Diffusive hidden Markov model characterization of DNA looping dynamics in tethered particle experiments

TL;DR

This paper introduces a diffusive hidden Markov model to analyze DNA looping dynamics in tethered particle experiments, effectively capturing kinetic states without data filtering and revealing transitions between different looping behaviors.

Contribution

The authors develop a novel diffusive hidden Markov analysis that incorporates Brownian motion, improving the characterization of DNA looping kinetics from tethered particle data.

Findings

Method yields consistent looping lifetime estimates across sampling frequencies.

It detects sudden changes and transitions between kinetically distinct states.

It outperforms traditional threshold-crossing analysis by avoiding time filtering.

Abstract

In many biochemical processes, proteins bound to DNA at distant sites are brought into close proximity by loops in the underlying DNA. For example, the function of some gene-regulatory proteins depends on such DNA looping interactions. We present a new technique for characterizing the kinetics of loop formation in vitro, as observed using the tethered particle method, and apply it to experimental data on looping induced by lambda repressor. Our method uses a modified (diffusive) hidden Markov analysis that directly incorporates the Brownian motion of the observed tethered bead. We compare looping lifetimes found with our method (which we find are consistent over a range of sampling frequencies) to those obtained via the traditional threshold-crossing analysis (which can vary depending on how the raw data are filtered in the time domain). Our method does not involve any time filtering and can detect sudden changes in looping behavior. For example, we show how our method can identify transitions between long-lived, kinetically distinct states that would otherwise be difficult to discern.