Author

A. Larabi

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:chicken / avian· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:mechanistic / dynamical model· 1 Organism:mouse· 1

Frequent Co-Authors

F. Pojer· 2 shared K. Lau· 2 shared A. Harari· 1 shared B. Yang· 1 shared D. Gfeller· 1 shared G. Coukos· 1 shared G. Croce· 1 shared J. Michaux· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersBiophysics

F. Pojer

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

L. Dumas, M. Marfoglia, B. Yang, M. Hijazi, A. Larabi, K. Lau, F. Pojer, M. Nash, P. BARTH

This study investigates the mechanical properties of the ADGRG1 GAIN domain using single-molecule force spectroscopy and simulations, revealing structural mechanisms that enhance mechanical resistance and informing potential drug design.

bioRxiv · Apr 5, 2023 · PreprintBiophysics

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

J. Racle, P. Guillaume, J. Schmidt, J. Michaux, A. Larabi, K. Lau, M. A. S. Perez, G. Croce, R. Genolet, G. Coukos, V. Zoete, F. Pojer, M. Bassani-Sternberg, A. Harari, D. Gfeller

The paper predicts MHC-II epitope specificities using machine learning, revealing a reverse binding mode in ligands across multiple species.

bioRxiv · Jun 26, 2022Immunology