Static and Dynamic Disorder in
            <i>IN VITRO</i>
            Reconstituted Receptor–Adaptor Interaction

Takagi, Hiroaki; Morimatsu, Masatoshi; Sako, Yasushi

doi:10.1002/9781118131374.ch8

Cited by 2 publications

(5 citation statements)

References 30 publications

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“…In this article, we demonstrated the potential of our theory by applying to the ON/OFF SM time series of the recognition kinetics between the wild type and the Y1068F mutant of EGFR and Grb2 for an in vitro reconstituted system at 1 nM concentration of Grb2. 16,42 Mathematically there is no difficulty to generalize our theory into non-binary time series having more than two symbols. Appropriate symbolization of time series depends on the experimental setting.…”

Section: Discussionmentioning

confidence: 99%

“…44 Exponential properties and memory effects in association and dissociation kinetics were analyzed for the Y1068F mutant. 16,42 The Y1068F mutant of EGFR showed non-exponential features in the dissociation kinetics (i.e., in the dwell time distribution of the bound form) at all concentrations, which was the same as the wild type EGFR. 16 However, the association kinetics (i.e., the dwell time distribution of the unbound form) at 1 nM Grb2 concentration showed that it is approximated by a single exponential kinetics for a wide range of timescales with the loss of correlation.…”

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

“…16 However, the association kinetics (i.e., the dwell time distribution of the unbound form) at 1 nM Grb2 concentration showed that it is approximated by a single exponential kinetics for a wide range of timescales with the loss of correlation. 16,42 …”

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

“…16 However, the association kinetics (=the dwell time distribution at the unbound form) at 1 nM concentration showed that it is approximated by a single exponential kinetics for a wide range of timescales. 16,42 The SSNs for the Y1068F mutant are interpreted to reflect such complexity of kinetics: the SSNs for the Y1068F mutant have effectively a single OFF state up to the timescale of ∼1.2 s at 1 nM (see Fig. 5).…”

Section: Heterogeneity Of Non-markovian Property Buried In Ssnsmentioning

confidence: 99%

“…The non-Markovianity could not be simply interpreted in terms of a multistate Markovian model whose unseen states were solely determined from the individual OFF-time and ON-time distributions. 42 43 ) by phenylalanine (F) to prevent phosphorylation. 44 Exponential properties and memory effects in association and dissociation kinetics were analyzed for the Y1068F mutant.…”

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

Section: Heterogeneity Of Non-markovian Property Buried In Ssnsmentioning

confidence: 99%

Section: A Brief Description Of An In Vitro Reconstituted Receptor-admentioning

confidence: 99%

See 3 more Smart Citations

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

Self Cite

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Origins of concentration dependence of waiting times for single-molecule fluorescence binding

Pearson

2012

The Journal of Chemical Physics

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Binary fluorescence time series obtained from single-molecule imaging experiments can be used to infer protein binding kinetics, in particular, association and dissociation rate constants from waiting time statistics of fluorescence intensity changes. In many cases, rate constants inferred from fluorescence time series exhibit nonintuitive dependence on ligand concentration. Here, we examine several possible mechanistic and technical origins that may induce ligand dependence of rate constants. Using aggregated Markov models, we show under the condition of detailed balance that non-fluorescent bindings and missed events due to transient interactions, instead of conformation fluctuations, may underly the dependence of waiting times and thus apparent rate constants on ligand concentrations. In general, waiting times are rational functions of ligand concentration. The shape of concentration dependence is qualitatively affected by the number of binding sites in the single molecule and is quantitatively tuned by model parameters. We also show that ligand dependence can be caused by non-equilibrium conditions which result in violations of detailed balance and require an energy source. As to a different but significant mechanism, we examine the effect of ambient buffers that can substantially reduce the effective concentration of ligands that interact with the single molecules. To demonstrate the effects by these mechanisms, we applied our results to analyze the concentration dependence in a single-molecule experiment EGFR binding to fluorophore-labeled adaptor protein Grb2 by Morimatsu et al. [Proc. Natl. Acad. Sci. U.S.A. 104, 18013 (2007)].

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Static and Dynamic Disorder in IN VITRO Reconstituted Receptor–Adaptor Interaction

Cited by 2 publications

References 30 publications

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

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

Origins of concentration dependence of waiting times for single-molecule fluorescence binding

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