scholarly journals Memory effects and static disorder reduce information in single-molecule signals

2022 ◽  
Author(s):  
Kevin Song ◽  
Dmitrii E Makarov ◽  
Etienne Vouga
Keyword(s):  
Molecular Dynamics ◽  
Information Theory ◽  
Time Series ◽  
Single Molecule ◽  
Dynamical Models ◽  
Static Disorder ◽  
Single Molecule Studies ◽  
Lower Entropy ◽  
Conformational Memory ◽  
Low Dimensional

A key theoretical challenge posed by single-molecule studies is the inverse problem of deducing the underlying molecular dynamics from the time evolution of low-dimensional experimental observables. Toward this goal, a variety of low-dimensional models have been proposed as descriptions of single-molecule signals, including random walks with or without conformational memory and/or with static or dynamics disorder. Differentiating among different models presents a challenge, as many distinct physical scenarios lead to similar experimentally observable behaviors such as anomalous diffusion and nonexponential relaxation. Here we show that information-theory-based analysis of single-molecule time series, inspired by Shannon's work studying the information content of printed English, can differentiate between Markov (memoryless) and non-Markov single-molecule signals and between static and dynamic disorder. In particular, non-Markov time series are more predictable and thus can be compressed and transmitted within shorter messages (i.e. have a lower entropy rate) than appropriately constructed Markov approximations, and we demonstrate that in practice the LZMA compression algorithm reliably differentiates between these entropy rates across several simulated dynamical models.

scholarly journals Finite-Temperature Single Molecule Vibrational Dynamics from Combined Density Functional Tight Binding Extended Lagrangian Dynamics Simulations and Time Series Analysis

2020 ◽  
Vol 20 (6) ◽  
pp. 201-212
Author(s):  
Bojana Koteska ◽  
Anastas Mishev ◽  
Ljupco Pejov
Keyword(s):  
Molecular Dynamics ◽  
Time Series ◽  
Single Molecule ◽  
Density Functional ◽  
Tight Binding ◽  
Range Data ◽  
Computationally Efficient ◽  
Vibrational Dynamics ◽  
Wide Range ◽  
Dynamics Simulations

AbstractCombining a computationally efficient and affordable molecular dynamics approach, based on atom-centered density matrix propagation scheme, with the density functional tight binding semiempirical quantum mechanics, we study the vibrational dynamics of a single molecule at series of finite temperatures, spanning quite wide range. Data generated by molecular dynamics simulations are further analyzed and processed using time series analytic methods, based on correlation functions formalism, leading to both vibrational density of states spectra and infrared absorption spectra at finite temperatures. The temperature-induced dynamics in structural intramolecular parameters is correlated to the observed changes in the spectral regions relevant to molecular detection. In particular, we consider a case when an intramolecular X-H stretching vibrational states are notably dependent on the intramolecular torsional degree of freedom, the dynamics of which is, on the other hand, strongly temperature-dependent.

scholarly journals Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yasuhiro Matsunaga ◽  
Yuji Sugita
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Time Series ◽  
Single Molecule ◽  
Time Series Data ◽  
Conformational Dynamics ◽  
Md Simulations ◽  
Series Data ◽  
Model Parameters ◽  
Markov Modeling

Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. It is applied to the folding dynamics of the formin-binding protein WW domain. MD simulations over 400 μs led to an initial Markov state model (MSM), which was then "refined" using single-molecule Förster resonance energy transfer (FRET) data through hidden Markov modeling. The refined or data-assimilated MSM reproduces the FRET data and features hairpin one in the transition-state ensemble, consistent with mutation experiments. The folding pathway in the data-assimilated MSM suggests interplay between hydrophobic contacts and turn formation. Our method provides a general framework for investigating conformational transitions in other proteins.

scholarly journals Hopping and Flipping of RNA Polymerase on DNA during Recycling for Reinitiation after Intrinsic Termination in Bacterial Transcription

2021 ◽  
Vol 22 (5) ◽  
pp. 2398
Author(s):  
Wooyoung Kang ◽  
Seungha Hwang ◽  
Jin Young Kang ◽  
Changwon Kang ◽  
Sungchul Hohng
Keyword(s):  
Single Molecule ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Facilitated Diffusion ◽  
One Dimensional ◽  
Bacterial Transcription ◽  
Fluorescence Measurements ◽  
Dimensional Diffusion ◽  
Single Molecule Studies ◽  
Hopping Diffusion

Two different molecular mechanisms, sliding and hopping, are employed by DNA-binding proteins for their one-dimensional facilitated diffusion on nonspecific DNA regions until reaching their specific target sequences. While it has been controversial whether RNA polymerases (RNAPs) use one-dimensional diffusion in targeting their promoters for transcription initiation, two recent single-molecule studies discovered that post-terminational RNAPs use one-dimensional diffusion for their reinitiation on the same DNA molecules. Escherichia coli RNAP, after synthesizing and releasing product RNA at intrinsic termination, mostly remains bound on DNA and diffuses in both forward and backward directions for recycling, which facilitates reinitiation on nearby promoters. However, it has remained unsolved which mechanism of one-dimensional diffusion is employed by recycling RNAP between termination and reinitiation. Single-molecule fluorescence measurements in this study reveal that post-terminational RNAPs undergo hopping diffusion during recycling on DNA, as their one-dimensional diffusion coefficients increase with rising salt concentrations. We additionally find that reinitiation can occur on promoters positioned in sense and antisense orientations with comparable efficiencies, so reinitiation efficiency depends primarily on distance rather than direction of recycling diffusion. This additional finding confirms that orientation change or flipping of RNAP with respect to DNA efficiently occurs as expected from hopping diffusion.

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