Alexandra Krina Van Hall-Beauvais

Related publications (4)

De novo design of protein interactions with learned surface fingerprints

Michael Bronstein, Stephen M Buckley, Bruno Emanuel Ferreira De Sousa Correia, Beat Fierz, Pablo Gainza Cirauqui, Sandrine Madeleine Suzanne Georgeon, Casper Alexander Goverde, Zander Harteveld, Anthony Marchand, Jane Marsden, Dongchun Ni, Elisa Oricchio, Aaron Simone Petruzzella, Charlène Mireille Raymonde Raclot, Stéphane Rosset, Andreas Scheck, Henning Paul-Julius Stahlberg, Freyr Sverrisson, Alexandra Teslenko, Didier Trono, Priscilla Turelli, Alexandra Krina Van Hall-Beauvais, Sarah Wehrle

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 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. 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 interactions10. 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.

2023

Proteins control nearly every facet of life on a molecular level. Proteins are formed from linear strings of amino acids, which fold into three-dimensional structures that can enact functions. Evolution has created highly efficient proteins in diverse folding patterns with a variety of functions. Despite this diversity, nature has sampled only a limited portion of the potential sequence/fold space, leaving a vast space that could contain novel folds or potential functions. Use of proteins in biotechnological and therapeutic fields has advanced in the past few decades, and with it the desire to design novel proteins with a given structure or function. Modern de novo design methods strive to design new proteins that fold into novel structures and/or have novel functions, be it enzymatic activity or binding to a target. Despite rapid advancements, many challenges still exist in the field. Previous efforts to design diverse backbones that fold into specific structures have faced difficulties. The design of novel binding partners for a specific target with no known binders can be challenging, and many methods face extremely low success rates and low affinity outcomes. The projects described in this thesis seek to address these limitations and expand the ability of protein engineers to design proteins.We developed Genesis, a program that allows for the prediction of protein sequence from a desired structure. We utilized Genesis in conjunction with trRosetta to design sequences that fold into native (existing in nature) folds and tested these using a high throughput assay. We then designed darkfolds, novel folds that have not been seen before in nature that could be given novel functions. We tested these folds for stability to proteolytic cleavage and produced the most stable designs to test biochemically. We were able to produce several darkfold proteins and found them to be folded and stable. Additionally, analysis of the high throughput assay allowed us to draw conclusions from the design process.Beyond structure, proteins are endowed with function. These functions require interaction with DNA, lipids, small molecules, and importantly, other proteins. Protein-protein interactions (PPIs) can be transient low-affinity interactions or long-lasting high-affinity partnerships. PPIs form by two complementary surfaces, generally hydrophobic patches, coming together and excluding water molecules from the buried interface. Dysregulation of certain PPIs perturbs cellular homeostasis and can lead to disease. Conversely, controlling specific PPIs can be useful for therapeutics and in cellular assays. MaSIF surface fingerprinting software utilizes deep learning to find complementary surface patches to a given target site of interest. In this thesis, MaSIF is used to design protein binders for therapeutically relevant targets. The binders were then optimized and validated experimentally. This project netted four site-specific protein binders: one for SARS-CoV-2 spike protein that was found to be neutralizing in cellular assays, two for PD-L1 and one for PD-1. Structures of the design bound to the target were solved for three of the four binders, highlighting the success of the method. This thesis addresses the challenge of designing de novo proteins with novel folds and designing protein binding partners to specific sites. These projects will allow for additional capability for protein engineers to be able to create functional novel proteins.

EPFL2023

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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.

2022

The mRNA-Binding Protein HuR Is a Kinetically-Privileged Electrophile Sensor

Yimon Aye, Jesse Poganik, Alexandra Krina Van Hall-Beauvais, Yi Zhao

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.

2020

EPFL2023