Go-and-Back method: Effective estimation of the hidden motion of proteins from single-molecule time series

TL;DR

The paper introduces the Go-and-Back method, a computationally efficient approach for estimating hidden protein motion from single-molecule data, leveraging Langevin dynamics and perturbation techniques.

Contribution

It presents a novel estimation method that significantly reduces computational costs while maintaining accuracy in analyzing protein trajectories.

Findings

Achieves over 10,000-fold reduction in computation time.

Provides stable and reasonable estimates of protein motion.

Demonstrates effectiveness with simple experimental models.

Abstract

We present an effective method for estimating the motion of proteins from the motion of attached probe particles in single-molecule experiments. The framework naturally incorporates Langevin dynamics to compute the most probable trajectory of the protein. By using a perturbation expansion technique, we achieve computational costs more than four orders of magnitude smaller than the conventional gradient descent method without loss of simplicity in the computation algorithm. We present illustrative applications of the method using simple models of single-molecule experiments and confirm that the proposed method yields reasonable and stable estimates of the hidden motion in a highly efficient manner.