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.
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
- Fyodor A Kondrashov
- Karen S Sarkisyan
- Karen S Sarkisyan
- Aubin Fleiss
- Alexander S Mishin
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Daniel J Kliebenstein, University of California, Davis, United States
© 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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