Evidence map›Paper›PMID 39617781›Full record

ArticleNature communications2024

Protein engineering using variational free energy approximation.

Evgenii Lobzaev, Michael A Herrera, Martyna Kasprzyk, Giovanni Stracquadanio

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Evgenii LobzaevSchool of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-8570-1011
Michael A HerreraSchool of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
Martyna KasprzykSchool of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0009-0001-8750-5866
Giovanni StracquadanioSchool of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom. giovanni.stracquadanio@ed.ac.uk.ORCID 0000-0001-9819-3645

Funding

RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/T00875X/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/S02431X/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/V033794/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/Y01913X/1
6 · The paper itself

Abstract

Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering.

Indexed as

Escherichia coliProtein EngineeringThermodynamicsEscherichia coli ProteinsMutationEscherichia coli Proteins

Identifiers

PMID39617781
PMCPMC11609274

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.