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Simultaneous prediction of protein secondary structure and transmembrane spans

Authors

  • Julia Koehler Leman,,

    1. Department of Chemistry, Vanderbilt University, Nashville, Tennessee
    2. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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  • Ralf Mueller,

    1. Department of Chemistry, Vanderbilt University, Nashville, Tennessee
    2. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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  • Mert Karakas,

    1. Department of Chemistry, Vanderbilt University, Nashville, Tennessee
    2. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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  • Nils Woetzel,

    1. Department of Chemistry, Vanderbilt University, Nashville, Tennessee
    2. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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  • Jens Meiler

    Corresponding author
    1. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
    • Department of Chemistry, Vanderbilt University, Nashville, Tennessee
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Correspondence to: Jens Meiler; Departments of Chemistry and Pharmacology, Center for Structural Biology, Vanderbilt University, 465 21st Ave South, BioSci/MRB III, Room 5144B, Nashville, TN 37232-8725. E-mail: jens.meiler@vanderbilt.edu

Abstract

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org. Proteins 2013; 81:1127–1140. © 2013 Wiley Periodicals, Inc.

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