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An optimization approach to the problem of protein structure prediction

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Abstract.

We describe a large-scale, stochastic-perturbation global optimization algorithm used for determining the structure of proteins. The method incorporates secondary structure predictions (which describe the more basic elements of the protein structure) into the starting structures, and thereafter minimizes using a purely physics-based energy model. Results show this method to be particularly successful on protein targets where structural information from similar proteins is unavailable, i.e., the most difficult targets for most protein structure prediction methods. Our best result to date is on a protein target containing over 4000 atoms and ∼12,000 cartesian coordinates.

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Authors and Affiliations

  1. Department of Computer Science, University of Colorado at Boulder, Boulder, Colorado, 80309-0430

    Elizabeth Eskow, Brett Bader, Richard Byrd, Vincent Lamberti & Robert Schnabel

  2. Physical Sciences, Life Sciences and NERSC Divisions, Lawrence Berkeley National Laboratory, Berkeley, California, 94720

    Silvia Crivelli & Teresa Head-Gordon

Authors
  1. Elizabeth Eskow
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  2. Brett Bader
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  3. Richard Byrd
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  4. Silvia Crivelli
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  5. Teresa Head-Gordon
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  6. Vincent Lamberti
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  7. Robert Schnabel
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Eskow, E., Bader, B., Byrd, R. et al. An optimization approach to the problem of protein structure prediction. Math. Program., Ser. A 101, 497–514 (2004). https://doi.org/10.1007/s10107-003-0493-4

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  • DOI: https://doi.org/10.1007/s10107-003-0493-4

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