e., address of a web site; WW, transmembrane prediction based on the Wimley-White hydrophobicity scale [xxx 23].

Abbreviations end

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