Open Access

eLife

, volume 11

Heterogeneity of the GFP fitness landscape and data-driven protein design

Louisa Gonzalez Somermeyer ¹

Aubin Fleiss ^{2, 3}

Alexander S. Mishin ⁴

Nina G Bozhanova ⁵

Anna A Igolkina ⁶

Jens Meiler ^{5, 7}

Maria Elisenda Alaball ^{2, 3}

Ekaterina V. Putintseva ⁸

Karen S. Sarkisyan ^{2, 3, 4}

Fyodor A. Kondrashov ^{1, 9}

Hide authors affiliations Show authors affiliations: 9 affiliations

Institute of Science and Technology |

DNA motors Group, MRC London Institute of Medical Sciences

Imperial College |

⁴

Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences

⁵

Department of Biological Sciences, Vanderbilt University |

⁶

CeMM Research Center for Molecular Medicine, Austrian Academy of Sciences

⁷

Wilhelm Wundt Institute of Psychology, Leipzig University |

⁸

LabGenius

⁹

Developmental Neurobiology Unit, Okinawa Institute of Science and Technology Graduate University |

Publication type: Journal Article

Publication date: 2022-05-05

eLife Sciences Publications

eLife

scimago Q1

SJR: 3.379

CiteScore: —

Impact factor: —

ISSN: 2050084X

DOI: 10.7554/elife.75842

Copy DOI

PubMed ID: 35510622

General Biochemistry, Genetics and Molecular Biology

General Medicine

General Immunology and Microbiology

General Neuroscience

Abstract

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

Found

1 citation

Kulakova Anna

PhD in Physics and Mathematics

37 publications, 135 citations

h-index: 6

Lomonosov Moscow State University

Research interests

Molecular modeling

Reaction mechanisms

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GOST |

Cite this

GOST Copy

Gonzalez Somermeyer L. et al. Heterogeneity of the GFP fitness landscape and data-driven protein design // eLife. 2022. Vol. 11.

GOST all authors (up to 50) Copy

Gonzalez Somermeyer L., Fleiss A., Mishin A. S., Bozhanova N. G., Igolkina A. A., Meiler J., Alaball M. E., Putintseva E. V., Sarkisyan K. S., Kondrashov F. A. Heterogeneity of the GFP fitness landscape and data-driven protein design // eLife. 2022. Vol. 11.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.7554/elife.75842

UR - https://doi.org/10.7554/elife.75842

TI - Heterogeneity of the GFP fitness landscape and data-driven protein design

T2 - eLife

AU - Gonzalez Somermeyer, Louisa

AU - Fleiss, Aubin

AU - Mishin, Alexander S.

AU - Bozhanova, Nina G

AU - Igolkina, Anna A

AU - Meiler, Jens

AU - Alaball, Maria Elisenda

AU - Putintseva, Ekaterina V.

AU - Sarkisyan, Karen S.

AU - Kondrashov, Fyodor A.

PY - 2022

DA - 2022/05/05

PB - eLife Sciences Publications

VL - 11

PMID - 35510622

SN - 2050-084X

ER -

BibTex

Cite this

BibTex (up to 50 authors) Copy

@article{2022_Gonzalez Somermeyer,

author = {Louisa Gonzalez Somermeyer and Aubin Fleiss and Alexander S. Mishin and Nina G Bozhanova and Anna A Igolkina and Jens Meiler and Maria Elisenda Alaball and Ekaterina V. Putintseva and Karen S. Sarkisyan and Fyodor A. Kondrashov},

title = {Heterogeneity of the GFP fitness landscape and data-driven protein design},

journal = {eLife},

year = {2022},

volume = {11},

publisher = {eLife Sciences Publications},

month = {may},

url = {https://doi.org/10.7554/elife.75842},

doi = {10.7554/elife.75842}

}

Publisher

eLife Sciences Publications

Journal

eLife

scimago Q1

SJR

3.379

CiteScore

—

Impact factor

—

ISSN

2050084X (Print, Electronic)

Labs

Optical Bioimaging Laboratory

Profiles

Alexander S Mishin