Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
Open accessPublished version of a preprint
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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Cites
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- Authors
- Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., & Weigt, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Zamponi, Francesco
- Weigt, Martin
- 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
- OpenAlex
- W4226186999
- Cited by
- 0
- References
- 9
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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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