Multi-parameter photon-by-photon hidden Markov modeling

Abstract

Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H2MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H2MM (mpH2MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH2MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations and their origin.

Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET

“…7 ). Recently, we studied YopO via multi-parameter photon-by-photon hidden Markov modelling, where the underlying apo distribution of YopO could be resolved 123 . We showed that apo YopO consists of two major FRET peaks that average on a sub 100 µs timescale to give one apparently “static” population.…”

Identification and Quantification of Within-Burst Dynamics in Singly-Labeled Single-Molecule Fluorescence Lifetime Experiments

“…MpH 2 MM is an analytical method that extends classical hidden Markov modelling to the domain of single-molecule measurements. It has previously been used to identify within-burst dynamics originating from both conformational and photophysical dynamics in smFRET experiments 19 . As such, it excels at distinguishing between data containing a mixture of states, and data with a single state of more mixed nature.…”

“…We test from one to for divisors in the non-IRF scheme, and 1 to 4 divisors (which have n+1 divisors due to the additional divisor of the IRF) in the IRF scheme. All analyses were performed using the H2MM_C python package 19 .…”

“…Therefore, we aspired to develop a method to quantify within-burst dynamics in single-dye single-molecule fluorescence lifetime-based measurements such as smPIFE. For this, we sought to adapt the multi-parameter photon-by-photon hidden Markov modeling (mpH 2 MM) framework 19 that we recently introduced to function with photon nanotime data. We therefore introduce a simple and widely implementable scheme to transform the exponential distribution of photon nanotimes into set of tractable parameters, amenable for use with mpH 2 MM.…”

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

“…The HMM formalism is based on a discrete memoryless Markov process that infers a set of parameters (probabilities of states, transitions, and observations) to describe the observed sequence of FRET efficiencies. Many extensions of the HMM formalism have been developed 10 – 15 including Bayesian approaches 16 – 19 , and very fast kinetics (low energy barrier crossings) can be inferred from single-photon arrival times 20 – 22 .…”