Single-Molecule Enzymology

Dovic̀hi, Norman J.; Polakowski, Robert; Skelley, and Alison; Craig, Doug; Wong, Jerome C. Y.

doi:10.1007/978-3-642-56544-1_14

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…Conformationally heterogeneous systems benefit most from SM measurements. Examples includeprotein folding and conformationaldynamics [3][4][5][6][7][8]2], enzymology [9][10][11][12], ribozyme function [13], bacterial light harvesting [5,14,15], and protein-nucleic acid interactions [16].…”

Section: Single Molecule Fluorescencementioning

confidence: 99%

Markov processes in single molecule fluorescence

Talaga¹

2007

Current Opinion in Colloid & Interface Science

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This article examines the current status of Markov processes in single molecule fluorescence. For molecular dynamics to be described by a Markov process, the Markov process must include all states involved in the dynamics and the FPT distributions out of those states must be describable by a simple exponential law. The observation of non-exponential first-passage time distributions or other evidence of non-Markovian dynamics is common in single molecule studies and offers an opportunity to expand the Markov model to include new dynamics or states that improve understanding of the system.

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Section: Single Molecule Fluorescencementioning

confidence: 99%

Markov processes in single molecule fluorescence

Talaga¹

2007

Current Opinion in Colloid & Interface Science

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“…The CAS9 protein of the CRISPR system is complexed together with a designed guide-RNA to create a Ribonucleoprotein (RNP) complex. The RNP is guided to the target DNA for specific cleavage near a protospacer adjacent motif (PAM) site [ [3] , [4] , [5] ], enabling guide RNAs to be engineered for various applications. Nevertheless, several challenges still hinder effective usage of this technology for medical applications.…”

Section: Introductionmentioning

confidence: 99%

“…The common perspective is that there is rapid equilibrium of the enzyme-substrate binding prior to enzyme activity. Thus, the characterization includes measurement of both the binding and the activity, as described by the Michaelis Menten model [ 5 , 6 ]. Regarding CRISPR-CAS9 enzymes and other RNA guided nucleases, specificity can be defined as the ratio of cleavage efficiencies of the target to its competing substrates [ [7] , [8] , [9] , [10] ].…”

Section: Introductionmentioning

confidence: 99%

Microfluidic tool for rapid functional characterization of CRISPR complexes

et al. 2022

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“…Single-molecule (SM) spectroscopy provides a means to uncover the complex mechanism of molecular machinery at single molecule level that one cannot address in bulk measurements. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] For example, it was found in an in vitro reconstituted system that the recognition (i.e., dissociation and association) kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) show non-exponential kinetics, and association kinetics depends nonlinearly on the Grb2 concentration, suggesting the existence of molecular memory in the signaling process. 16 Such innovative experimental developments of synthetic approaches using intact plasma membrane fractions a) Email: tamiki@es.hokudai.ac.jp have offered new challenges in theoretical modeling of the underlying mechanism of molecular machinery.…”

Section: Introductionmentioning

confidence: 99%

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

Sultana

Takagi

Morimatsu

et al. 2013

The Journal of Chemical Physics

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We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ∼3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

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Single-Molecule Enzymology

Cited by 4 publications

References 31 publications

Markov processes in single molecule fluorescence

Markov processes in single molecule fluorescence

Microfluidic tool for rapid functional characterization of CRISPR complexes

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

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