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Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts

Author

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  • Tobias Sikosek
  • Erich Bornberg-Bauer
  • Hue Sun Chan

Abstract

Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed. Author Summary: Proteins are essential molecules for performing a majority of functions in all biological systems. These functions often depend on the three-dimensional structures of proteins. Here, we investigate a fundamental question in molecular evolution: how can proteins acquire new advantageous structures via mutations while not sacrificing their existing structures that are still needed? Some authors have suggested that the same protein may adopt two or more alternative structures, switch between them and thus perform different functions with each of the alternative structures. Intuitively, such a protein could provide an evolutionary compromise between conflicting demands for existing and new protein structures. Yet no theoretical study has systematically tackled the biophysical basis of such compromises during evolutionary processes. Here we devise a model of evolution that specifically recognizes protein molecules that can exist in several different stable structures. Our model demonstrates that proteins can indeed utilize multiple structures to satisfy conflicting evolutionary requirements. In light of these results, we identify data from known protein structures that are consistent with our predictions and suggest novel directions for future investigation.

Suggested Citation

  • Tobias Sikosek & Erich Bornberg-Bauer & Hue Sun Chan, 2012. "Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts," PLOS Computational Biology, Public Library of Science, vol. 8(9), pages 1-17, September.
  • Handle: RePEc:plo:pcbi00:1002659
    DOI: 10.1371/journal.pcbi.1002659
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    References listed on IDEAS

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    Cited by:

    1. Tobias Sikosek & Heinrich Krobath & Hue Sun Chan, 2016. "Theoretical Insights into the Biophysics of Protein Bi-stability and Evolutionary Switches," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-27, June.
    2. Evandro Ferrada, 2014. "The Amino Acid Alphabet and the Architecture of the Protein Sequence-Structure Map. I. Binary Alphabets," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-20, December.
    3. Pengfei Tian & Robert B Best, 2020. "Exploring the sequence fitness landscape of a bridge between protein folds," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.

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