Cited by 496Machine Learning in BioinformaticsProtein Structure and DynamicsRNA and protein synthesis mechanisms

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ProGen2: Exploring the boundaries of protein language models

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Nijkamp, Erik, Ruffolo, Jeffrey A., Weinstein, Eli N., Naik, Nikhil, Madani, Ali·2023·Cell Systems

DOI 10.1016/j.cels.2023.10.002

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Abstract

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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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
ORCID: Ruffolo, Jeffrey A.
Weinstein, Eli N.
Naik, Nikhil
Madani, Ali
Venue: Cell Systems
Publisher: Elsevier BV
Published: 2023-11
Volume: 14
Issue: 11
Pages: 968-978.e3
ISSN: 2405-4712 (ISSN-L)
DOI: 10.1016/j.cels.2023.10.002
OpenAlex: W4388024559
Cited by: 480
References: 57
Citations by year: 2026: 150 · 2025: 169 · 2024: 112 · 2023: 39
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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