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Energetic landscape of α-lytic protease optimizes longevity through kinetic stability

Author

Listed:
  • Sheila S. Jaswal

    (University of California at San Francisco
    Yale University)

  • Julie L. Sohl
  • Jonathan H. Davis

    (University of California at San Francisco
    Lexigen Pharmaceuticals)

  • David A. Agard

    (University of California at San Francisco)

Abstract

During the evolution of proteins the pressure to optimize biological activity is moderated by a need for efficient folding. For most proteins, this is accomplished through spontaneous folding to a thermodynamically stable and active native state. However, in the extracellular bacterial α-lytic protease (αLP) these two processes have become decoupled. The native state of αLP is thermodynamically unstable, and when denatured, requires millennia (t1/2 ∼ 1,800 years)1 to refold. Folding is made possible by an attached folding catalyst, the pro-region, which is degraded on completion of folding, leaving αLP trapped in its native state by a large kinetic unfolding barrier (t1/2 ∼1.2 years)1. αLP faces two very different folding landscapes: one in the presence of the pro-region controlling folding, and one in its absence restricting unfolding. Here we demonstrate that this separation of folding and unfolding pathways has removed constraints placed on the folding of thermodynamically stable proteins, and allowed the evolution of a native state having markedly reduced dynamic fluctuations. This, in turn, has led to a significant extension of the functional lifetime of αLP by the optimal suppression of proteolytic sensitivity.

Suggested Citation

  • Sheila S. Jaswal & Julie L. Sohl & Jonathan H. Davis & David A. Agard, 2002. "Energetic landscape of α-lytic protease optimizes longevity through kinetic stability," Nature, Nature, vol. 415(6869), pages 343-346, January.
  • Handle: RePEc:nat:nature:v:415:y:2002:i:6869:d:10.1038_415343a
    DOI: 10.1038/415343a
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    Cited by:

    1. Artur Meller & Michael Ward & Jonathan Borowsky & Meghana Kshirsagar & Jeffrey M. Lotthammer & Felipe Oviedo & Juan Lavista Ferres & Gregory R. Bowman, 2023. "Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Neema L Salimi & Bosco Ho & David A Agard, 2010. "Unfolding Simulations Reveal the Mechanism of Extreme Unfolding Cooperativity in the Kinetically Stable α-Lytic Protease," PLOS Computational Biology, Public Library of Science, vol. 6(2), pages 1-14, February.

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