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March 13, 2026Biophysical Journal0 citationsOpen Access

Backbone rigidity of disordered protein linkers from NMR experiments and MD simulations

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EMEfstathia MantzariCMCajsa MalmRNRicky Nencini

Key Points

  • Understanding the biophysical properties of disordered protein linkers is crucial for protein function and design.
  • Combined NMR experiments with molecular dynamics simulations.
  • Measured <sup>15</sup>N spin relaxation times to assess backbone rigidities.
  • Characterized four model peptides representing natural and engineered linker sequences.
  • Used the QEBSS framework to evaluate simulation accuracy.
  • Glycine-rich linkers showed looping tendencies, while proline linkers had extended conformations.
  • Proline sequences exhibited increased persistence lengths and slower dynamics.
  • Sodium and calcium binding minimally affected linker rigidity, indicating weak influence of electrostatics.

Abstract

Disordered protein linkers are essential for multidomain protein function and engineering, but quantitative methods for their biophysical characterization remain limited. We combined NMR experiments with molecular dynamics simulations to demonstrate that protein backbone 15N spin relaxation times correlates with backbone rigidities in short disordered linkers. Using a tailored version of the Quality Evaluation Based Simulation Selection (QEBSS) framework, we characterized four model peptides: (GGS)3, (GPS)3, K(AP)5K, and KKEEVKKEEV-(PK)7KEEVKKEEVKK, representing common natural and engineered linker repeats. Glycine-rich sequences showed slight looping tendencies, while proline-containing sequences adopted extended conformations with increased approximate persistence lengths and slower dynamics. Notably, sodium and calcium binding to charged peptides minimally affected rigidity, indicating electrostatics don't dominate linker stiffness. This integrated approach provides quantitative insights into disordered linker properties and MD simulation accuracy, offering biophysical understanding for protein design and machine learning model development.

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Cite This Study

Mantzari et al. (2026) studied this question.

synapsesocial.com/papers/69b3aad702a1e69014ccb817https://doi.org/10.1016/j.bpj.2026.03.009
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