Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design
Jean V Sampaio1,2, Andrielly H S Costa1,2, Aline O Albuquerque1
1Laboratory of Structural and Functional Biology Applied to Biopharmaceuticals, Fundação Oswaldo Cruz, Fiocruz Ceará, Eusébio 61773-270, Brazil.
Journal of Chemical Information and Modeling
|January 20, 2026
Summary
Ab-SELDON is a new computational tool for antibody design. It improves antibody-antigen interactions, accelerating biopharmaceutical development by exploring more design possibilities efficiently.
Area of Science:
- Biopharmaceutical Development
- Computational Biology
- Immunology
Background:
- Predictive tools accelerate biopharmaceutical research but face limitations in antibody design.
- Current methods struggle with limited antibody structures, immunogenicity, and restricted paratope exploration.
Purpose of the Study:
- To introduce Ab-SELDON, a novel, modular pipeline for optimizing antibody-antigen interactions.
- To enhance the exploration of paratope chemical and conformational space in antibody design.
Main Methods:
- Ab-SELDON employs iterative optimization with five modification steps: CDR and framework grafting, and mutagenesis.
- The pipeline utilizes diversity data from millions of public human antibody sequences.
- It guides optimization by exploring chemical and conformational paratope space.
Main Results:
- Ab-SELDON enhanced paratope exploration in anti-HER2 antibody optimization.
- It stabilized antibody-antigen interactions for an anti-Gal-3BP antibody in simulations.
- The pipeline accurately predicted the effects of most antibody-antigen mutations, including affinity-enhancing ones.
Conclusions:
- Ab-SELDON offers a computationally efficient and automated approach to in silico antibody design.
- This pipeline facilitates biopharmaceutical development by improving antibody optimization.
- The freely available tool expands the possibilities for designing novel therapeutic antibodies.
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