Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Cryptic genetic variation shapes the adaptive evolutionary potential of enzymes

Loading...
Thumbnail Image

Authors

Baier, Florian
Hong, Nansook
Yang, Gloria
Pabis, Anna
Miton, Charlotte M.
Barrozo, Alexandre
Carr, Paul D
Kamerlin, Shina CL
Jackson, Colin
Tokuriki, Nobuhiko

Journal Title

Journal ISSN

Volume Title

Publisher

eLife Sciences Publications Ltd

Abstract

Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may impact the evolvability of proteins, but the underlying molecular basis remains unclear. Here, we performed comparative directed evolution of four orthologous metallo-β-lactamases toward a new function and found that different starting genotypes evolved to distinct evolutionary outcomes. Despite a low initial fitness, one ortholog reached a significantly higher fitness plateau than its counterparts, via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show how cryptic molecular properties and conformational variation of active site residues in the initial genotypes cause epistasis, that could lead to distinct evolutionary outcomes. Our work highlights the importance of understanding the molecular details that connect genetic variation to protein function to improve the prediction of protein evolution.

Description

Keywords

Citation

Source

eLife

Book Title

Entity type

Access Statement

Open Access

License Rights

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Restricted until

abcd