Massively parallel de novo protein design for targeted therapeutics

Abstract

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.

Heterogeneous‐Backbone Foldamer Mimics of a Computationally Designed, Disulfide‐Rich Miniprotein

“…[20] This sequence was created as part of a broader effort to develop a family of hyperstable miniproteins as scaffolds for biomedical applications. [5] The fold of 1 is comprised of an α-helix and a β-hairpin stapled by two disulfide bonds (Cys 4−18 and Cys 14−27 ). Of note, 1 already contains a backbone alteration at a single site in the form of a D-Pro introduced to stabilize the hairpin turn.…”

Co‐Evolutionary Fitness Landscapes for Sequence Design

“…This can be achieved experimentally, by screening methods such as directed evolution [3] , as well as computationally by structure-based de novo protein design [4] . A variety of successful computational algorithms exist, of which the best known is ROSETTA, that exploit both the information in structural databases together with a combination of physics-based and knowledge-based energy functions [5] .…”

Structural insights into the design of novel anti-influenza therapies

“…The designs were then further optimized to produce a 51-residue protein (HB80.4) that was able to bind to all influenza A group 1 HAs and neutralize H1N1 viruses with potencies akin to the best bnAbs 73 . The most recent advance in these small protein designs came from using a massively parallel approach, whereby 22,600 mini-proteins with different backbone scaffolds of 37–43 residues were screened against influenza HA 74 . Binders with very high affinity (K d < 10 nM) and stability (T m > 95 ºC) were identified and, importantly, those designed proteins were not found to be immunogenic even after repeated injections in mice.…”

“…Four different designs that target the HA stem region, namely HB36.3 (PDB 3R2X) 72 , HB80.4 (PDB 4EEF) 73 , HB1.6928.2.3 (PDB 5VLI) 74 , and P7 (PDB 5W6T) 59 , along with CR9114 Fab (PDB 3GBM) 40 are shown in burgundy. HA is colored white.…”