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.

The universally conserved enzyme CTP synthase (CTPS) forms filaments in bacteria and eukaryotes. In bacteria polymerization inhibits CTPS activity and is required for nucleotide homeostasis. Here we show that human CTPS polymerization increases catalytic activity. The cryoEM structures of bacterial and human CTPS filaments differ dramatically in overall architecture and in the conformation of the CTPS protomer, explaining the divergent consequences of polymerization on activity. The filament structure of human CTPS is the first full-length structure of the human enzyme and reveals a novel active conformation. The filament structures elucidate allosteric mechanisms of assembly and regulation that rely on a conserved conformational equilibrium. This may provide a mechanism for increasing human CTPS activity in response to metabolic state, and challenges the assumption that metabolic filaments are generally storage forms of inactivated enzymes. Allosteric regulation of CTPS polymerization by ligands likely represents a fundamental mechanism underlying assembly of other metabolic filaments.

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting,” starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.