The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index; 1 of the 8 sources its claims cite were checked for support.
Robust deep learning–based protein sequence design using ProteinMPNN
Open accessPublished version of a preprint
Abstract
Although 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, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. 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. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo–electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
ProteinMPNN is a deep learning–based protein sequence design method that uses a message-passing neural network with order-agnostic decoding and symmetry-aware coupling to design sequences for monomers, oligomers, nanoparticles, and interfaces. It achieves higher native sequence recovery than Rosetta and rescues previously failed experimental designs across multiple protein design challenges.
Key findings
- On native protein backbones, ProteinMPNN achieved 52.4% sequence recovery versus 32.9% for Rosetta, while requiring only 1.2 seconds versus 4.3 minutes for a 100-residue protein.
- Median sequence recovery was 52% for monomers, 55% for homomers, and 51% for heteromers; recovery ranged from 90–95% in the deep core to 35% on the surface.
- Training with backbone noise improved robustness: a model trained with 0.3 Å noise generated 2–3 times more sequences with AlphaFold predictions within lDDT-Ca of 95.0 and 90.0 of the true structures than unnoised or slightly noised models.
- Experimental rescue of AlphaFold-generated designs: of 96 designs produced in E. coli, 73 were soluble (median soluble yield 247 mg/L vs. 9 mg/L for AF sequences) and 50 had the target monomeric or oligomeric state; for C5/C6 cyclic oligomers, ProteinMPNN designs were 88% soluble with 27.7% correct oligomeric state versus 40% soluble and none correct for Rosetta designs.
Methods
- ProteinMPNN is based on a message-passing neural network with 3 encoder and 3 decoder layers and 128 hidden dimensions, using backbone features including Ca-Ca distances, relative frame orientations, dihedral angles, and added N, Ca, C, O, and virtual Cb distances; edge updates and order-agnostic autoregressive decoding were introduced.
- Training used protein assemblies from the PDB (as of Aug 02, 2021) determined by X-ray crystallography or cryoEM to better than 3.5 Å resolution and with fewer than 10,000 residues, clustered at 30% sequence identity with mmseqs2, yielding 25,361 clusters; monomer training/validation used 19.7k high-resolution single-chain structures split by CATH.
- Evaluation included in silico test sets of 402 monomer backbones for Rosetta comparison and 690 monomers, 732 homomers (<2000 residues), and 98 heteromers, plus experimental characterization by E. coli expression, SEC, SEC-MALS, X-ray crystallography, cryoEM, and biolayer interferometry.
Limitations
- The paper states that in silico metrics such as native sequence recovery are sensitive to crystallographic resolution and may not correlate with proper folding; even a single residue substitution can block folding.
- The paper notes that deep learning methods lack the physical transparency of methods like Rosetta.
- The text does not include a dedicated limitations section; evident from the text, experimental validation covers selected design challenges and backbones, and not all designed sequences were structurally characterized.
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29 Own result · 7 Own interpretation · 6 No basis given · 4 Sources not found · 1 Partly supported · 1 No full text of the sources · accuracy not yet measured
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In the paper
“we found that the AF generated sequences were mostly insoluble (median soluble yield: 9 mg per liter of culture equivalent Figure 3A).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
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In the paper
“This resulted in a sequence recovery increase from 41.2% (baseline model) to 49.0% (experiment 1), see Table 1 below”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“Combining additional input features and edge updates leads to a sequence recovery of 50.5% (experiment 3).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“Although requiring only a small fraction of the compute time (1.2 seconds versus 4.3 minutes for 100 residues)”
- Also stated in: § Conclusion · plain
“ProteinMPNN solves sequence design problems in a small fraction of the time (1.2 sec vs 258.8 sec on a single CPU for a 100 residue protein) required for physically based approaches such as Rosetta”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“found that performance saturated at 32–48 neighbors.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“sequences encoded by noised ProteinMPNN models are more accurately decoded into 3D coordinates by AlphaFold”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
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In the paper
“with improvements across the full range of residue burial from protein core to surface”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“a model trained with 0.3Å noise generated 2–3 times more sequences with AlphaFold predictions within lDDT-Ca (14) of 95.0 and 90.0 of the true structures than unnoised or slightly noised models (Figure 2C).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
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In the paper
“The accuracy was particularly high in the central core of the structure, with sidechains predicted using AlphaFold from the ProteinMPNN sequence fitting nearly perfectly into the electron density (Figure 3D).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the strength of the single sequence to structure mapping, as assessed by AlphaFold, was higher for models trained with additional backbone noise.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
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In the paper
“Machine learning sequence design approaches have been developed previously (1–6, 6a), notably the previously described message passing method on which ProteinMPNN is based, but have focused on the monomer design problem, achieve lower native sequence recoveries, and with the exception of a TIM barrel design study (6) have not been extensively validated using crystallography and cryoEM to evaluate”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
What the sources say
- Not matchedcites 1–6, 6a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [6] Anand N, Eguchi R, Mathews II, Perez CP, Derry A, Altman RB, & Huang PS (2022). Protein sequence design with a learned potential. Nature communications, 13(1), 1– 11. PMC8826426 35136054 sourceExperimental — accuracy not yet measured
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In the paper
“Whereas only a small fraction of the original Rosetta designed sequences were predicted to fold to the design target structures, following ProteinMPNN redesign the majority were confidently predicted to fold to close to the design target structures (Figure 2F).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
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In the paper
“unconstrained design without symmetry, averaged probabilities, and averaged logits resulted in 52%, 53%, and 55% median sequence recoveries respectively (Figure S1C).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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“The median overall sequences recoveries were 52% for monomers, 55% for homomers, and 51% for heteromers (Figure 2B).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
plainn = 3- Asserted: passes — The authors put it forward as true.
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“Many of the proteins were highly thermo-stable, with secondary structure being maintained up to 95 °C (Figure 3B).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The alpha-beta protein structure contains 5 beta strands and 4 alpha helices, and is close to the design target backbone (2.35 Å over 130 residues)”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
plainSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“of 96 designs produced in E. coli, 73 were expressed solubly (median soluble yield: 247 mg per liter of culture equivalent, Figure 3A) and 50 had the target monomeric or oligomeric state as assessed by SEC (Figure 3A,C).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“training models on backbones to which Gaussian noise (std=0.02Å) had been added improved sequence recovery on confident protein structure models generated by AlphaFold (average pLDDT>80.0) from UniRef50, while the sequence recovery on unperturbed PDB structures decreased as expected (Table 1).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
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In the paper
“Biolayer interferometry experiments showed strong binding to the Grb2 SH3 domain (Figure 4B), with considerably higher signal than the free proline rich peptide; point mutations predicted to disrupt the design completely eliminated the binding signal.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
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- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
In the paper
“ProteinMPNN designs also have a much higher propensity to crystallize, greatly facilitating structure determination of designed proteins.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
What the sources say
- Not matchedcites Wicky et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“For the Rosetta designed set, 40% were soluble and none had the correct oligomeric state confirmed by SEC-MALS. For the ProteinMPNN designed set, 88% were soluble and 27.7% had the correct oligomeric state.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Experimental evaluation of ProteinMPNN
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“they are trained directly to find the most probable amino acid for a protein backbone given all the examples in the PDB, and hence such ambiguities do not arise, making sequence design more robust and less dependent on the judgement of a human expert.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
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In the paper
“In traditional physically based approaches, sequence design maps to the problem of identifying an amino acid sequence whose lowest energy state is the desired structure.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
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In the paper
“However, the methods described thus far are limited in their applicability to the wide range of current protein design challenges, and have not been extensively validated experimentally.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
What the sources say
- Not matchedcites 1–6, 6a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Recently deep learning approaches have shown considerable promise in rapidly generating plausible amino acid sequences given monomeric protein backbones without need for compute intensive explicit consideration of sidechain rotameric states (1–6, 6a).”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
What the sources say
- Not matchedcites 1–6, 6a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“AlphaFold (9) and RoseTTAfold (13) produce remarkably good structure predictions for native proteins given multiple sequence alignments which can contain substantial co-evolutionary and other information reflecting aspects of the 3D structure, but generally produce much poorer structures when provided only with a single sequence.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Training with backbone noise improves model performance for protein design
What the sources say
- Not checked — no full text of the sourcecites [9] Uria B, Murray I, & Larochelle H (2014, January). A deep and tractable density estimator. In International Conference on Machine Learning (pp. 467– 475). PMLR. source
- Not checked — no full text of the sourcecites [13] Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, … & Bonneau R (2020). Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nature methods, 17(7), 665– 680. PMC7603796 32483333 source
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In the paper
“locally connected graph neural networks can be used to model the structure to sequence mapping problem because protein backbones provide a notion of local neighborhoods which primarily determine sequence identities.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
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In the paper
“This is, however, computationally intractable as it requires computing energies over all possible structures, including unwanted oligomeric and aggregated states”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
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In the paper
“ProteinMPNN requires no expert customization for specific design challenges, and it should thus make protein design more broadly accessible.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
plainconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In silico metrics such as native sequence recovery are very sensitive to crystallographic resolution (Figure S3 B, C) and may not correlate with proper folding”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“instead Rosetta and other approaches as a proxy carry out a search for the lowest energy sequence for a given backbone structure, and structure prediction calculations are required in a second step to confirm that there are no other structures in which the sequence has still lower energy.”
§ Robust deep learning based protein sequence design using ProteinMPNN > Conclusion
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The paper relies on this without support.
Evolution in most cases does not optimize for stability.
“as evolution in most cases does not optimize for stability”
The paper relies on this without support.
The backbones of previously failed designs are correct and designable targets, so failure was due to the sequence.
“we kept the backbones of the original designs fixed but discarded the original sequences and generated new ones using ProteinMPNN.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Second, protein design models have shown that new folds can be successfully predicted beyond the structural landscape explored by nature.249”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [249]
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Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Robust deep learning based protein sequence design using ProteinMPNN > Abstract
“the PDB split based on the CATH (7) protein classification”
§ Robust deep learning based protein sequence design using ProteinMPNN > References
“8. Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, & Thornton JM (1997). CATH–a hierarchic classification of protein domain structures.”
Citations
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Highly accurate protein structure prediction with AlphaFold
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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
- Published
- 2022-10-07
- Volume
- 378
- Issue
- 6615
- Pages
- 49-56
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC9997061
- OpenAlex
- W4296032638
- Semantic Scholar
- 98926d43356e87c22c82efc132dcaaac1ff40ebe
- Cited by
- 2,210
- References
- 35
- Citations by year
- 2026: 927 · 2025: 660 · 2024: 391 · 2023: 201 · 2022: 29
- FWCI
- 94.19
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, 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)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02)
- National Institute of General Medical Sciences (DE-AC02-05CH11231, GM124169-01, 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, sequence recovery, protein structure prediction
- 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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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