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This study introduces a new Bayesian method for analyzing single-molecule force spectroscopy data. The approach accurately reconstructs complex bond potentials and diffusivity, even with limited data, improving molecular understanding.

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Area of Science:

  • Single-molecule biophysics
  • Computational chemistry
  • Statistical mechanics

Background:

  • Dynamic single-molecule force spectroscopy (SMFS) probes molecular interactions by distorting bonds.
  • Traditional methods for reconstructing bond potentials from SMFS data often rely on simplified models and assumptions.
  • Existing techniques struggle with complex potentials, smooth results, and spatially varying diffusivity.

Purpose of the Study:

  • To develop a comprehensive empirical Bayesian approach for analyzing SMFS data.
  • To overcome limitations of existing methods in inferring complex bond potentials and diffusivity profiles.
  • To enable accurate uncertainty quantification in reconstructed molecular properties.

Main Methods:

  • Developed a novel empirical Bayesian framework incorporating data and regularization into a path integral.
  • All experimental and statistical parameters are estimated directly from the data.
  • The method allows for simultaneous inference of bond potentials and diffusivity profiles.

Main Results:

  • The regularized approach requires less data compared to traditional methods.
  • Successfully inferred complex bond potentials and spatially varying diffusivity profiles simultaneously.
  • Demonstrated that accurate potential reconstruction is sensitive to and requires simultaneous inference of diffusivity.

Conclusions:

  • The proposed Bayesian method offers a robust and accurate way to analyze SMFS data.
  • Enables simultaneous inference of bond potentials and diffusivity, crucial for accurate molecular modeling.
  • Provides a means for self-consistent regularization parameter selection and uncertainty quantification.