Computational Antibody Papers

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2026
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  • 2026-02-26

    Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

    • nanobodies
    • structure prediction
    • Authors propose a new method to train a nanobody structure predictor by using ‘blueprints’.
    • They developed a classifier (NbFrame) to identify whether the HCDR3 loop adopts a kinked (framework-contacting) or extended (solvent-exposed) conformation. This allows the model to use sequence-encoded priors and explicit constraints during the folding process.
    • The model itself is very lightweight and runs significantly faster than "heavy" models like AF or Boltz. NbForge achieves sub-second inference speeds, predicting structures in less than a second on both CPU and GPU. In comparison, models like AlphaFold3 or Boltz1 typically require tens of seconds to minutes per structure - MSA is a different story altogether.
    • The model matches the HCDR3 prediction quality of heavy models while being much more efficient. While AF3 and Boltz1 are more accurate at modeling the rigid framework, NbForge achieves parity in the hypervariable HCDR3 region—the part most critical for binding. Its speed and high recovery of disulphide bonds make it ideal for triaging millions of candidates in large-scale discovery campaigns.

  • 2026-01-09

    NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks

    • language models
    • nanobodies
    • Benchmarking of pretrained protein, antibody, and nanobody language model representations on a comprehensive suite of nanobody-specific tasks.
    • The authors introduce eight tasks spanning variable-region annotation, CDR infilling, antigen binding prediction, paratope prediction, affinity prediction, polyreactivity, thermostability, and nanobody type classification (e.g. VHH, VNAR, conventional antibody chains).
    • They evaluate generic protein LMs, antibody-specific LMs, and nanobody-specific LMs under a unified and standardized benchmark.
    • All backbone models are kept frozen, with task-specific lightweight heads trained on top to isolate representational quality.
    • No single model consistently outperforms others across all tasks, showing that nanobody-specific pretraining alone does not guarantee superior performance over antibody-specific or generic protein language models.