CoChat
Sign Up
PreprintCited by 42Protein Structure and DynamicsBiochemical and Structural CharacterizationMachine Learning in Bioinformatics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

Open access
de Haas, Robbert J., Brunette, Natalie, Goodson, Alex, Dauparas, Justas, Yi, Sue Y., Yang, Erin C., Dowling, Quinton, Nguyen, Hannah, Kang, Alex, Bera, Asim K., Sankaran, Banumathi, de Vries, Renko, Baker, David, King, Neil P.·2023·Proceedings of the National Academy of Sciences
DOI 10.1101/2023.08.04.551935PMC PMC10990136

Preprint — published as 10.1073/pnas.2314646121 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2023.08.04.551935

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 70

  1. 10.1073/pnas.1914677117 · DOI · Paper page
  2. 10.1126/science.abj8754 · DOI · Paper page
  3. 10.1126/science.ade2574 · DOI · Paper page
  4. 2021, De novo protein design by deep network hallucination, Nature, 600, 547, 10.1038/s41586-021-04184-w · DOI · Paper page
  5. 10.1126/science.abn2100 · DOI · Paper page
  6. 10.1038/s41586-023-05696-3 · DOI
  7. 10.1101/2022.12.01.518682 · DOI · Paper page
  8. 10.1101/2023.02.24.529906 · DOI
  9. 2023, Top-down design of protein architectures with reinforcement learning, Science, 380, 266, 10.1126/science.adf6591 · DOI
  10. 10.1126/science.add1964 · DOI · Paper page
  11. 2022, Publisher Correction: Rotamer-free protein sequence design based on deep learning and self-consistency, Nature Computational Science, 2, 526, 10.1038/s43588-022-00305-1 · DOI
  12. 2021, Expanding functional protein sequence spaces using generative adversarial networks, Nat. Mach. Intell, 3, 324, 10.1038/s42256-021-00310-5 · DOI · Paper page
  13. 10.1101/2022.04.10.487779 · DOI
  14. 10.1126/science.add2187 · DOI · Paper page
  15. Ingraham, John and Garg, Vikas and Barzilay, Regina and Jaakkola, Tommi. Generative Models for Graph-Based Protein Design . Adv. Neural Inf. Process. Syst. 32, (2019).
  16. 10.1038/s41467-021-27838-9 · DOI · Paper page
  17. 2023, Improving de novo protein binder design with deep learning, Nat. Commun, 14, 2625, 10.1038/s41467-023-38328-5 · DOI · Paper page
  18. 10.1038/nature13404 · DOI
  19. 10.1126/science.1233936 · DOI
  20. 10.1038/s41467-019-12080-1 · DOI
  21. 2021, Structure-based design of novel polyhedral protein nanomaterials, Curr. Opin. Microbiol, 61, 51, 10.1016/j.mib.2021.03.003 · DOI
  22. 10.1002/pro.2187 · DOI
  23. 10.1126/science.aaf8818 · DOI
  24. 10.1038/s41467-020-20241-w · DOI · Paper page
  25. 10.1073/pnas.2015037118 · DOI
  26. 2021, Complete and cooperative in vitro assembly of computationally designed self-assembling protein nanomaterials, Nat. Commun, 12, 883, 10.1038/s41467-021-21251-y · DOI
  27. 10.1038/nature06942 · DOI
  28. 10.1021/bi0261645 · DOI
  29. 10.1016/j.cell.2020.10.043 · DOI · Paper page
  30. 2023, Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock, PLoS Comput. Biol, 19, e1010680, 10.1371/journal.pcbi.1010680 · DOI
  31. 2021, Quadrivalent influenza nanoparticle vaccines induce broad protection, Nature, 592, 623, 10.1038/s41586-021-03365-x · DOI
  32. 2021, Elicitation of broadly protective sarbecovirus immunity by receptor-binding domain nanoparticle vaccines, Cell, 184, 5432, 10.1016/j.cell.2021.09.015 · DOI · Paper page
  33. 10.1016/j.cell.2021.01.035 · DOI
  34. 2022, Induction of cross-neutralizing antibodies by a permuted hepatitis C virus glycoprotein nanoparticle vaccine candidate, Nat. Commun, 13, 7271, 10.1038/s41467-022-34961-8 · DOI
  35. 10.1038/nature20108 · DOI
  36. 10.1126/science.1219364 · DOI
  37. 10.1021/jacs.8b06442 · DOI
  38. 10.1038/nature25157 · DOI
  39. 10.1038/s41467-020-19112-1 · DOI
  40. 10.1038/s41467-019-09836-0 · DOI
  41. 10.1073/pnas.1718825115 · DOI
  42. 10.1101/2022.09.18.508009 · DOI
  43. Ueda, G. et al. Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens. Elife 9, (2020). · DOI
  44. 10.1021/acsnano.8b02805 · DOI
  45. 2021, Overcoming Symmetry Mismatch in Vaccine Nanoassembly through Spontaneous Amidation, Angew. Chem. Int. Ed Engl, 60, 321, 10.1002/anie.202009663 · DOI
  46. 10.1016/j.cell.2019.01.046 · DOI
  47. 2022, Safety and immunogenicity of a SARS-CoV-2 recombinant protein nanoparticle vaccine (GBP510) adjuvanted with AS03: A randomised, placebo-controlled, observer-blinded phase 1/2 trial, EClinicalMedicine, 51, 101569, 10.1016/j.eclinm.2022.101569 · DOI
  48. 2022, Deep learning and protein structure modeling, Nat. Methods, 19, 13, 10.1038/s41592-021-01360-8 · DOI
  49. 2022, Controllable protein design with language models, Nature Machine Intelligence, 4, 521, 10.1038/s42256-022-00499-z · DOI
  50. 2022, Protein sequence design by deep learning, Nature Computational Science, 2, 416, 10.1038/s43588-022-00274-5 · DOI
  51. Elnaggar, A. et al. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. https://ieeexplore.ieee.org/document/9477085.
  52. Nijkamp, E. , Ruffolo, J. , Weinstein, E. N. , Naik, N. & Madani, A. ProGen2: Exploring the Boundaries of Protein Language Models. (2022). · DOI · Paper page
  53. 10.1101/2023.07.05.547496 · DOI
  54. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. U. S. A. 118, (2021). · DOI · Paper page
  55. 10.1101/2023.06.16.545341 · DOI
  56. 10.1101/2023.06.16.545393 · DOI
  57. 10.1101/2023.04.07.535760 · DOI
  58. 2019, Building machines with DNA molecules, Nat. Rev. Genet, 21, 5
  59. 10.1021/jacs.9b05626 · DOI
  60. 2017, DNA nanotechnology, Nature Reviews Materials, 3, 1
  61. 2020, Anisotropic reticular chemistry, Nature Reviews Materials, 5, 764, 10.1038/s41578-020-0225-x · DOI
  62. 10.1385/1-59259-890-0:571 · DOI
  63. 10.1038/nmeth.4169 · DOI · Paper page
  64. 10.1107/S0907444909047337 · DOI · Paper page
  65. 10.1107/S0907444910045749 · DOI · Paper page
  66. 10.1107/S0021889807021206 · DOI · Paper page
  67. 10.1107/S0907444909052925 · DOI · Paper page
  68. 10.1107/S0907444904019158 · DOI · Paper page
  69. 10.1002/pro.3330 · DOI · Paper page
  70. 10.1093/nar/28.1.235 · DOI · Paper page

Metadata

Authors
de Haas, R. J., Brunette, N., Goodson, A., Dauparas, J., Yi, S. Y., Yang, E. C., Dowling, Q., Nguyen, H., Kang, A., Bera, A. K., Sankaran, B., de Vries, R., Baker, D., & King, N. P.
Affiliations
  • Wageningen University & Research (NL)
  • University of Washington (US)
  • Lawrence Berkeley National Laboratory (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
King, Neil P.
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
openRxiv
Published
2023-08-04
Volume
121
Issue
13
Pages
e2314646121-e2314646121
ISSN
0027-8424 (ISSN-L)
DOI
10.1101/2023.08.04.551935
PMC
PMC10990136
OpenAlex
W4392946437
Cited by
42
References
1
Citations by year
2026: 14 · 2025: 17 · 2024: 11
FWCI
5.69
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Socio-Environmental Systems Modeling (submittedVersion, cc-by-nc-nd)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • PubMed Central (submittedVersion)
Funders
  • National Science Foundation (DEAC02-05CH11231, 1629214)
  • Howard Hughes Medical Institute (DE-AC02-05CH11231)
  • U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
  • Bill and Melinda Gates Foundation (INV-010680)
  • Pfizer
  • GlaxoSmithKline
  • University of Washington (WA 98195)
  • Open Philanthropy Project
  • National Institutes of Health (DE-AC02–05CH11231)
  • Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02)
  • Wageningen University and Research
  • National Institute of General Medical Sciences (DE-AC02-05CH11231)
  • National Institute of Allergy and Infectious Diseases (P01AI167966, U54AI170856)
  • Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., AC02–05CH11231)
  • Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, 05CH11231)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein sequence design, ProteinMPNN, deep learning, protein nanomaterials, self-assembly, in vitro assembly, crystal structure

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 ›

Are you an author, or do you see a problem with this page? Request a review