Massively parallel de novo protein design for targeted therapeutics

By A. Chevalier, Daniel‐Adriano Silva, G. Rocklin, Derrick R. Hicks, R. Vergara, Patience Murapa, S. Bernard, Lu Zhang, K. Lam, G. Yao and 18 more. 2017

438 citations recorded by the source corpus.

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Subfields: Geography, Planning and Development

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Citations in the corpus

Scaffolding protein functional sites using deep learning (2022, background): “…acid identities which produce the desired activity--for enzymes this can be done using quantum chemistry calculations (1–3) and for protein binders by fragment docking calculations (4, 5); alternatively, functional sites can be extracted from a native protein having the desired activity (6, 7).”
Essentials of de novo protein design: Methods and applications (2018, background): “37 Finally, Chevalier et al. introduced disulfide bonds into the massive design of miniprotein binders, 46 and the subsequent analysis of the massive dataset allowed to determine that designed disulfide bridges can effectively stabilize de novo binders as long as its introduction results in a…”
Deep learning methods for designing proteins scaffolding functional sites (2021, background): “the enzyme case (to identify ideal theozymes for catalyzing a desired reaction) (1–3) or fragment docking calculations in the protein binder case (4, 5); alternatively functional sites can”
De novo protein design, a retrospective (2020, methodology): “Combining computational design with experimental library screening to achieve function In another pioneering contribution, Baker and coworkers (Chevalier et al., 2017) integrated large-scale computational design, parallel oligonucleotide synthesis, yeast display screening, and next-generation…”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Exciting recently designed functions include inhibitors of viral infections (3, 4), immune system modulators (5, 6), self-assembling biomaterials (7 – 9), sense-and-respond signaling systems (10 – 13), and protein logic gates (14, 15).”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, positive): “Themini- mizeddockedstrandswerethengeometri-callymatchedtothescaffoldlibraryusingthe MotifGraftMovertocreateadockedprotein-proteincomplex( 36 ).”
Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, background): “First, a dataset of miniprotein binders designed to bind the Botulinum neurotoxin B (BoNT/B) (Chevalier et al., 2017) (“BoNT dataset”).”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, methodology): “This approach has been successfully applied to the redesign of various protein systems [4–8], and holds tremendous potential for the development of novel therapeutics, enzymes, and other useful proteins.”