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Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence

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Research in Computational Molecular Biology (RECOMB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9029))

Abstract

Practical protein design problems require designing sequences with a combination of affinity, stability, and specificity requirements. Multistate protein design algorithms model multiple structural or binding “states" of a protein to address these requirements. comets provides a new level of versatile, efficient, and provable multistate design. It provably returns the minimum with respect to sequence of any desired linear combination of the energies of multiple protein states, subject to constraints on other linear combinations. Thus, it can target nearly any combination of affinity (to one or multiple ligands), specificity, and stability (for multiple states if needed). Empirical calculations on 52 protein design problems showed comets is far more efficient than the previous state of the art for provable multistate design (exhaustive search over sequences). comets can handle a very wide range of protein flexibility and can enumerate a gap-free list of the best constraint-satisfying sequences in order of objective function value.

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Authors and Affiliations

  1. Department of Computer Science, Duke University, Durham, NC, 27708, USA

    Mark A. Hallen & Bruce R. Donald

  2. Department of Biochemistry, Duke University Medical Center, Durham, NC, 27710, USA

    Bruce R. Donald

Authors
  1. Mark A. Hallen
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  2. Bruce R. Donald
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Correspondence to Bruce R. Donald .

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Editors and Affiliations

  1. National Center of Biotechnology Information, Bethesda, Maryland, USA

    Teresa M. Przytycka

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Hallen, M.A., Donald, B.R. (2015). Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence. In: Przytycka, T. (eds) Research in Computational Molecular Biology. RECOMB 2015. Lecture Notes in Computer Science(), vol 9029. Springer, Cham. https://doi.org/10.1007/978-3-319-16706-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-16706-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16705-3

  • Online ISBN: 978-3-319-16706-0

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