High Degree of Coordination and Division of Labor among Subunits in a Homomeric Ring ATPase

Chistol, Gheorghe; Liu, Shixin; Hetherington, Craig L.; Moffitt, Jeffrey R.; Grimes, Shelley; Jardine, Paul J.; Bustamante, Carlos

doi:10.1016/j.cell.2012.10.031

Cited by 112 publications

(174 citation statements)

References 49 publications

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“…By fitting the measured N min versus [ATP] with Eqn (24), we determined that this motor has N L = 1.6 AE 0.3. Recent work [58] has shown that the motor binds and hydrolyzes five ATPs and that the binding and hydrolysis of the fifth ATP plays a regulatory role; thus, N bind = 5 for the packaging motor. This same study [58] also suggests that the regulatory ATP is bound much more slowly than the other ATPs.…”

Section: What Do the New Kinetic Parameters Reveal About The Enzymatimentioning

confidence: 99%

“…Recent work [58] has shown that the motor binds and hydrolyzes five ATPs and that the binding and hydrolysis of the fifth ATP plays a regulatory role; thus, N bind = 5 for the packaging motor. This same study [58] also suggests that the regulatory ATP is bound much more slowly than the other ATPs. We can use the measured value of N L and Eqn (34) to show that, if all other subunits bind ATP with equal substrate specificity, this subunit must have a 14-fold lower substrate specificity.…”

Section: What Do the New Kinetic Parameters Reveal About The Enzymatimentioning

confidence: 99%

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Extracting signal from noise: kinetic mechanisms from a Michaelis–Menten‐like expression for enzymatic fluctuations

2013

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Enzyme-catalyzed reactions are naturally stochastic, and precision measurements of these fluctuations, made possible by single-molecule methods, promise to provide fundamentally new constraints on the possible mechanisms underlying these reactions. We review some aspects of statistical kinetics: a new field with the goal of extracting mechanistic information from statistical measures of fluctuations in chemical reactions. We focus on a widespread and important statistical measure known as the randomness parameter. This parameter is remarkably simple in that it is the squared coefficient of variation of the cycle completion times, although it places significant limits on the minimal complexity of possible enzymatic mechanisms. Recently, a general expression has been introduced for the substrate dependence of the randomness parameter that is for rate fluctuations what the Michaelis-Menten expression is for the mean rate of product generation. We discuss the information provided by the new kinetic parameters introduced by this expression and demonstrate that this expression can simplify the vast majority of published models.

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Section: What Do the New Kinetic Parameters Reveal About The Enzymatimentioning

confidence: 99%

Section: What Do the New Kinetic Parameters Reveal About The Enzymatimentioning

confidence: 99%

Extracting signal from noise: kinetic mechanisms from a Michaelis–Menten‐like expression for enzymatic fluctuations

2013

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“…We have applied EBS to experimental stepping data of ϕ29 recorded with a bandwidth of 2.5kHz using opposing forces of around 5pN [12,13]. We used 2.5bp for the level grid spacing as well as for the jump height prior ( in Eq.…”

Section: Ebs Is Orders Of Magnitude Faster Than Competing Algorithmsmentioning

confidence: 99%

“…Nowadays state of the art optical tweezers experiments allow to study the movement of enzymes with a resolution down to single base pairs [3,10]. For example, studies on the ϕ29 bacteriophage ring ATPase [11][12][13] used the information from step detection data to propose a complete model of the mechanochemical cycle. However, oftentimes analysis schemes rely on low pass smoothed data.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in order to better test the step-finding properties of EBS on actual experimental data one would need to reduce the stepping rates, or apply the algorithm to a motor protein with larger step size. A prominent example for such a process is the packaging of DNA by the bacteriophage ϕ29 motor, which makes steps of 10bp which consist of a burst of four steps with a size of 2.5bp each [12].We have applied EBS to experimental stepping data of ϕ29 recorded with a bandwidth of 2.5kHz using opposing forces of around 5pN [12,13]. We used 2.5bp for the level grid spacing as well as for the jump height prior ( in Eq.…”

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Energy-based scheme for reconstruction of piecewise constant signals observed in the movement of molecular machines

et al. 2016

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Analyzing the physical and chemical properties of single DNA based molecular machines such as polymerases and helicases requires to track stepping motion on the length scale of base pairs. Although high resolution instruments have been developed that are capable of reaching that limit, individual steps are oftentimes hidden by experimental noise which complicates data processing. Here, we present an effective two-step algorithm which detects steps in a high bandwidth signal by minimizing an energy based model (Energy based step-finder, EBS). First, an efficient convex denoising scheme is applied which allows compression to tuples of amplitudes and plateau lengths. Second, a combinatorial clustering algorithm formulated on a graph is used to assign steps to the tuple data while accounting for prior information.Performance of the algorithm was tested on Poissonian stepping data simulated based on published kinetics data of RNA Polymerase II (Pol II). Comparison to existing step-finding methods shows that EBS is superior in speed while providing competitive step detection results especially in challenging situations.Moreover, the capability to detect backtracked intervals in experimental data of Pol II as well as to detect stepping behavior of the Phi29 DNA packaging motor is demonstrated.‡ The authors contributed equally to this article. IntroductionSingle molecule measurements of molecular motors allow to study the motion of individual enzymes. The studies range from enzymes making comparably large steps e.g. motor proteins like Myosin V [1] and Kinesin [2] to DNA based molecular machines which make steps on the scale of single nucleotides [3][4][5][6]. Experimental techniques to study these systems range from single molecule fluorescence localization [7] to optical and magnetic tweezers [8]. Most of these measurements represent the underlying dynamics as one-dimensional time series of positional changes. The enzymatic reactions which fuel this motion appear as stochastic events resulting in step-like movements [9] obliterated by noise. Nowadays state of the art optical tweezers experiments allow to study the movement of enzymes with a resolution down to single base pairs [3,10]. For example, studies on the ϕ29 bacteriophage ring ATPase [11][12][13] used the information from step detection data to propose a complete model of the mechanochemical cycle. However, oftentimes analysis schemes rely on low pass smoothed data.Indeed, the problem of finding steps is not only limited to studies of movement of enzymes but appears in a wide range of biomolecular experiments from fluorescence resonance energy transfer trajectories [14], to steps in membrane tether formation [15], or the opening of ion channels [16], just to name a few.Consequently, there is a rich amount of signal processing techniques available to recover piecewise constant signals from noisy data. Due to the stochastic nature of enzymatic stepping the number of steps is often not known a priori. Therefore, different step finding algorithms have been develop...

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Revolving ATPase motors as asymmetrical hexamers in translocating lengthy dsDNA via conformational changes and electrostatic interactions in phi29, T7, herpesvirus, mimivirus, E. coli, and Streptomyces

2023

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Investigations of the parallel architectures of biomotors in both prokaryotic and eukaryotic systems suggest a similar revolving mechanism in the use of ATP to drive translocation of the lengthy double‐stranded (ds)DNA genomes. This mechanism is exemplified by the dsDNA packaging motor of bacteriophage phi29 that operates through revolving but not rotating dsDNA to “Push through a one‐way valve”. This unique and novel revolving mechanism discovered in phi29 DNA packaging motor was recently reported in other systems including the dsDNA packaging motor of herpesvirus, the dsDNA ejecting motor of bacteriophage T7, the plasmid conjugation machine TraB in Streptomyces, the dsDNA translocase FtsK of gram‐negative bacteria, and the genome‐packaging motor in mimivirus. These motors exhibit an asymmetrical hexameric structure for transporting the genome via an inch‐worm sequential action. This review intends to delineate the revolving mechanism from a perspective of conformational changes and electrostatic interactions. In phi29, the positively charged residues Arg‐Lys‐Arg in the N‐terminus of the connector bind the negatively charged interlocking domain of pRNA. ATP binding to an ATPase subunit induces the closed conformation of the ATPase. The ATPase associates with an adjacent subunit to form a dimer facilitated by the positively charged arginine finger. The ATP‐binding induces a positive charging on its DNA binding surface via an allostery mechanism and thus the higher affinity for the negatively charged dsDNA. ATP hydrolysis induces an expanded conformation of the ATPase with a lower affinity for dsDNA due to the change of the surface charge, but the (ADP+Pi)‐bound subunit in the dimer undergoes a conformational change that repels dsDNA. The positively charged lysine rings of the connector attract dsDNA stepwise and periodically to keep its revolving motion along the channel wall, thus maintaining the one‐way translocation of dsDNA without reversal and sliding out. The finding of the presence of the asymmetrical hexameric architectures of many ATPases that use the revolving mechanism may provide insights into the understanding of translocation of the gigantic genomes including chromosomes in complicated systems without coiling and tangling to speed up dsDNA translocation and save energy.

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High Degree of Coordination and Division of Labor among Subunits in a Homomeric Ring ATPase

Cited by 112 publications

References 49 publications

Extracting signal from noise: kinetic mechanisms from a Michaelis–Menten‐like expression for enzymatic fluctuations

Extracting signal from noise: kinetic mechanisms from a Michaelis–Menten‐like expression for enzymatic fluctuations

Energy-based scheme for reconstruction of piecewise constant signals observed in the movement of molecular machines

Revolving ATPase motors as asymmetrical hexamers in translocating lengthy dsDNA via conformational changes and electrostatic interactions in phi29, T7, herpesvirus, mimivirus, E. coli, and Streptomyces

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