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Cited by 0Evolution and Genetic DynamicsProtein Structure and DynamicsRNA and protein synthesis mechanisms

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Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.

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Bisardi, Matteo, Rodriguez-Rivas, Juan, Zamponi, Francesco, Weigt, Martin·2022·Molecular Biology and Evolution
DOI 10.1093/molbev/msab321
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Abstract

During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here, we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength, and library size. We showcase the potential of the approach in reanalyzing two recent experiments to determine protein structure from signals of epistasis emerging in experimental sequence libraries. To be detectable, these signals require sufficiently large and sufficiently diverged libraries. Our modeling framework offers a quantitative explanation for different outcomes of recently published experiments. Furthermore, we can forecast the outcome of time- and resource-intensive evolution experiments, opening thereby a way to computationally optimize experimental protocols.

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Research works printed but not linked: 36

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Metadata

Authors
Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., & Weigt, M.
Affiliations
Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Zamponi, Francesco
  • Weigt, Martin
ORCID
Venue
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2021-11-09
Volume
39
Issue
1
Pages
msab321
ISSN
  • 0737-4038 (ISSN-L)
  • 1537-1719
DOI
10.1093/molbev/msab321
OpenAlex
W4226186999
Cited by
0
References
9
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Biology and Evolution (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
Horizon 2020 Framework Programme (734439)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
protein evolution, sequence space exploration, epistasis, fitness landscape, stochastic modeling, fitness distribution, homologous protein families, protein structure prediction, selection strength, library size

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