Steps and bumps: precision extraction of discrete states of molecular machines using physically-based, high-throughput time series analysis

TL;DR

This paper introduces new statistical time-series analysis tools that enable rapid, precise extraction of discrete state transitions in molecular machines from large datasets, outperforming existing methods in accuracy and speed.

Contribution

The authors develop physically-informed, high-throughput analysis techniques that improve detection of molecular state changes, including weak signals, in noisy data.

Findings

Successfully applied to simulated and real molecular data

Identifies subtle molecular steps and symmetries

Operates faster than existing algorithms

Abstract

We report new statistical time-series analysis tools providing significant improvements in the rapid, precision extraction of discrete state dynamics from large databases of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we demonstrate new techniques for recovering discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of step-like rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, symmetries and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a major advance in the drive towards automated, precision, highthroughput studies of molecular machine dynamics. Modular, open-source software that implements these techniques is provided at http://www.eng.ox.ac.uk/samp/members/max/software/