Author

Justin Barton

University of LondonORCID 0000-0002-1244-9484Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:protein sequence· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Approach:benchmark study· 1 Approach:contrastive learning· 1 Approach:large language model· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Jacob D. GalsoniD· 3 shared Jinwoo LeemiD· 3 shared James H. R. FarmeryiD· 2 shared María Rodríguez MartíneziD· 2 shared Morten NielseniD· 2 shared Pieter MeysmaniD· 2 shared Vadim K KarnaukhoviD· 2 shared Aleksandra M. WalczakiD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBioinformatics

Jacob D. GalsonORCID

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Author profile →

5 papers

Recent·Most cited

IMMREP25: Unseen Peptides

Eve Richardson, Yannick Jurriaan Maria Aarts, John A. Altin, Coos A. B. Baakman, Philip H Bradley, Binbin Chen, Joakim Nøddeskov Clifford, Manjima Dhar, Danielle Diepenbroek, Ethan Fast, Ragul Gowthaman, Jieling He, Vadim K Karnaukhov, Dario F. Marzella, Pieter Meysman, , Bjoern Peters

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

bioRxiv · Apr 1, 2026 · PreprintBioinformatics

A generative foundation model for antibody sequence understanding

Justin Barton, Aretas Gaspariunas, David Yadin, Jorge Dias, Francesca L. Nice, Danielle Minns, Olivia Snudden, Chelsea Povall, Sara Valle, Harry Dobson, James H. R. Farmery, Jinwoo Leem, Jacob D. Galson

FAbCon is a generative antibody-specific language model that improves antigen binding prediction and antibody design with increasing model size.

bioRxiv · May 22, 2024 · PreprintBioinformatics

Enhancing Antibody Language Models with Structural Information

Justin Barton, Jacob D. Galson, Jinwoo Leem

This paper enhances antibody language models by integrating structural information through a contrastive learning approach, improving binding prediction accuracy and data efficiency.

bioRxiv · Dec 12, 2023 · PreprintBioinformatics

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Pieter Meysman, Justin Barton, Barbara Bravi, Liel Cohen-Lavi, Vadim K Karnaukhov, Elias Lilleskov, Alessandro Montemurro, Morten Nielsen, Thierry Mora, Paul Pereira, Anna Postovskaya, María Rodríguez Martínez, Jorge Fernández-de-Cossio-Díaz, Alexandra Vujkovic, Aleksandra M. Walczak, , Virag Sharma

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

ImmunoInformatics · Feb 3, 2023Peer-reviewedBioinformatics

Deciphering the language of antibodies using self-supervised learning

Jinwoo Leem, L. Mitchell, James H. R. Farmery, Justin Barton, Jacob D. Galson

This paper introduces AntiBERTa, a language model for BCR sequences, showing it can predict paratope positions better than existing tools.

Patterns · May 18, 2022Peer-reviewedBioinformatics