ProGen2: Exploring the boundaries of protein language models
Open accessAbstract
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4B parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional fine-tuning. As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. Our models and code are open sourced for widespread adoption in protein engineering. A record of this paper's Transparent Peer Review process is included in the supplemental information.
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Evo reaches performance competitive with leading protein-specific language models on bacterial proteins.
“Evo also reaches competitive performance with leading protein-specific language models (41, 46–48) (Fig. 2B).”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [48]
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- Authors
- Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A.
- Affiliations
- Salesforce (United States) (US)
- Johns Hopkins University (US)
- Columbia University (US)
- Corresponding author
- Madani, Ali
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 14
- Issue
- 11
- Pages
- 968-978.e3
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W4388024559
- Cited by
- 480
- References
- 57
- Citations by year
- 2026: 150 · 2025: 169 · 2024: 112 · 2023: 39 · 2022: 9
- FWCI
- 42.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 380 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein language models, protein sequence generation, protein fitness prediction, protein design, model scaling, data distribution, immune repertoire, attention-based models
- MeSH terms
- Amino Acid Sequence, Artificial Intelligence, Language, Proteins, Databases, Factual
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