An Energy Based Scheme for Reconstruction of Piecewise Constant Signals observed in the Movement of Molecular Machines

TL;DR

This paper introduces a novel energy-based algorithm for accurately detecting steps in noisy signals from molecular machines, improving speed and precision over existing methods.

Contribution

The paper presents a two-step energy-based algorithm combining convex denoising and graph optimization for step detection in molecular machine data, outperforming prior techniques.

Findings

EBS outperforms existing methods in speed and accuracy

Successfully detects backtracked intervals in experimental data

Effective in analyzing stepping behavior of DNA motors

Abstract

Analyzing the physical and chemical properties of single DNA based molecular machines such as polymerases and helicases often necessitates to track stepping motion on the length scale of base pairs. Although high resolution instruments have been developed that are capable of reaching that limit, individual steps are oftentimes hidden by experimental noise which complicates data processing. Here, we present an effective two-step algorithm which detects steps in a high bandwidth signal by minimizing an energy based model (Energy based step-finder, EBS). First, an efficient convex denoising scheme is applied which allows compression to tupels of amplitudes and plateau lengths. Second, a combinatorial optimization algorithm formulated on a graph is used to assign steps to the tupel data while accounting for prior information. Performance of the algorithm was tested on poissonian stepping data simulated based on published kinetics data of RNA Polymerase II (Pol II). Comparison to existing step-finding methods shows that EBS is superior both in speed and precision. Moreover, the capability to detect backtracked intervals in experimental data of Pol II as well as to detect stepping behavior of the Phi29 DNA packaging motor is demonstrated.