Computational Antibody Papers

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epitope prediction
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2025
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  • 2025-07-25

    NextGenPLM: A Novel Structure-Infused Foundational Protein Language Model for Antibody Discovery and Optimization

    • language models
    • epitope prediction
    • Novel protein language model with applications to epitope prediction and ranking hits in campaigns.
    • NextGenPLM introduces a modular, multimodal transformer that fuses frozen pretrained protein language models with structural information via spectral contact-map embeddings, enabling efficient modeling of multi-chain antibody–antigen complexes without requiring full 3D folding of antibodies.
    • The model was benchmarked on 112 diverse antibody–antigen complexes against state-of-the-art structure predictors (Chai-1 and Boltz-1x), matching their contact-map and epitope prediction accuracy while achieving ~100× higher throughput (4 complexes/sec vs. ~1 min/complex).
    • The model was experimentally validated through an internal affinity-maturation campaign. Using its predictions to rank antibody variants led to designs that achieved up to 17× binding affinity improvements over the wild-type, as confirmed by surface plasmon resonance (SPR) assays.

  • 2025-03-31

    AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

    • epitope prediction
    • docking
    • structure prediction
    • Benchmarking of the structure prediction/docking and co-folding methods for antibody design
    • Authors measure the impact of antibody-antigen model quality on the success rate of epitope prediction and antibody design.
    • For epitope prediction and antibody design they use a proxy measure of DockQ score - they call success when DockQ is better than 0.23, for antibody design they use a stricter threshold of 0.49.
    • Using these measures, AlphaFold3 comes out on top, and it would be successful roughly ~47% times.
    • THey introduce an approach where ProPOSE and ZDOCK decoys are refined using AlphaFold. With this combined protocol they reach success rates of 35% for epitope mapping and 30% for antibody design.