Matt Raybould

Immunoinformatics, Oxford Protein Informatics Group

Welcome to my academic website! I'm currently a Research Fellow at the University of Oxford, working in the Oxford Protein Informatics Group (OPIG).

Research Interests: I am fascinated by the adaptive immune system and am driven to understand the efficiency of the naive/baseline repertoire, whose relatively sparse diversity can instigate immune responses against almost any antigen. We aim to harness the growing body of repertoire sequencing data to elucidate core principles governing B cell/T cell selection and response across populations. Our belief is that combining genetic information (clonality), amino acid chemistry, and structural information (binding site topography) will yield exciting discoveries that can contribute to the fields of immunodiagnostics, immunosenescence, immunodeficiency/autoimmunity, allergy, and nature-inspired immunotherapeutic/vaccine design.

Research Posts

Other Academic Roles

Education

Publications

You can view my publications on Google Scholar or ResearchGate.

Alternatively, below is an updated list of preprints, peer-reviewed papers, invited articles, and book chapters. Asterisk tags indicate shared authorships.

Preprints

  • Ellmen, I.; Errington, D.; Raybould, M.I.J.; Deane, C.M. (2026) Modelling antibody structures at the speed of language. bioRxiv. doi: 10.64898/2026.06.03.729879.

    [Link to Preprint]

Peer-Reviewed Papers

  1. Quast, N.P.; Raybould, M.I.J.; Deane, C.M. (2026) Structural T-Cell Receptor Analysis in the Age of Machine Learning. Immunol Rev. 342(1):e70168.

    [Link to Review]

  2. Abdalla Elsayed, M.E.A.; Barone, V.; Kaukonen, M.; Raybould, M.I.J.; MacLaren, Robert E. (2026) IMPG2-Associated Retinal Dystrophy with a Novel Missense Variant and Therapeutic Options via Adenine Base Editing. Ophthalmic Genet. doi: 10.1080/13816810.2025.2609679.

    [Link to Paper]

  3. Capel, H.L.; Wang, E.J.D.; Williams, B.H.; Deane, C.M.; Raybould, M.I.J. (2026) Pox-AbDab: the Orthopoxvirus Antibody Database. Front. Immunol. 16:1698441.

    [Link to Paper; Database]

  4. Sagie, S.; Babu, T.; Weller, C.; Tabachnik, C.; Livney, I.; Quast, N.P.; Gumpert, N.; Shomuradova, A.; Raybould, M.I.J.; Levy, R.; Malko, D.; Alfia, A.; Ben Lulu, T.; Alon, M.; Herrera, F.; Kutuzov, M.; Zerbib, M.; Greenberg, P.; Wasserman-Bartov, T.; Benedek, G.; Levin, Y.; Stossel, C.; Kamer, I.; Golan, T.; Oren, R.; Shmueli, M.; Bartok, O.; Bar, J.; Cohen, J.; Dushek, O.; Deane, C.M.; Samuels, Y. (2025) Lymphodepleting chemotherapy potentiates neoantigen-directed T cell therapy by enhancing antigen presentation. Cell Rep. Med. 6(12):102506.

    [Link to Paper]

  5. Quast, N.P.; Deane, C.M.; Raybould, M.I.J. (2025) STCRpy: a software suite for TCR:pMHC structure parsing, interaction profiling, and machine learning dataset preparation. Bioinformatics. 41(10):btaf566. doi: 10.1093/bioinformatics/btaf566.

    [Link to Paper, Codebase]

  6. Ellmen, I.; Schneider, C.; Raybould, M.I.J.; Deane, C.M. (2025) Transformers trained on proteins can learn to attend to Euclidean distance. Transactions in Machine Learning Research.

    [Link to Paper]

  7. Quast, N.P.; Abanades, B.; Guloglu, B.; Karuppiah, V.; Harper, S.; Raybould, M.I.J., Deane, C.M. (2025) T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity. Commun Biol. 8:362.

    [Links to Paper, TCRBuilder2+ Code, TCRBuilder2+ Weights, Dataset of OTS Models]

  8. Condado-Morales, I.; Dingfelder, F.; Waibel, I.; Turnbull, O.M.; Patel, B.; Cao, Z.; Bjelke, J.R.; Grell, S.N.; Bennet, A.; Hummer, A.M.; Raybould, M.I.J.; Deane, C.M.; Egebjerg, T.; Lorenzen, N.; Arosio, P. (2024) A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs. mAbs 16(1):2403156.

    [Link to Paper]

  9. Raybould, M.I.J.* and Greenshields-Watson, A.*; Agarwal, P.; Aguilar-Sanjuan, B.; Olsen, T.H.; Turnbull, O.M.; Deane, C.M. (2024) The Observed T cell receptor Space database enables paired-chain repertoire mining, coherence analysis and language modelling. Cell Rep. 43(9):114704.

    [Links to Paper; Database]

  10. Fischer, K.; Lulla, A.* and So, T.* and Pereyra-Gerber, P.* and Raybould, M.I.J.* and Kohler, T.N.* and Yam-Puc, J.C.*; Kaminski, T.S.; Hughes, R.; Leiss-Maier, F.; Brear, P.; Matheson, N.J.; Deane, C.M.; Hyvonen, M.; Thaventhiran, J.; Hollfelder, F. (2024) Microfluidics-enabled fluorescence-activated cell sorting of single pathogen-specific antibody secreting cells for the rapid discovery of monoclonal antibodies. Nat Biotechnol. doi: 10.1038/s41587-024-02346-5

    [Link to Paper]

  11. Gordon, G.; Raybould, M.I.J.; Wong, A.; Deane, C.M. (2024) Prospects for the computational humanization of antibodies and nanobodies. Front Immunol. 15:1399438

    [Link to Review]

  12. Theorell, J.; Harrison, R.; Williams, R.; Raybould, M.I.J.; Zhao, M.; Fox, H.; Fower, A.; Miller, G.; Wu, Z.; Browne, E.; Mgbachi, V.; Sun, B.; Mopuri, R.; Li, Y.; Waters, P.; Deane, C.M.; Handel, A.; Makuch, M.; Irani, S.R. (2024) Ultra-high frequencies of peripherally matured LGI1 & CASPR2-reactive B cells characterise the cerebrospinal fluid in autoimmune encephalitis. PNAS. 121(7):e2311049121

    [Link to Paper]

  13. Raybould, M.I.J.; Turnbull, O.M.; Suter, A.; Guloglu, B.; Deane, C.M. (2024) Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling. Commun Biol. 7:62.

    [Link to Paper]

  14. Abanades, B.* and Olsen, T.H.*; Raybould, M.I.J.; Aguilar-Sanjuan, B.; Wong, W-K.; Georges, G.; Bujotzek, A.; Deane, C.M. (2024) The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures. Nucleic Acids Res. 52(D1):D545-D551.

    [Link to Paper; Database]

  15. Spoendlin, F.C.; Abanades, B.; Raybould, M.I.J.; Wong W-K.; Georges, G.; Deane, C.M. (2023) Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope. Front Mol Biosci. 10:1237621.

    [Links to Paper]

  16. Raybould, M.I.J.; Nissley, D.A.; Kumar, S.; Deane, C.M. (2023) Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies. Front Immunol. 13:1080596.

    [Links to Paper; Datasets]

  17. Schneider, C.; Raybould, M.I.J.; Deane, C.M. (2022) SAbDab in the Age of Biotherapeutics: Updates including SAbDab-nano, the Nanobody Structure Tracker. Nucleic Acids Res. 50(D1):D1368-D1372.

    [Links to Paper; SAbDab-Nano]

  18. Robinson, S.A.* and Raybould, M.I.J.*; Schneider, C.; Wong, W-K.; Marks, C.; Deane, C.M. (2021) Epitope Profiling using Computational Structural Modelling Demonstrated on Coronavirus-Binding Antibodies. PLoS Comput Biol. 17(12):e1009675.

    [Link to Paper]

  19. Raybould, M.I.J.; Rees, A.R.; Deane, C.M. (2021) Current Strategies for Detecting Functional Convergence across B-cell Receptor Repertoires. mAbs. 13(1):1996732.

    [Link to Review]

  20. Raybould, M.I.J.; Marks, C.; Kovaltsuk, A.; Lewis, A.P.; Shi, J. & Deane, C.M. (2021) Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality. PLoS Comput Biol. 17(3):e1008781.

    [Links to Paper; Datasets]

  21. Raybould, M.I.J.; Kovaltsuk, A.; Marks, C. & Deane, C.M. (2021) CoV-AbDab: the Coronavirus Antibody Database. Bioinformatics. 37(5):734-735.

    [Links to Paper; Database]

  22. Galson, J.D.; Schaetzle, S.; Bashford-Rogers, R.J.M.; Raybould, M.I.J.; Kovaltsuk, A.; Kilpatrick, G.J.; Minter, R.; Finch, D.K.; Dias, J.; James, L.; Thomas, G.; Lee, W.-Y.J.; Betley, J.; Cavlan, O.; Leech, A.; Deane, C.M.; Seoane, J.; Caldas, C.; Pennington, D.; Pfeffer, P.; Osbourn, J. (2020) Deep Sequencing of B cell Receptor Repertoires from COVID-19 Patients Reveals Strong Convergent Immune Signatures. Front Immunol. 11:605170.

    [Link to Paper]

  23. Kovaltsuk, A.; Raybould, M.I.J.; Wong, W-K.; Marks, C.; Kelm, S.; Snowden, J.; Trück, J. & Deane, C.M. (2020) Structural Diversity of B-cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice. PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007636.

    [Links to Paper; Code]

  24. Raybould, M.I.J.; Marks, C.; Lewis, A.P.; Shi, J.; Bujotzek, A.; Taddese, B. & Deane, C.M. (2020) Thera-SAbDab: the Therapeutic Structural Antibody Database. Nucleic Acids Res. 48(1):D383-D388.

    [Links to Paper; Database]

  25. Krawczyk, K.; Raybould, M.I.J.; Kovaltsuk, A.; Deane, C.M. (2019) Looking for Therapeutic Antibodies in Next-Generation Sequencing Repositories. mAbs. 11(7):1197-1205.

    [Link to Paper]

  26. Raybould, M.I.J.* and Wong, W-K.*; Deane, C.M. (2019) Antibody-Antigen Complex Modelling in the Era of Immunoglobulin Repertoire Sequencing. Mol Syst Des Eng. 4(4):679-688.

    [Link to Review]

  27. Raybould, M.I.J.; Marks, C.; Krawczyk, K.; Taddese, B.; Nowak, J.; Lewis, A.P.; Bujotzek, A.; Shi, J.; Deane, C.M. (2019) Five Computational Developability Guidelines for Therapeutic Antibody Profiling. PNAS. 116(10):4025-4030.

    [Links to Paper; Web Application]

Communications & Invited Articles

  1. Ellmen, I.; Raybould, M.I.J.; Deane, C.M. (2025) The protein universe in 3D. Nat Chem Biol. 21, 27-28.

    [Link to Article]

  2. Erasmus, M.F.; Spector, L.; Perea-Schmittle, K.; ...; Deane, C.M.; Raybould, M.I.J.; Evers, A.; ...; Dovner, M.; D'Angelo, S.; Bradbury, A.R.M. (2024) AIntibody: an experimentally validated in silico antibody discovery design challenge. Nat Biotechnol. 42, 1637-1642.

    [Link to Article]

  3. Raybould, M.I.J.; Deane, C.M. (2019) Structural Information to Aid in silico Therapeutic Antibody Design from Next-Generation Sequencing Repertoires. Am Pharm Rev. 22(5):30-35.

    [Link to Article]

Book Chapters

  1. Nissley, D.A.; Raybould, M.I.J.; Deane, C.M.; Kumar, S. (2025) Use of Molecular Simulations to Understand Structural Dynamics of Antibodies. Chapter in: Biopharmaceutical Informatics: Learning to discover developable molecules. pp. 201-227.

    [Link to Chapter]

  2. Raybould, M.I.J.; Deane C.M. (2022) The Therapeutic Antibody Profiler for Computational Developability Assessment. Chapter in: Therapeutic Antibodies (Methods in Molecular Biology). vol 2313, pp. 115-125.

    [Link to Chapter]

Conference Presentations

2026

2025

2024

2023

2022

  • Antibody Society Webinar (Virtual). Community engagement talk
  • PEGS, Boston MA, USA

2021

  • NeurIPS, Learning Meaningful Representations of Life track (Virtual)
  • European Antibody Congress, Basel, Switzerland)
  • RSC Antibody Workshop, CICAG (Virtual)
  • Meeting Minds Global (Virtual). Outreach talk
  • CASSS Higher Order Structure (Virtual)

2020

  • BioDataScience101, Virtual
  • European Antibody Congress, Virtual
  • Intelligent Systems in Molecular Biology, Virtual

2018

  • Intelligent Systems in Molecular Biology (Chicago IL, USA)

Undergraduate Teaching/Supervision

Since 2017, I have tutored the first year biochemists at Merton College in organic chemistry/mechanistic biochemistry and have also mentored students through the first (Prelims) and third (Finals, Part IB) year chemistry courses. I've additionally provided tutorial cover in mechanistic biochemistry for Pembroke College and St Peter's College. I have supervised two Part II MBiochem research projects and a 'Current Opinions' essay.

Graduate Teaching/Supervision

As the co-director of the SABS, ILESLA, and TTPS Programmes, I am jointly responsible for admissions, the academic coordination of the course, and student mentoring. I have taught several graduate-level modules at the Doctoral Training Centre in topics such as organic chemistry, programming, cells and systems, structural biology and drug discovery. I currently primary-supervise two DPhil students and secondary-supervise four DPhil students across MPLS and Medical Sciences.

Consulting

Private Sector

I am a biotechnology consultant with Oxford University Innovation, specialising in machine learning and classical methods for biological pharmaceutical discovery, design and optimisation.

I have four years of experience consulting for patent cases and biotech start-ups/scale-ups. If you would like to discuss a potential consulting arrangement, please contact me.

Public Sector

I am sitting on a National Academic of Sciences Consensus Committee exploring the risks of AI-assisted biological threats and potential preparedness measures.

Contact

Address

24-29 St Giles'
Department of Statistics
University of Oxford
Oxford
OX1 3LB

Email

matthew.raybould@stats.ox.ac.uk

LinkedIn

in/mraybould