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Robust deep learning based protein sequence design using ProteinMPNN
Open accessPreprint — published as 10.1126/science.add2187 · Paper page
Abstract
While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summary: A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.
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Metadata
- Authors
- Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Wageningen University & Research (NL)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Baker, D.
- ORCID
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2022-06-04
- Volume
- 378
- Issue
- 6615
- Pages
- 49-56
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC9997061
- OpenAlex
- W4296032638
- Cited by
- 2,107
- References
- 35
- Citations by year
- 2026: 802 · 2025: 655 · 2024: 407 · 2023: 210 · 2022: 32
- FWCI
- 106.46
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Socio-Environmental Systems Modeling (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Science Foundation (DEAC02-05CH11231, DGE-214-0004, 1937533, P30 GM124169, 2140004)
- Howard Hughes Medical Institute (P30 GM124169-01, DE-AC02-05CH11231, GT11817)
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, GM124169, DE-AC02-)
- Alfred P. Sloan Foundation (g-2021-16899, DE-AC02-05CH11231)
- Washington Research Foundation
- Open Philanthropy Project
- National Institutes of Health (DE-AC02–05CH11231, P30-GM124169-01, GM124169)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02)
- National Institute of General Medical Sciences (DE-AC02-05CH11231, GM124169-01, GM124169, P30GM124169)
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., AC02–05CH11231)
- 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 › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- protein sequence design, de novo protein design, ProteinMPNN, deep learning, protein structure prediction, sequence recovery, tetrahedral nanoparticles
- MeSH terms
- Deep Learning, Amino Acid Sequence, Protein Conformation, Proteins, Protein Engineering, Crystallography, X-Ray, Cryoelectron Microscopy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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