| Title: |
Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations |
| Authors: |
Dreyer, Frederic A.; Schneider, Constantin; Kovaltsuk, Aleksandr; Cutting, Daniel; Byrne, Matthew J.; Nissley, Daniel A.; Kenlay, Henry; Marks, Claire; Errington, David; Gildea, Richard J.; Damerell, David; Tizei, Pedro; Bunjobpol, Wilawan; Darby, John F.; Drulyte, Ieva; Hurdiss, Daniel L.; Surade, Sachin; Wahome, Newton; Pires, Douglas E. V.; Deane, Charlotte M.; Virologie; Infectious Diseases and Immunology - Virology |
| Publication Year: |
2025 |
| Subject Terms: |
Antibody design; Artificial intelligence; Immunology; Mab; Structural biology |
| 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
1942-0862 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/477335 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/477335 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.4FBF91DE |
| Database: |
BASE |