Quantitative Single-Molecule Conformational Distributions:  A Case Study with Poly-(<scp>l</scp>-proline)

Watkins, Lucas P.; Chang, Huibin; Yang, Haw

doi:10.1021/jp055886d

Cited by 105 publications

(193 citation statements)

References 71 publications

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“…Fig. 2f also shows that ligands cause AK's conformational distribution to become more compact when compared with that observed in the substrate-free case, results made possible using our methodology (32). This narrowing of the distribution can be understood in terms of AK's interaction with ligands, which restricts the range of motion that AK can otherwise experience.…”

Section: Effects Of Ligand Binding On the Conformation Distributionmentioning

confidence: 66%

“…This distribution could be equally well fit to a number of different models if one assumes the number of states present (e.g., one, two, or three) and their shapes (e.g., Gaussian or log-normal). To avoid subjective bias in interpreting the distribution, we applied our deconvolution technique to remove the broadening due to photon-counting noise (32). Based on the maximum entropy method, this procedure recovers the entire distribution of conformational states without a preassumed model regarding the number of states or the shape of the distribution.…”

Section: Resultsmentioning

confidence: 99%

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Illuminating the mechanistic roles of enzyme conformational dynamics

Hanson

Duderstadt

Watkins

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

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276

430

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Many enzymes mold their structures to enclose substrates in their active sites such that conformational remodeling may be required during each catalytic cycle. In adenylate kinase (AK), this involves a large-amplitude rearrangement of the enzyme's lid domain. Using our method of high-resolution single-molecule FRET, we directly followed AK's domain movements on its catalytic time scale. To quantitatively measure the enzyme's entire conformational distribution, we have applied maximum entropy-based methods to remove photon-counting noise from single-molecule data. This analysis shows unambiguously that AK is capable of dynamically sampling two distinct states, which correlate well with those observed by x-ray crystallography. Unexpectedly, the equilibrium favors the closed, active-site-forming configurations even in the absence of substrates. Our experiments further showed that interaction with substrates, rather than locking the enzyme into a compact state, restricts the spatial extent of conformational fluctuations and shifts the enzyme's conformational equilibrium toward the closed form by increasing the closing rate of the lid. Integrating these microscopic dynamics into macroscopic kinetics allows us to model lid opening-coupled product release as the enzyme's rate-limiting step.conformational equilibrium ͉ rate-limiting step ͉ single-molecule FRET ͉ adenylate kinase P roteins such as enzymes are flexible with a range of motions spanning from picoseconds for localized vibrations to seconds for concerted global conformational rearrangements (1). Despite their randomly fluctuating environment, in which stochastic collisions with solvent molecules drive changes in tertiary structure, enzymes have evolved to catalyze reactions efficiently and specifically. Indeed, conformational transitions have been postulated to play a central role in enzyme functions in a wide variety of ways, including direct contribution to catalysis (2), allosteric regulation (3), and large-scale conformational changes in response to ligand binding (4). Most of our current understanding of structural motions in solution comes from NMR experiments (5) as well as from molecular dynamics simulations (6), approaches that are best suited to study dynamics in the picoto millisecond time scales. Because catalysis in enzymes frequently occurs in the submillisecond to minute time regime, our current understanding of the relationship between enzyme function and conformational dynamics comes from NMR experiments involving relatively localized motions of active site forming loops on the submillisecond time scale (7-10). However, many enzymes contain active sites located in between domains in which large-amplitude, low-frequency domain motions are required to complete their Michaelis-Menten enzyme-substrate complexes. Even simple questions regarding these transitions remain generally unanswered: What is the number and range of conformational states accessible to enzymes during their catalytic cycle? How does the enzyme's conformation respond to interac...

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Section: Effects Of Ligand Binding On the Conformation Distributionmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Illuminating the mechanistic roles of enzyme conformational dynamics

Hanson

Duderstadt

Watkins

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

276

430

View full text Add to dashboard Cite

show abstract

“…The underlying best-fit distance distributions can be δ functions 33 or narrow peaks (Figure 10), but could very possibly turn out to be wider in other experimental cases. Equipped with the tools presented in this work, researchers should now be able to make the two analytical steps evoked in the Introduction and extract meaningful information on distance distributions and energy landscapes.…”

Section: Extension To More Complex Systemsmentioning

confidence: 99%

“…I D is the donor excitation intensity, is the absorption cross section of the acceptor at the donor excitation wavelength. We can rewrite (32) Finally, following ref 14 (33) where is the absorption cross section of the donor at the donor excitation wavelength.…”

Section: Appendix C: Theoretical Expression Of ε In the Absence Of Bamentioning

confidence: 99%

Shot-Noise Limited Single-Molecule FRET Histograms: Comparison between Theory and Experiments

et al. 2006

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We describe a simple approach and present a straightforward numerical algorithm to compute the best fit shot-noise limited proximity ratio histogram (PRH) in single-molecule fluorescence resonant energy transfer diffusion experiments. The key ingredient is the use of the experimental burst size distribution, as obtained after burst search through the photon data streams. We show how the use of an alternated laser excitation scheme and a correspondingly optimized burst search algorithm eliminates several potential artifacts affecting the calculation of the best fit shot-noise limited PRH. This algorithm is tested extensively on simulations and simple experimental systems. We find that dsDNA data exhibit a wider PRH than expected from shot noise only and hypothetically account for it by assuming a small Gaussian distribution of distances with an average standard deviation of 1.6 Å. Finally, we briefly mention the results of a future publication and illustrate them with a simple two-state model system (DNA hairpin), for which the kinetic transition rates between the open and closed conformations are extracted.

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“…37 Recently, it was shown that the existence of dynamic disorder of single enzymatic turnover reactions depends on how one assigns the ON/OFF levels and the most widely used binning and thresholding approach may yield a misleading interpretation. 38 Because some subsequences involving one step transitions in the ON/OFF time series were removed from the analyses, 16 it is desired to confirm how such removals would affect in the analyses based on change point detection method.…”

Section: Discussionmentioning

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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Quantitative Single-Molecule Conformational Distributions: A Case Study with Poly-(l-proline)

Cited by 105 publications

References 71 publications

Illuminating the mechanistic roles of enzyme conformational dynamics

Illuminating the mechanistic roles of enzyme conformational dynamics

Shot-Noise Limited Single-Molecule FRET Histograms: Comparison between Theory and Experiments

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

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