Throughout the PhD and postdoctoral training, Bruno Correia worked in leading laboratories in the United States, including the University of Washington and The Scripps Research Institute. These formative years taught him to approach scientific problems broadly and creatively while collaborating with exceptional scientists. Early on, he developed a fascination with protein structure and function. His PhD focused on immunogen design and vaccine engineering, sparking a lasting interest in vaccinology and translational research. Bruno pioneered computational, structure-guided strategies to create immunogens with enhanced immunogenicity, demonstrating for the first time that rationally designed antigens can elicit potent neutralizing antibodies.

Seeking to broaden his expertise, he joined a chemical biology laboratory at Scripps for his postdoctoral studies, where he became a proficient experimentalist and developed chemoproteomics methods to map protein–small molecule interactions in complex proteomes.

In 2015, Bruno Correia was appointed tenure-track associate professor at the École Polytechnique Fédérale de Lausanne (EPFL). His group develops computational tools for protein design, with an emphasis on immunoengineering for vaccines and cancer immunotherapy. They integrate method development with the biochemical and biophysical characterization of designed proteins. 

Orchid number: https://orcid.org/0000-0002-7377-8636
LinkedIn: https://www.linkedin.com/in/bruno-correia-23a1aa4/

A central focus of the research of Bruno Correia is the development of computational strategies that exploit structural information to design functional proteins. Much of this work has centered on immunogen design, but the same principles extend to other challenges, including the creation of protein inhibitors. His early efforts involved algorithms that searched structural databases for scaffolds with backbone conformations similar to epitopes of interest. Once identified, the epitope’s side chains were transplanted onto the scaffold (side-chain grafting). Although effective in some cases, this approach was limited because suitable scaffolds were unavailable for many targets. To address this, Bruno devised an algorithm that folds new proteins around an epitope using local loops as anchors. This innovation produced the most successful synthetic immunogens for respiratory syncytial virus (RSV) vaccines.

The Correia laboratory has expanded these methods through tools such as AlphaFold-inspired frameworks for designing de novo globular proteins and high-affinity protein interfaces.

The Correia lab has also applied rational design to cellular therapies. Chimeric antigen receptor T cells (CAR-Ts) achieve durable responses against B-cell malignancies but carry severe risks, including cytokine release syndrome. To address this, they created the STOP-CAR, a control system in which antigen recognition and signaling are encoded on two chains linked by a computationally designed heterodimer. A small molecule disrupts this dimer, transiently reducing T-cell activity without eliminating the therapy, unlike suicide switches. To advance clinical translation, the team developed chemically disruptable heterodimers (CDHs) from human proteins with minimal mutations, chosen for compatibility with well-tolerated drugs of long half-life. STOP-CARs responded dynamically to drug administration in cellular assays and tumor models, underscoring how structure-based design can improve safety and function in cell therapies.

A further research direction addresses how to predict protein interactions directly from structure. Protein molecular surfaces encode patterns of chemical and geometric features that act as “fingerprints” of binding behavior, but these are difficult to discern by inspection. The Correia lab therefore introduced MaSIF (Molecular Surface Interaction Fingerprinting), a geometric deep-learning framework that decomposes a surface into overlapping patches and learns descriptors capturing their interaction signatures. This interdisciplinary project exemplifies the group’s strength in combining expertise from disparate fields to innovate in protein science. MaSIF has since been extended to the de novo design of protein–protein and ligand-mediated interactions, addressing long-standing challenges in computational modeling.

Together, these contributions chart a trajectory from scaffold-based grafting to generative algorithms, from immunogen discovery to controllable cell therapies, and from visual inspection to deep-learning representations of molecular surfaces. They illustrate how rational, structure-guided design can broaden the repertoire of tools available for vaccines, therapeutics, and synthetic biology.

Publication Highlights

BindCraft: one-shot design of functional protein binders. Pacesa M*, Nickel L*, Schellhaas C*, Schmidt J, Pyatova E, Kissling L, Barendse P, Choudhury J, Kapoor S, Alcaraz-Serna A, Cho Y, Ghamary KH, Vinué L, Yachnin BJ, Wollacott AM, Buckley S, Westphal AH, Lindhoud S, Georgeon S, Goverde CA, Hatzopoulos GN, Gönczy P, Muller YD, Schwank G, Swarts DC, Vecchio AJ, Schneider BL, Ovchinnikov S*, Correia BE*. Nature 2025. doi: 10.1038/s41586-025-09429-6

De novo design of protein interactions with learned surface fingerprints. Gainza P, Wehrle S, Van Hall-Beauvais A, Marchand A, Scheck A, Harteveld Z, Buckley S, Ni D, Tan S, Sverrisson F, Goverde C, Turelli P, Raclot C, Teslenko A, Pacesa M, Rosset S, Georgeon S, Marsden J, Petruzzella A, Liu K, Xu Z, Chai Y, Han P, Gao GF, Oricchio E, Fierz B, Trono D, Stahlberg H, Bronstein M*, Correia BE*. Nature. 2023, 176, doi: 10.1038/s41586-023-05993-x

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Gainza P, Sverrisson F, Monti F, Rodolà E, Boscaini D, Bronstein MM, Correia BE. Nat Methods. 2020, 184, doi: 10.1038/s41592-019-0666-6