Back

Hierarchical design of pseudosymmetric protein nanoparticles

Dowling, Q.; Park, Y.-J.; Gerstenmaier, N.; Yang, E.; Wargacki, A.; Hsia, Y.; Fries, C.; Ravichandran, R.; Walkey, C.; Burrell, A.; Veesler, D.; Baker, D.; King, N.

2023-06-18 bioengineering
10.1101/2023.06.16.545393 bioRxiv
Show abstract

Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions 1-3. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry 4,5. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 2%
35.5%
Similar papers in this journal
2
Science Advances
● 1243 papers in training set
Top 1%
9.6%
Similar papers in this journal
3
Science
● 477 papers in training set
Top 0.9%
7.7%
Similar papers in this journal
50% of probability mass above
4
Cell
○ 431 papers in training set
Top 1%
5.4%
Similar papers in this journal
5
ACS Nano
● 113 papers in training set
Top 0.4%
5.1%
Similar papers in this journal
6
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 12%
4.2%
7
Nature Chemical Biology
○ 119 papers in training set
Top 0.6%
4.2%
8
Nature Methods
○ 385 papers in training set
Top 3%
2.7%
9
Nature
○ 645 papers in training set
Top 5%
2.4%
10
Nature Chemistry
○ 42 papers in training set
Top 0.4%
2.3%
11
Nature Nanotechnology
○ 32 papers in training set
Top 0.4%
1.7%
12
eLife
● 5828 papers in training set
Top 54%
1.5%
13
Neuron
○ 337 papers in training set
Top 4%
1.5%
14
Nature Materials
○ 28 papers in training set
Top 0.4%
1.1%
15
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.1%
16
Nature Neuroscience
○ 252 papers in training set
Top 4%
1.1%
17
Nature Biotechnology
○ 172 papers in training set
Top 4%
1.0%
18
Cell Research
○ 51 papers in training set
Top 1%
1.0%
19
Advanced Science
○ 286 papers in training set
Top 10%
0.8%
20
Communications Biology
○ 993 papers in training set
Top 31%
0.8%
21
Nano Letters
● 71 papers in training set
Top 2%
0.6%
22
Structure
○ 193 papers in training set
Top 3%
0.6%
23
ACS Central Science
● 71 papers in training set
Top 2%
0.6%
24
Cell Reports
○ 1498 papers in training set
Top 29%
0.6%
25
Nature Cell Biology
○ 118 papers in training set
Top 4%
0.6%
26
Angewandte Chemie International Edition
○ 93 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.