CoChat
Sign Up
PreprintCited by 19Protein Structure and DynamicsEnzyme Structure and FunctionBiochemical and Structural Characterization

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

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Open access
Meador, Kyle, Castells-Graells, Roger, Aguirre, Roman, Sawaya, Michael R., Arbing, Mark A., Sherman, Trent, Senarathne, Chethaka, Yeates, Todd O.·2023·Structure
DOI 10.1101/2023.10.09.561468PMC PMC11162342

Preprint — published as 10.1016/j.str.2024.02.017 · Paper page

Open this paper in CoChat — free account

Abstract

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

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 · 10.1101/2023.10.09.561468

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: 84

  1. 10.1126/science.abj7662 · DOI
  2. 10.1126/science.add1964 · DOI · Paper page
  3. 10.1038/s41586-023-06415-8 · DOI · Paper page
  4. 10.1073/pnas.041614998 · DOI
  5. 10.1093/protein/gzab008 · DOI
  6. 10.1126/science.add2187 · DOI · Paper page
  7. 10.1101/2022.12.01.518682 · DOI · Paper page
  8. 10.1038/s41587-022-01618-2 · DOI · Paper page
  9. 10.1038/s41586-020-03120-8 · DOI
  10. 10.1016/j.cell.2019.01.046 · DOI
  11. 10.3390/biom13071122 · DOI
  12. 10.1038/s41541-020-00223-1 · DOI
  13. 10.1038/s41586-021-03530-2 · DOI
  14. 10.1038/s41586-021-03365-x · DOI
  15. 10.1021/jacs.9b13462 · DOI
  16. 10.1021/acssynbio.9b00407 · DOI
  17. 10.1038/s41598-019-51017-y · DOI
  18. 10.1073/pnas.2305494120 · DOI · Paper page
  19. 10.1128/mr.53.2.242-255.1989 · DOI
  20. 10.1126/science.1113397 · DOI
  21. 10.1021/bi900641c · DOI
  22. 10.1073/pnas.1201504109 · DOI
  23. 10.1016/j.cell.2013.10.044 · DOI
  24. 10.1016/j.cell.2017.12.024 · DOI · Paper page
  25. 10.1038/s41467-021-21251-y · DOI
  26. 10.1126/science.abm1183 · DOI
  27. 10.7554/elife.57659 · DOI
  28. 10.1038/nchem.2107 · DOI
  29. 10.1038/s41586-023-05993-x · DOI
  30. 10.1016/j.jmb.2010.09.028 · DOI · Paper page
  31. 10.1016/j.jmb.2007.03.069 · DOI
  32. 10.1002/pro.5560060901 · DOI
  33. 10.1016/j.jmb.2011.09.031 · DOI
  34. 10.1002/pro.2187 · DOI
  35. 10.1002/pro.2610 · DOI
  36. 10.1021/acs.jctc.7b00125 · DOI · Paper page
  37. 10.1002/prot.26030 · DOI · Paper page
  38. 10.1016/j.jmb.2003.12.073 · DOI
  39. 10.1002/pro.3802 · DOI
  40. 10.1021/acs.jctc.8b00033 · DOI
  41. 10.1002/pro.5560020916 · DOI
  42. 10.1038/nature13404 · DOI
  43. 10.1126/science.aaf8818 · DOI
  44. 10.1006/jmbi.1993.1648 · DOI
  45. 10.1002/pro.604 · DOI
  46. 10.1016/j.jmb.2010.05.006 · DOI
  47. Ingraham, J. , Garg, V.K. , Barzilay, R. , and Jaakkola, T. (2019). Generative models for graph-based protein design. Advances in neural information processing systems.
  48. 10.1038/s41467-023-38328-5 · DOI · Paper page
  49. 10.1002/pro.2792 · DOI
  50. 10.1016/j.str.2021.07.012 · DOI
  51. 10.1093/bioinformatics/btm274 · DOI
  52. 10.1126/science.1179513 · DOI
  53. 10.1002/pro.3941 · DOI
  54. 10.1016/S0006-3495(02)75245-4 · DOI
  55. 10.1006/smvy.1997.0102 · DOI
  56. 10.1016/j.str.2009.06.013 · DOI
  57. 10.1073/pnas.1908723117 · DOI
  58. 10.1101/2023.08.04.551935 · DOI · Paper page
  59. 10.1371/journal.pcbi.1006112 · DOI · Paper page
  60. 10.1371/journal.pcbi.1010633 · DOI · Paper page
  61. 10.1038/nmeth.4510 · DOI
  62. 10.1371/journal.pone.0059004 · DOI
  63. 10.1371/journal.pone.0268253 · DOI
  64. 10.12688/f1000research.7931.1 · DOI
  65. 10.1002/prot.340230412 · DOI
  66. 10.1093/bioinformatics/btt473 · DOI · Paper page
  67. 10.1073/pnas.1617873114 · DOI
  68. 10.1038/nmeth.1818 · DOI · Paper page
  69. 10.1093/bioinformatics/btq404 · DOI · Paper page
  70. 10.1093/nar/25.17.3389 · DOI · Paper page
  71. 10.1073/pnas.1321126111 · DOI
  72. 10.1371/journal.pone.0020450 · DOI
  73. 10.1038/s41592-022-01488-1 · DOI · Paper page
  74. 10.1016/j.pep.2005.01.016 · DOI
  75. 10.1016/j.jsb.2005.07.007 · DOI
  76. 10.1038/nmeth.4169 · DOI · Paper page
  77. 10.1107/S0907444910007493 · DOI · Paper page
  78. 10.1107/s2059798319011471 · DOI · Paper page
  79. 10.1002/pro.3943 · DOI
  80. 10.1002/pro.3235 · DOI · Paper page
  81. 10.1107/S0021889807021206 · DOI · Paper page
  82. 10.1107/s2059798318006551 · DOI
  83. 10.1107/S0907444904019158 · DOI · Paper page
  84. 10.1107/s1600576720013412 · DOI

Metadata

Authors
Meador, K., Castells-Graells, R., Aguirre, R., Sawaya, M. R., Arbing, M. A., Sherman, T., Senarathne, C., & Yeates, T. O.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Yeates, Todd O.
ORCID
Venue
Structure
Publisher
openRxiv
Published
2023-10-09
Volume
32
Issue
6
Pages
751-765.e11
ISSN
0969-2126 (ISSN-L)
DOI
10.1101/2023.10.09.561468
PMC
PMC11162342
OpenAlex
W4392976128
Cited by
19
References
1
Citations by year
2026: 3 · 2025: 12 · 2024: 4
FWCI
2.58
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Structure (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • U.S. Department of Energy (76SF00515, DE–AC02–76SF00515, AC02‐76SF00515, P30-GM133894, DEFC0202ER63421, FC02-02ER63421, DE-AC02-)
  • Oregon Health and Science University
  • National Institutes of Health (DE-FC02-02ER63421, 1s10od021512-01, DE-AC02-76SF00515)
  • Office of Science (AC02-76SF00515, 02ER63421, DE-AC02-76SF0051 5, DE-AC02, DEFC0202ER63421, 76SF00515)
  • University of California, Los Angeles (DE-FC02-02ER63421, U24GM129547)
  • U.S. Public Health Service (T32GM008496)
  • National Institute of General Medical Sciences (T32GM008496, S10OD021512, U24GM129541, R01GM129854, DE-AC02-76SF00515, U24GM129547, P30GM133894, P30GM124165)
  • Basic Energy Sciences (DE-AC02, DE-FC02-02ER63421, 76SF00515, AC02-76SF00515, DE-AC-02-76SF00515)
  • Biological and Environmental Research (DE‐FC02‐02ER63421, DE- AC0276SF00515, P30GM133894)
  • California NanoSystems Institute
  • SLAC National Accelerator Laboratory (DEAC02−76SF00515, AC02‐76SF00515)
  • Common Fund
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
Keywords
protein cages, protein self-assembly, tetrahedral symmetry, ProteinMPNN, knowledge-based design, protein docking

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