De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Discrimination among protein variants using an unfoldase-coupled nanopore.

Previously we showed that the protein unfoldase ClpX could facilitate translocation of individual proteins through the α-hemolysin nanopore. This results in ionic current fluctuations that correlate with unfolding and passage of intact protein strands through the pore lumen. It is plausible that this technology could be used to identify protein domains and structural modifications at the single-molecule level that arise from subtle changes in primary amino acid sequence (e.g., point mutations). As a test, we engineered proteins bearing well-characterized domains connected in series along an ∼700 amino acid strand. Point mutations in a titin immunoglobulin domain (titin I27) and point mutations, proteolytic cleavage, and rearrangement of beta-strands in green fluorescent protein (GFP), caused ionic current pattern changes for single strands predicted by bulk phase and force spectroscopy experiments. Among these variants, individual proteins could be classified at 86-99% accuracy using standard machine learning tools. We conclude that a ClpXP-nanopore device can discriminate among distinct protein domains, and that sequence-dependent variations within those domains are detectable.

Unfoldase-mediated protein translocation through an α-hemolysin nanopore.

Using nanopores to sequence biopolymers was proposed more than a decade ago. Recent advances in enzyme-based control of DNA translocation and in DNA nucleotide resolution using modified biological pores have satisfied two technical requirements of a functional nanopore DNA sequencing device. Nanopore sequencing of proteins was also envisioned. Although proteins have been shown to move through nanopores, a technique to unfold proteins for processive translocation has yet to be demonstrated. Here we describe controlled unfolding and translocation of proteins through the α-hemolysin (α-HL) pore using the AAA+ unfoldase ClpX. Sequence-dependent features of individual engineered proteins were detected during translocation. These results demonstrate that molecular motors can reproducibly drive proteins through a model nanopore--a feature required for protein sequence analysis using this single-molecule technology.