Bayesian Inference of Binding Kinetics from Fluorescence Time Series.

Area of Science:

• Biophysics
• Biochemistry
• Molecular Dynamics

Background:

• Analyzing binding kinetics using fluorescence time traces is challenging due to measurement noise and photophysics.
• Photobleaching, a common issue in fluorescence microscopy, limits current methods for determining binding and unbinding rates.
• Existing techniques struggle to accurately quantify kinetic rates when photobleaching events occur concurrently with binding events.

Purpose of the Study:

• To develop a novel method for inferring binding and unbinding rates alongside photobleaching rates from fluorescence intensity traces.
• To overcome limitations of current methods by accounting for noise and photobleaching in kinetic analysis.
• To provide a robust approach for analyzing noisy fluorescence data in binding studies.

Main Methods:

• A two-stage process involving Hidden Markov Model (HMM) analysis of individual regions of interest (ROIs).
• Inferring fluorescence intensity levels and state trajectories for each trace using the HMM.
• Utilizing the inferred intensity level state trajectories from all ROIs to determine kinetic rates.

Main Results:

• The proposed method effectively infers binding and unbinding rates in the presence of photobleaching.
• The approach successfully analyzes noisy fluorescence traces, providing reliable kinetic rate estimations.
• The method quantifies uncertainties associated with the inferred binding kinetics, enhancing data interpretation.
• Demonstrated effectiveness and reliability through simulations and experimental DNA origami binding data.

Conclusions:

• The developed method offers a significant advancement in analyzing binding kinetics from fluorescence data.
• It provides a robust solution for studies affected by noise and photobleaching, improving accuracy and reliability.
• This approach enhances the understanding of molecular interactions by enabling precise kinetic rate determination.