Heterogeneity of the GFP fitness landscape and data-driven protein design
- Institute of Science and Technology Austria, Austria
- MRC London Institute of Medical Sciences, United Kingdom
- Russian Academy of Sciences, Russian Federation
- Vanderbilt University, United States
- Austrian Academy of Sciences, Austria
- LabGenius, United Kingdom
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.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting file and are available on GitHub https://github.com/aequorea238/Orthologous_GFP_Fitness_Peaks
Article and author information
Author details
Louisa Gonzalez Somermeyer
Alexander S Mishin
Maria-Elisenda Alaball Pujol
Fyodor A Kondrashov
Funding
European Research Council (771209-CharFL)
- Fyodor A Kondrashov
MRC London Institute of Medical Sciences (UKRI MC-A658-5QEA0)
- Karen S Sarkisyan
President's Grant (МК-5405.2021.1.4)
- Karen S Sarkisyan
Marie Skłodowska-Curie Fellowship (898203)
- Aubin Fleiss
Russian Science Foundation (19-74-10102)
- Alexander S Mishin
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Daniel J Kliebenstein, University of California, Davis, United States
Version history
- Received: November 25, 2021
- Preprint posted: December 9, 2021 (view preprint)
- Accepted: March 25, 2022
- Accepted Manuscript published: May 5, 2022 (version 1)
- Accepted Manuscript updated: May 6, 2022 (version 2)
- Version of Record published: May 19, 2022 (version 3)
Copyright
© 2022, Gonzalez Somermeyer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Heterogeneity of the GFP fitness landscape and data-driven protein design
eLife 11:e75842.
https://doi.org/10.7554/eLife.75842
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