Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations
Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorpor...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2025.2511220 |
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| author | Frédéric A. Dreyer Constantin Schneider Aleksandr Kovaltsuk Daniel Cutting Matthew J. Byrne Daniel A. Nissley Henry Kenlay Claire Marks David Errington Richard J. Gildea David Damerell Pedro Tizei Wilawan Bunjobpol John F. Darby Ieva Drulyte Daniel L. Hurdiss Sachin Surade Newton Wahome Douglas E.V. Pires Charlotte M. Deane |
| author_facet | Frédéric A. Dreyer Constantin Schneider Aleksandr Kovaltsuk Daniel Cutting Matthew J. Byrne Daniel A. Nissley Henry Kenlay Claire Marks David Errington Richard J. Gildea David Damerell Pedro Tizei Wilawan Bunjobpol John F. Darby Ieva Drulyte Daniel L. Hurdiss Sachin Surade Newton Wahome Douglas E.V. Pires Charlotte M. Deane |
| author_sort | Frédéric A. Dreyer |
| collection | DOAJ |
| description | Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of candidate antibodies with improved developability against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterized binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs. |
| format | Article |
| id | doaj-art-65e47a8eb9ef4e0aba1968ae8caca45b |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-65e47a8eb9ef4e0aba1968ae8caca45b2025-08-20T03:44:42ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2511220Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutationsFrédéric A. Dreyer0Constantin Schneider1Aleksandr Kovaltsuk2Daniel Cutting3Matthew J. Byrne4Daniel A. Nissley5Henry Kenlay6Claire Marks7David Errington8Richard J. Gildea9David Damerell10Pedro Tizei11Wilawan Bunjobpol12John F. Darby13Ieva Drulyte14Daniel L. Hurdiss15Sachin Surade16Newton Wahome17Douglas E.V. Pires18Charlotte M. Deane19Exscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKMaterials and Structural Analysis, Thermo Fisher Scientific, Eindhoven, NetherlandsVirology Section, Infectious Diseases and Immunology Division, Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, NetherlandsExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKExscientia, Oxford Science Park, Oxford, UKDeveloping therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of candidate antibodies with improved developability against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterized binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs.https://www.tandfonline.com/doi/10.1080/19420862.2025.2511220Antibody designartificial intelligenceimmunologymabstructural biology |
| spellingShingle | Frédéric A. Dreyer Constantin Schneider Aleksandr Kovaltsuk Daniel Cutting Matthew J. Byrne Daniel A. Nissley Henry Kenlay Claire Marks David Errington Richard J. Gildea David Damerell Pedro Tizei Wilawan Bunjobpol John F. Darby Ieva Drulyte Daniel L. Hurdiss Sachin Surade Newton Wahome Douglas E.V. Pires Charlotte M. Deane Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations mAbs Antibody design artificial intelligence immunology mab structural biology |
| title | Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| title_full | Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| title_fullStr | Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| title_full_unstemmed | Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| title_short | Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| title_sort | computational design of therapeutic antibodies with improved developability efficient traversal of binder landscapes and rescue of escape mutations |
| topic | Antibody design artificial intelligence immunology mab structural biology |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2511220 |
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