Learning continuous potentials from smFRET.

Area of Science:

• Biophysics
• Physical Chemistry
• Computational Biology

Background:

• Potential energy landscapes model crucial biological processes like protein folding and binding.
• Single-molecule fluorescence resonance energy transfer (smFRET) experiments capture continuous system dynamics but this information is often lost in analysis.
• Current methods typically discretize the system into states for Hidden Markov Model (HMM) analysis, limiting insights.

Purpose of the Study:

• To develop a novel method for inferring continuous potential energy landscapes directly from smFRET data.
• To overcome the limitations of existing methods that discretize the state space.
• To extract previously inaccessible dynamic information such as barrier heights and friction coefficients.

Main Methods:

• Bayesian nonparametric inference with priors on potential energy curves.
• Utilizing a structured-kernel-interpolation Gaussian process prior to manage computational costs.
• Incorporating experimental features like photon shot noise into the inference framework.

Main Results:

• Accurate inference of potential energy landscapes from smFRET binding experiments.
• Demonstrated advantages over standard Hidden Markov Model (HMM) approaches.
• Successfully extracted barrier heights and friction coefficients, information not available through HMMs.

Conclusions:

• The developed Bayesian nonparametric framework accurately reconstructs continuous potential energy landscapes from smFRET data.
• This method offers a significant advancement over traditional HMM approaches for smFRET data analysis.
• Enables a more comprehensive understanding of molecular dynamics by providing access to key kinetic parameters.