Designed proteins assemble antibodies into modular nanocages

Divine, Robby; Dang, Ha V.; Ueda, George; Fallas, Jorge A.; Vulovic, Ivan; Sheffler, William; Saini, Shally; Zhao, Yan Ting; Raj, Infencia Xavier; Morawski, Peter A.; Jennewein, Madeleine F.; Homad, Leah J.; Wan, Yu; Tooley, Marti R.; Seeger, Franziska; Etemadi, Ali; Fahning, Mitchell L.; Lazarovits, James; Roederer, Alex; Walls, Alexandra C.; Stewart, Lance; Mazlomi, Mohammad Ali; King, Neil P.; Campbell, Daniel; McGuire, Andrew T.; Stamatatos, Leonidas; Ruohola‐Baker, Hannele; Mathieu, Julie; Veesler, David; Baker, David

doi:10.1126/science.abd9994

Cited by 125 publications

(108 citation statements)

References 74 publications

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“…Essential to the ability to accurately design such systems are the symmetry and cooperativity of assembly, which result in the strong favoring of just one of the very large number of possible states. Once formed, these assemblies are therefore typically quite static and exchange subunits only on very long time scales, which is advantageous for applications such as nanoparticle vaccine design and multivalent receptor engagement ( 9 ).…”

Section: Main Textmentioning

confidence: 99%

“…Using the relaxed crystal structures of LHD29 and LHD101 fused to their respective DHRs, the WORMS software ( 7, 9, 33 ) was used to fuse the two hetero-dimers into cyclic symmetrical rings. As one construct has exposed N-termini and the other has exposed C-termini, they were able to be fused head to tail without introduction of further building blocks.…”

Section: Supplementary Materials Formentioning

confidence: 99%

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Reconfigurable asymmetric protein assemblies through implicit negative design

Sahtoe

Praetorius

Courbet

et al. 2021

Preprint

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Asymmetric multi-protein complexes that undergo subunit exchange play central roles in biology, but present a challenge for protein design. The individual components must contain interfaces enabling reversible addition to and dissociation from the complex, but be stable and well behaved in isolation. Here we employ a set of implicit negative design principles to generate beta sheet mediated heterodimers which enable the generation of a wide variety of structurally well defined asymmetric assemblies. Crystal structures of the heterodimers are very close to the design models, and unlike previously designed orthogonal heterodimer sets, the subunits are stable, folded and monomeric in isolation and rapidly assemble upon mixing. Rigid fusion of individual heterodimer halves to repeat proteins yields central assembly hubs that can bind two or three different proteins across different interfaces. We use these connectors to assemble linearly arranged hetero-oligomers with up to 6 unique components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub, and demonstrate such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

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Section: Main Textmentioning

confidence: 99%

Section: Supplementary Materials Formentioning

confidence: 99%

Reconfigurable asymmetric protein assemblies through implicit negative design

Sahtoe

Praetorius

Courbet

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stable protein super-assemblies have recently been designed and engineered to form functional nanodevices such as nano-cages for therapeutic applications [1][2][3][4]. To increase the number and the complexity of these super-assemblies, mechanically stable building blocks are prerequisites.…”

Section: Introductionmentioning

confidence: 99%

Classifying Residues in Mechanically Stable and Unstable Substructures Based on a Protein Sequence: The Case Study of the DnaK Hsp70 Chaperone

Gala

2021

Nanomaterials

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Artificial proteins can be constructed from stable substructures, whose stability is encoded in their protein sequence. Identifying stable protein substructures experimentally is the only available option at the moment because no suitable method exists to extract this information from a protein sequence. In previous research, we examined the mechanics of E. coli Hsp70 and found four mechanically stable (S class) and three unstable substructures (U class). Of the total 603 residues in the folded domains of Hsp70, 234 residues belong to one of four mechanically stable substructures, and 369 residues belong to one of three unstable substructures. Here our goal is to develop a machine learning model to categorize Hsp70 residues using sequence information. We applied three supervised methods: logistic regression (LR), random forest, and support vector machine. The LR method showed the highest accuracy, 0.925, to predict the correct class of a particular residue only when context-dependent physico-chemical features were included. The cross-validation of the LR model yielded a prediction accuracy of 0.879 and revealed that most of the misclassified residues lie at the borders between substructures. We foresee machine learning models being used to identify stable substructures as candidates for building blocks to engineer new proteins.

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“… 5 Notably, Stamatatos et al also show that B.1.351 neutralizing titers in previously infected vaccinees after a single dose were comparable to or higher than autologous neutralizing titers in previously naïve vaccinees after two doses of mRNA vaccine. 3 This result is significant given that the latter is associated with greater than 90% protective efficacy against COVID-19 in clinical trials. 6 , 7 …”

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confidence: 99%

“… 2 In a recent report in Science , Stamatatos et al investigate neutralizing antibody responses against B.1.351 strains in the sera of naïve and previously infected recipients of BNT162b2 or mRNA-1273. 3 The B.1.351 lineage encompasses the following spike protein amino acid substitutions: K417N, E484K, and N501Y in the receptor-binding domain (RBD); D614G and frequently A701V in S2; D80A, D215G, and occasionally a 242–244 deletion in the N-terminal domain (NTD). 1 , 4 In their experiments, the authors assess the susceptibility to neutralization of a pseudovirus expressing B.1.351 spike in comparison to a vaccine-matched Wuhan-Hu-1 pseudovirus.…”

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confidence: 99%