Biology subjects

Van Hall-Beauvais, A.

Publications and source records attributed to Van Hall-Beauvais, A..

4 recordsLinked to original sources

bioRxiv2023

Exploring "dark matter" protein folds using deep learning

De novo protein design aims to explore uncharted sequence-and structure areas to generate novel proteins that have not been sampled by evolution. One of the main challenges in de novo design involves crafting "designable" structural templates that can guide the sequence search towards adopting the target structures. Here, we present an approach to learn patterns of protein structure based on a convolutional variational autoencoder, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance-and angle distributions for five native folds and three novel, so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize protease resistance of the designs, obtaining encouraging success rates for folded proteins and further biochemically characterized folded designs. The Genesis framework enables the exploration of the protein sequence and fold space within minutes and is not bound to specific protein topologies. Our approach addresses the backbone designability problem, showing that structural patterns in proteins can be efficiently learned by small neural networks and could ultimately contribute to the de novo design of proteins with new functions.

bioinformatics↗

bioRxiv2022

Z-REX uncovers a bifurcation in function of Keap1 paralogs

Studying electrophile signaling is marred by difficulties in parsing changes in pathway flux attributable to on-target, vis-a-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, wherein following electrophile treatment: (1) zKeap1b-transfected cells are permissive for augmented AR-signaling through reduced zKeap1b-Nrf2 binding; (2) zKeap1a-transfected cells are non-permissive for AR-upregulation, as zKeap1a-Nrf2 binding capacity remains unaltered; (3) 1:1 ZKeap1a:zKeap1b-transfected cells show no Nrf2-release from the Keap1-complex, rendering these cells unable to upregulate AR. We identified a zKeap1a-specific point-mutation (C273I) responsible for zKeap1as behavior. 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. Impact statementHow electrophile-sensing versus -signaling marshal stress responses: two zebrafish Keap1-paralogs are equally adept at electrophile-sensing but manifest divergent and co-regulatory electrophile-signaling behaviors.

pharmacology and toxicology↗

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized 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 while others were purely generated 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 a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗

bioRxiv2020

The mRNA-binding protein HuR is a kinetically-privileged electrophile sensor

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.

biochemistry↗