An integrated methodology for data processing in dynamic force spectroscopy of ligand-receptor binding

Area of Science:

• Biophysics
• Biochemistry
• Materials Science

Background:

• Dynamic force spectroscopy (DFS) with atomic force microscopy (AFM) is crucial for studying ligand-receptor interactions.
• Analyzing stochastic bond rupture events reveals binding mechanisms.
• Current methods for identifying rupture events in force-distance data lack standardization.

Purpose of the Study:

• To develop an efficient data processing protocol for DFS analysis.
• To investigate the influence of different peak selection methods on rupture event identification.
• To streamline experimental procedures and data analysis in AFM-based binding studies.

Main Methods:

• Implementation of novel data processing software for AFM force-distance curves.
• Utilizing various peak selection algorithms for event identification.
• Analysis of experimental data from a chelate metal compound-monoclonal antibody complex.

Main Results:

• The developed protocol efficiently manages large datasets from DFS experiments.
• Different peak selection methods significantly influence the identification and analysis of rupture events.
• The software simplifies calibration, statistical treatment, and distribution analysis of binding data.

Conclusions:

• Standardized data processing is essential for reliable DFS analysis of molecular interactions.
• The developed software provides a robust framework for studying ligand-receptor binding dynamics.
• This approach enhances the reproducibility and accuracy of AFM-based biophysical studies.