Article ; Online: Heterogeneity of the GFP fitness landscape and data-driven protein design.
2022 Volume 11
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 ... ...
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. |
---|---|
MeSH term(s) | Genetic Fitness ; Models, Genetic ; Mutation ; Proteins/genetics |
Chemical Substances | Proteins |
Language | English |
Publishing date | 2022-05-05 |
Publishing country | England |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2687154-3 |
ISSN | 2050-084X ; 2050-084X |
ISSN (online) | 2050-084X |
ISSN | 2050-084X |
DOI | 10.7554/eLife.75842 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.