Robust genetic codes enhance protein evolvability.


Journal

PLoS biology
ISSN: 1545-7885
Titre abrégé: PLoS Biol
Pays: United States
ID NLM: 101183755

Informations de publication

Date de publication:
May 2024
Historique:
received: 07 07 2023
accepted: 19 03 2024
medline: 17 5 2024
pubmed: 17 5 2024
entrez: 16 5 2024
Statut: epublish

Résumé

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Identifiants

pubmed: 38754362
doi: 10.1371/journal.pbio.3002594
pii: PBIOLOGY-D-23-01697
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e3002594

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright: © 2024 Rozhoňová et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Hana Rozhoňová (H)

Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Carlos Martí-Gómez (C)

Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.

David M McCandlish (DM)

Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.

Joshua L Payne (JL)

Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.

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Classifications MeSH