Pablo Gainza Cirauqui

Related publications (10)

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

New protein–protein interactions designed by a computer

Pablo Gainza Cirauqui, Anthony Marchand

Creating protein interactions through computational design is a key challenge in the fields of both basic and translational biology. An approach that uses the machine-learned fingerprints of protein-surface features was used to produce synthetic proteins that engage immunotherapeutic or viral targets with binding affinities comparable to those of naturally occurring proteins.

2023

Rational Design of Chemically Controlled Antibodies and Protein Therapeutics

Lucia Bonati, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Sandrine Madeleine Suzanne Georgeon, Anthony Marchand, Stéphane Rosset, Leo Scheller, Sailan Shui, Li Tang

Protein-based therapeutics, such as monoclonal antibodies and cytokines, are important therapies for various pathophysiological conditions such as oncology, autoimmune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely, cytokine storm syndrome, organ failure, and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small-molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used the Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon the addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into anti-CTLA4, anti-HER2 antibodies, or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon the addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

2023

A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy

Jaume Bonet Martinez, George Coukos, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Sailan Shui, Sabrina Susan Vollers

The activity of CAR-T cells is reversibly halted with a small-molecule drug. Approaches to increase the activity of chimeric antigen receptor (CAR)-T cells against solid tumors may also increase the risk of toxicity and other side effects. To improve the safety of CAR-T-cell therapy, we computationally designed a chemically disruptable heterodimer (CDH) based on the binding of two human proteins. The CDH self-assembles, can be disrupted by a small-molecule drug and has a high-affinity protein interface with minimal amino acid deviation from wild-type human proteins. We incorporated the CDH into a synthetic heterodimeric CAR, called STOP-CAR, that has an antigen-recognition chain and a CD3 zeta- and CD28-containing endodomain signaling chain. We tested STOP-CAR-T cells specific for two antigens in vitro and in vivo and found similar antitumor activity compared to second-generation (2G) CAR-T cells. Timed administration of the small-molecule drug dynamically inactivated the activity of STOP-CAR-T cells. Our work highlights the potential for structure-based design to add controllable elements to synthetic cellular therapies.

2020

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen

Jaume Bonet Martinez, Patricia Corthésy Henrioud, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Sai Reddy, Stéphane Rosset, Fabian Sesterhenn, Sabrina Susan Vollers, Che Yang

Throughout the last several decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g., respiratory syncytial virus [RSV], influenza, dengue, and others) have resisted vaccine development efforts, largely because of the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

2019

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Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein’s modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein–protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein–protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

2019

Decrypting interaction fingerprints in protein molecular surfaces

Michael Bronstein, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Freyr Sverrisson

SPRINGER2019

Heterodimeric inactivatable chimeric antigen receptors

George Coukos, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui

The invention relates to heterodimeric inactivatable chimeric antigen receptors (CARs) and their use for treatment.

2019

Computational protein design - the next generation tool to expand synthetic biology applications

Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui

2018

OFF-switch CAR T cell for safety-enhanced cancer immunotherapy

George Coukos, Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Sailan Shui, Sabrina Susan Vollers

AMER ASSOC CANCER RESEARCH2018