Author

Kamel Lahouel

Translational Genomics Research InstituteORCID 0000-0002-4339-5749Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:protein structure prediction· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:graph neural network· 1 Assay:protein structure· 1 Assay:proteomics· 1

Frequent Co-Authors

Cristian TomasettiiD· 3 shared John A. AltiniD· 2 shared Kameron BatesiD· 2 shared Mete MulazimogluiD· 2 shared Antoine Goursaud· 1 shared Brandon Neff· 1 shared Ehsan Variani· 1 shared Erin KelleyiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersImmunology

Cristian TomasettiORCID

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3 papers

Recent·Most cited

General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3

Lawson Woods, Brandon Neff, Kamel Lahouel, Antoine Goursaud, Mete Mulazimoglu, Kameron Bates, Cristian Tomasetti, John A. Altin

This paper uses AlphaFold 3 to predict T cell receptor antigen specificity from sequence, achieving high accuracy on unseen epitopes.

Science Advances · Oct 30, 2026Peer-reviewedImmunology

Learned Geometry, Predicted Binding: Structurally-BasedPrediction of Peptide:MHC Binding Using AlphaFold 3 EnablesCD4 T Cell Epitope Prediction

Kamel Lahouel, Mete Mulazimoglu, Jorge Soria‐Bustos, Kameron Bates, Erin Kelley, Lawson J Woods, Kunjur Manasa Upadhyaya, Gonzalo Raúl Acevedo, Sophie Pénisson, Matteo Munini, Ehsan Variani, Margaret E. Feeney, John A. Altin, Cristian Tomasetti

The paper introduces MHCIIFold-GNN, a method combining structural modeling and graph neural networks to predict CD4 T cell epitopes, achieving state-of-the-art performance.

bioRxiv · Jul 10, 2025 · PreprintBioinformatics

Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity

Hirokazu Kimura, Kamel Lahouel, Cristian Tomasetti, Nicholas Jason Roberts

The paper functionally characterizes all CDKN2A missense variants and evaluates in silico pathogenicity models, finding 17.7% are deleterious and models have moderate accuracy.

eLife · Apr 16, 2025Peer-reviewedGenetics