email@example.com +41 21 693 61 66 https://www.epfl.ch/labs/lpdi/
Birth date: 23.05.1980
EPFL STI IBI-STI LPDI
AAB 2 44 (Bâtiment AAB)
+41 21 693 61 66
+41 21 693 96 82
Office: AAB 2 44
EPFL > STI > IBI-STI > LPDI
Web site: Web site: https://lpdi.epfl.ch
+41 21 693 61 66
EPFL > SV > SV-SSV > SSV-ENS
Web site: Web site: https://sv.epfl.ch/education
EPFL P-SG CCE
CH C2 397 (Bâtiment CH)
Web site: Web site: https://cce.epfl.ch/
Fields of expertise
BiographyThroughout my PhD and postdoctoral studies I was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in my scientific career I found out my fascination about protein structure and function. My PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked my interest in the many needs and opportunities in vaccinology and translational research. My efforts resulted in an enlightening piece of work where for the first time, computationally designed immunogens elicited potent neutralizing antibodies. During my postdoctoral studies I joined a chemical biology laboratory at the Scripps Research Institute. In this stage I developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In March 2015, I joined the École Polytechnique Fédérale de Lausanne (EPFL) – Switzerland as a tenure track assistant professor. The focus of my research group is to develop computational tools for protein design with particular emphasis in applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). The activities in my laboratory focus on computational design methods development and experimental characterization of the designed proteins. Our laboratory has been awarded with 2 prestigious research grants from the European Research Council. Lastly, I have been awarded the prize for best teacher of Life sciences in 2019.
- Featuring molecular surface fingerprints to decipher protein functional properties
- Bottom-up de novo design of functional proteins
- Computational design of synthetic components for CAR T-cells
- Computational Design of Precision Vaccines
Institute of Bioengineering - EPFL
The Scripps Research Institute
Universidade Nova de Lisboa
Universidade de Coimbra
EPFL - SV
Radcliffe Institute - Harvard
European Research Council
Fundação para a Ciência e Tecnologia
Laboratory of Protein Design and Immunoengineering
De novo designed proteins: a study in engineering novel folds and functionsLausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-11556.
Antibodies to combat viral infections: development strategies and progressNature Reviews Drug Discovery. 2022-06-20. DOI : 10.1038/s41573-022-00495-3.
Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactionsLausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9195.
Targeting molecular surfaces to engineer novel protein-based immunogens and inhibitorsLausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9141.
Rational design of protein switches: applications in synthetic biology and cancer immunotherapyLausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9246.
A Nanoscaffolded Spike-RBD Vaccine Provides Protection against SARS-CoV-2 with Minimal Anti-Scaffold ResponseVaccines. 2021-04-27. DOI : 10.3390/vaccines9050431.
Bottom-up de novo design of functional proteins with complex structural featuresNature Chemical Biology. 2021-01-04. DOI : 10.1038/s41589-020-00699-x.
Fast end-to-end learning on protein surfaces2021-01-01. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), ELECTR NETWORK, Jun 19-25, 2021. p. 15267-15276. DOI : 10.1109/CVPR46437.2021.01502.
On the exploration of novel methodological approaches for immunogen design: case studies in influenza and hepatitis CLausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8752.
Designs and Characterization of Subunit Ebola GP Vaccine Candidates: Implications for ImmunogenicityFrontiers In Immunology. 2020-11-04. DOI : 10.3389/fimmu.2020.586595.
De novo protein design enables the precise induction of RSV-neutralizing antibodiesScience. 2020-05-15. DOI : 10.1126/science.aay5051.
A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapyNature Biotechnology. 2020-02-03. DOI : 10.1038/s41587-019-0403-9.
Functional de novo Protein Design for Targeted Vaccines and Synthetic Biology ApplicationsLausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-7545.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learningNature Methods. 2019-12-09. DOI : 10.1038/s41592-019-0666-6.
rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformaticsBMC Bioinformatics. 2019-05-15. DOI : 10.1186/s12859-019-2796-3.
Expanding beyond the natural protein repertoire to engineer targeted vaccines and diagnosticsLausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-7515.
Computational protein design - the next generation tool to expand synthetic biology applicationsCURRENT OPINION IN BIOTECHNOLOGY. 2018. DOI : 10.1016/j.copbio.2018.04.001.
|Sesterhenn F*, Yang C*, Cramer JT, Bonet J, Wen X, Abriata LA, Kucharska I, Chiang CI, Wang Y, Castoro G, Vollers SS, Galloux M, Dheilly E, Richard CA, Rosset S, Corthesy P, Georgeon S, Villard M, Richard CA, Descamps D, Delgado T, Oricchio E, Rameix-Welti MA, Mas V, Ervin S, Eleouet JF, Riffault S, Bates JT, Julien JP, Li Y, Jardetzky T, Krey T, Correia BE
|De novo protein design enables precise induction of functional antibodies in vivo|
|Mathony J*, Harteveld Z*, Schmelas C*, Belzen JU, Aschenbrenner S, Hoffmann MD, Stengl C, Scheck A, Rosset S, Grimm D, Eils R, Correia BE*, Niopek D*
Nature Chemical Biology, 2020
|Computational design of anti-CRISPR proteins with improved inhibition potency and expanded specificity|
|Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RY, Watkins A, Zimmerman L, Bonneau R
Nature Methods, 2020
|Macromolecular modeling and design in Rosetta: recent methods and frameworks|
|Giordano-Attianese G*, Gainza P*, Gray-Gaillard E*, Cribioli E, Shui S, Kim S, Kwak M, Vollers S, Osorio A, Reichenbach P, Bonet J, Oh B, Irving M*, Coukos G*, Correia BE*
Nature Biotechnology, 2020
|Computationally designed STOP-CAR disrupted by small molecule confers on-command regulation of T-cell therapy|
|Gainza P, Sverrisson F, Monti F, Rodola E, Bronstein MM, Correia BE
Nature Methods, 2020
|Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning|
|Bonet J, Harteveld Z, Sesterhenn F, Scheck A, Correia BE
BMC Bioinformatics, 2019
|rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics|
|Sesterhenn F, Galloux M, Vollers S, Cspregi L, Yang C, Descamps D, Bonet J, Friedensohn S, Gainza P, Corthesy P, Chen M, Rosset S, Rameix-Welti MA, Elouet JF, Reddy ST, Graham B, Riffault S, Correia BE
Plos Biology, 2019
|Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen|
|Bonet J*, Wehrle S*, Schriever K*, Yang C*, Billet A, Sesterhenn F, Scheck A, Sverrisson F, Veselkova B, Vollers S, Lourman R, Villard M, Rosset S, Krey T, Correia BE
Plos Computational Biology, 2018
|Rosetta FunFolDes - a general framework for the computational design of functional proteins|
|Bubeck F, Hoffmann M, Harteveld Z, Aschenbrenner S, Bietz A, Waldhauer MC, Boerner K, Fakhiri J, Schmelas C, Dietz L, Grimm D, Correia BE, Eils R, Niopek D
Nature Methods, 2018
|Engineered anti-CRISPR proteins for optogenetic control of CRISPR/Cas9|
Protein structures are typically modelled as a set of discrete atoms, we develop a new computational framework (MaSIF) which processes proteins as molecular surfaces and leverages machine learning approaches to identify patterns that reveal the interaction fingerprints with other biomolecules. MaSIF was shown to be useful to identify interaction fingerprints (with proteins and ligands) with functional significance and also for the design of novel protein-protein interactions. link
Computational design of functional de novo proteins
Development of a computational algorithm for the de novo design of functional proteins. In one of the selected applications, the designed proteins showed to elicit neutralizing antibodies in animal models providing a proof-of-principle for rationally designed immunogens to modulate antibody responses and provide the basis for future vaccines. link
Computational design of chemical switches for synthetic biology We computationally designed a suite of protein switches which the assembly state was controlled by the presence of a small molecule. Some of these molecules were pre-clinical or clinically approved drugs which opens exciting opportunities for the use of these switches in translational applications. As a proof-of-concept we have embedded these switches in engineered T-cells and showed their activity in vivo. link
Teaching & PhD
Life Sciences Engineering