Expanding functional protein sequence spaces using generative adversarial networks
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The model learned evolutionary conservation patterns without being trained on explicit alignment information.
“This indicates that the model has captured evolutionary conservation patterns without training on explicit alignment information such as with Potts models51, as implemented in direct coupling analysis7,52–55.”
From Large language models generate functional protein sequences across diverse families (2023) · cites this paper as [55]
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- Authors
- Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., Savolainen, O., Meskys, R., Engqvist, M. K. M., & Zelezniak, A.
- Affiliations
- Chalmers University of Technology (SE)
- Science for Life Laboratory (SE)
- Corresponding author
- Zelezniak, Aleksej
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2021-03-04
- Volume
- 3
- Issue
- 4
- Pages
- 324-333
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3135130381
- Cited by
- 388
- References
- 81
- Citations by year
- 2026: 68 · 2025: 70 · 2024: 93 · 2023: 67 · 2022: 51 · 2021: 30 · 2020: 8 · 2019: 1
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Mokslo, Inovaciju ir Technologiju Agentura (31V-59/(1.78)SU-1687)
- Science for Life Laboratory
- Vetenskapsrådet (2019-05356, 2018-05973)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- de novo protein design, generative adversarial networks, self-attention, protein sequence space, malate dehydrogenase, enzyme catalysis, protein solubility, protein evolution, amino acid substitutions
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