Decoding Single Molecule Time Traces with Dynamic Disorder

TL;DR

This paper introduces VB-DCMM, a new Bayesian algorithm for analyzing single molecule trajectories with dynamic disorder, enabling detection of hidden states and transition rates in complex biomolecular data.

Contribution

The paper presents a novel Variational Bayes-double chain Markov model (VB-DCMM) for analyzing single molecule time traces with dynamic disorder, addressing a gap in current analysis methods.

Findings

VB-DCMM detects dynamic disorder in single molecule data.

Identifies the number of internal states and transition rates.

Applied successfully to H-DNA FRET data revealing multiple kinetic paths.

Abstract

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na$^+$ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.