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Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae
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Disulphide and sequence-encoded conformational priors guide nanobody structure prediction.

Montader Ali1, Mateusz Jaskolowski1,2, Matthew Greenig1

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Biorxiv : the Preprint Server for Biology
|April 29, 2026
PubMed
Summary

Nanobody structure prediction is improved by considering HCDR3 blueprint and disulphide bonds. NbForge, a new model, accurately predicts these features, matching larger predictors with faster speeds.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Nanobody binding relies heavily on the HCDR3 loop, which can adopt compact or solvent-exposed conformations.
  • Non-canonical disulfide bonds in nanobodies impose structural constraints, affecting binding loop conformations.
  • Current structure predictors often fail to accurately model HCDR3 blueprints and disulfide connectivity, impacting functional interpretation.

Purpose of the Study:

  • To develop a nanobody folding model that explicitly incorporates HCDR3 blueprint and disulfide bond information.
  • To improve the accuracy and efficiency of nanobody structure prediction.
  • To establish new benchmarks for nanobody modeling beyond RMSD.

Main Methods:

  • Sequence-based prediction of HCDR3 blueprint.
  • Implementation of blueprint- and disulfide-aware inductive biases in the NbForge model.
  • Training NbForge using filtered self-distillation.

Main Results:

  • NbForge accurately predicts HCDR3 blueprint and non-canonical disulfide formation.
  • NbForge achieves coordinate accuracy comparable to state-of-the-art predictors with sub-second inference speed.
  • Using NbForge monomer models as templates enhances nanobody-antigen complex prediction.

Conclusions:

  • Blueprint- and disulfide-aware modeling significantly improves nanobody structure prediction.
  • Lightweight models with appropriate inductive biases can match the performance of resource-intensive predictors.
  • NbFrame and NbForge offer valuable tools for nanobody research.