Modeling ensembles of transmembrane β-barrel proteins

Authors

  • Jérôme Waldispühl,

    1. Department of Mathematics, MIT, Cambridge, Massachusetts
    2. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Massachusetts
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    • Jérôme Waldispühl and Charles W. O'Donnell contributed equally to this work.

  • Charles W. O'Donnell,

    1. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Massachusetts
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    • Jérôme Waldispühl and Charles W. O'Donnell contributed equally to this work.

  • Srinivas Devadas,

    1. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Massachusetts
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  • Peter Clote,

    1. Department of Biology, Boston College, Chestnut Hill, Massachusetts
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  • Bonnie Berger

    Corresponding author
    1. Department of Mathematics, MIT, Cambridge, Massachusetts
    2. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Massachusetts
    • Department of Mathematics, MIT, Cambridge, MA
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Abstract

Transmembrane β-barrel (TMB) proteins are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. Despite their importance, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. We introduce the program partiFold to investigate the folding landscape of TMBs. By computing the Boltzmann partition function, partiFold estimates inter-β-strand residue interaction probabilities, predicts contacts and per-residue X-ray crystal structure B-values, and samples conformations from the Boltzmann low energy ensemble. This broad range of predictive capabilities is achieved using a single, parameterizable grammatical model to describe potential β-barrel supersecondary structures, combined with a novel energy function of stacked amino acid pair statistical potentials. PartiFold outperforms existing programs for inter-β-strand residue contact prediction on TMB proteins, offering both higher average predictive accuracy as well as more consistent results. Moreover, the integration of these contact probabilities inside a stochastic contact map can be used to infer a more meaningful picture of the TMB folding landscape, which cannot be achieved with other methods. Partifold's predictions of B-values are competitive with recent methods specifically designed for this problem. Finally, we show that sampling TMBs from the Boltzmann ensemble matches the X-ray crystal structure better than single structure prediction methods. A webserver running partiFold is available at http://partiFold.csail.mit.edu/. Proteins 2008. © 2007 Wiley-Liss, Inc.

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