Alexandra Van Hall‐Beauvais

Abstract Physical interactions between proteins are essential for most biological processes governing life 1 . However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications 2–9 . Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions 10 . We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Protein-protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly engineer PPIs has immense potential for the development of novel synthetic biology tools and protein-based therapeutics. Over the last decades, many efforts in this area have relied purely on experimental approaches, but more recently, computational protein design has made important contributions. Template-based approaches utilize known PPIs and transplant the critical residues onto heterologous scaffolds. De novo design instead uses computational methods to generate novel binding motifs, allowing for a broader scope of the sites engaged in protein targets. Here, we review successful design cases, giving an overview of the methodological approaches used for templated and de novo PPI design.

T-REX (targetable reactive electrophiles and oxidants) enables electrophile targeting in living systems with high spatiotemporal precision and at single-protein-target resolution. T-REX allows functional consequences of individual electrophile signaling events to be directly linked to on-target modifications. T-REX is accomplished by expressing a HaloTagged protein of interest (POI) and introducing a Halo-targetable bioinert photocaged precursor to a reactive electrophilic signal (RES). Light exposure releases the unfettered RES on demand, enabling precision modification of the POI due to proximity. Using alkyne-functionalized 4-hydroxynonenal (HNE) as a representative RES, this protocol delineates optimized strategies to (1) execute T-REX in live human cells and C. elegans, (2) quantitate the POI's RES-sensitivity by either azido-fluorescent-dye conjugation or (3) enrich using biotin-azide/streptavidin pulldown procedure in both model systems, and (4) identify the site of RES-labeling on the POI using proteomics. Built-in T-REX controls that allow users to directly confirm on-target/on-site specificity of RES-sensing are also described. © 2018 by John Wiley & Sons, Inc.

Studying electrophile signaling is marred by difficulties in parsing changes in pathway flux attributable to on-target, vis-à-vis off-target, modifications. By combining bolus dosing, knockdown, and Z-REX—a tool investigating on-target/on-pathway electrophile signaling, we document that electrophile labeling of one zebrafish-Keap1-paralog (zKeap1b) stimulates Nrf2- driven antioxidant response (AR) signaling (like the human-ortholog). Conversely, zKeap1a is a dominant-negative regulator of electrophile-promoted Nrf2-signaling, and itself is nonpermissive for electrophile-induced Nrf2-upregulation. This behavior is recapitulated in human cells: (1) zKeap1b-expressing cells are permissive for augmented AR-signaling through reduced zKeap1b–Nrf2 binding following whole-cell electrophile treatment; (2) zKeap1a-expressing cells are non-permissive for AR-upregulation, as zKeap1a–Nrf2 binding capacity remains unaltered upon whole-cell electrophile exposure; (3) 1:1 ZKeap1a:zKeap1b-co-expressing cells show no Nrf2-release from the Keap1-complex following whole-cell electrophile administration, rendering these cells unable to upregulate AR. We identified a zKeap1a-specific point-mutation (C273I) responsible for zKeap1a’s behavior during electrophilic stress. Human-Keap1(C273I), of known diminished Nrf2-regulatory capacity, dominantly muted electrophile-induced Nrf2-signaling. These studies highlight divergent and interdependent electrophile signaling behaviors, despite conserved electrophile sensing .

The key mRNA-binding proteins HuR and AUF1 are reported stress sensors in mammals. Intrigued by recent reports of sensitivity of these proteins to the electrophilic lipid prostaglandin A2 and other redox signals, we here examined their sensing abilities to a prototypical redox-linked lipid-derived electrophile, 4-hydroxynonenal (HNE). Leveraging our T-REX electrophile delivery platform, we found that only HuR, and not AUF1, is a kinetically-privileged sensor of HNE in HEK293T cells, and sensing functions through a specific cysteine, C13. Cells depleted of HuR, upon treatment with HNE, manifest unique alterations in cell viability and Nrf2-transcription-factor-driven antioxidant response (AR), which our recent work shows is regulated by HuR at the Nrf2-mRNA level. Mutagenesis studies showed that C13-specific sensing alone is not sufficient to explain HuR-dependent stress responsivities, further highlighting a complex context-dependent layer of Nrf2/AR regulation through HuR.