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PubMed · 7584366

Transmembrane segment prediction from protein sequence data.

S M Weiss·D M Cohen·N Indurkhya
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Abstract

We consider the automated identification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segments) were examined, representing every protein in the PIR database having the transmembrane domain feature annotation. Machine learning techniques were used to evaluate the efficacy of alternative hydrophobicity measures and windowing techniques. We describe a simpler measure of hydrophobicity and a new variable window size concept. We demonstrate that these techniques are superior to some previous techniques in minimizing the segment error rate. Using these new techniques, we describe an algorithm that has a 7.9% segment error rate on the sampled proteins, while classifying 16.7% of the amino acid residues as transmembrane.

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AlgorithmsAmino Acid SequenceAmino AcidsArtificial IntelligenceForecastingMembrane ProteinsMolecular Sequence DataReproducibility of ResultsSequence AnalysisJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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S M Weiss, D M Cohen, N Indurkhya. 1993. Transmembrane segment prediction from protein sequence data.. https://pubmed.ncbi.nlm.nih.gov/7584366/

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