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A Generic Program for Multistate Protein Design

Author

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  • Andrew Leaver-Fay
  • Ron Jacak
  • P Benjamin Stranges
  • Brian Kuhlman

Abstract

Some protein design tasks cannot be modeled by the traditional single state design strategy of finding a sequence that is optimal for a single fixed backbone. Such cases require multistate design, where a single sequence is threaded onto multiple backbones (states) and evaluated for its strengths and weaknesses on each backbone. For example, to design a protein that can switch between two specific conformations, it is necessary to to find a sequence that is compatible with both backbone conformations. We present in this paper a generic implementation of multistate design that is suited for a wide range of protein design tasks and demonstrate in silico its capabilities at two design tasks: one of redesigning an obligate homodimer into an obligate heterodimer such that the new monomers would not homodimerize, and one of redesigning a promiscuous interface to bind to only a single partner and to no longer bind the rest of its partners. Both tasks contained negative design in that multistate design was asked to find sequences that would produce high energies for several of the states being modeled. Success at negative design was assessed by computationally redocking the undesired protein-pair interactions; we found that multistate design's accuracy improved as the diversity of conformations for the undesired protein-pair interactions increased. The paper concludes with a discussion of the pitfalls of negative design, which has proven considerably more challenging than positive design.

Suggested Citation

  • Andrew Leaver-Fay & Ron Jacak & P Benjamin Stranges & Brian Kuhlman, 2011. "A Generic Program for Multistate Protein Design," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-17, July.
  • Handle: RePEc:plo:pone00:0020937
    DOI: 10.1371/journal.pone.0020937
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    References listed on IDEAS

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    1. Elisabeth L Humphris & Tanja Kortemme, 2007. "Design of Multi-Specificity in Protein Interfaces," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-14, August.
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    Cited by:

    1. Aliza B Rubenstein & Manasi A Pethe & Sagar D Khare, 2017. "MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-24, June.
    2. Jordan R Willis & Bryan S Briney & Samuel L DeLuca & James E Crowe Jr & Jens Meiler, 2013. "Human Germline Antibody Gene Segments Encode Polyspecific Antibodies," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-14, April.
    3. Patrick Löffler & Samuel Schmitz & Enrico Hupfeld & Reinhard Sterner & Rainer Merkl, 2017. "Rosetta:MSF: a modular framework for multi-state computational protein design," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-24, June.
    4. Alexander M Sevy & Swetasudha Panda & James E Crowe Jr & Jens Meiler & Yevgeniy Vorobeychik, 2018. "Integrating linear optimization with structural modeling to increase HIV neutralization breadth," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-18, February.
    5. Alexander M Sevy & Tim M Jacobs & James E Crowe Jr. & Jens Meiler, 2015. "Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-23, July.

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