Single-molecule FRET methods to study the dynamics of proteins at work

Mazal, Hisham; Haran, Gilad

doi:10.1016/j.cobme.2019.08.007

Cited by 113 publications

(88 citation statements)

References 94 publications

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“…For clarity, we call ∆t the data acquisition period. This assumption is consistent with a number of experimental biophysical settings (11,12,(38)(39)(40)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56).…”

Section: Introductionsupporting

confidence: 86%

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Kilic

Sgouralis

Pressé

2020

Preprint

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The hidden Markov model (HMM) is a framework for time series analysis widely applied to single molecule experiments. It has traditionally been used to interpret signals generated by systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, originally developed for applications outside the Natural Sciences, such as speech recognition, transitions between states, such as molecular conformational states, are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, while measurements are often performed at discrete time levels in the Natural Sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, as the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, as these switch between discrete states in continuous time. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process (HMJP). We explicitly show in what limit the HMJP reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

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Section: Introductionsupporting

confidence: 86%

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Kilic

Sgouralis

Pressé

2020

Preprint

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“…The FRET pair configuration displayed in Figure 2a (top) results in low FRET efficiency (little acceptor fluorescence) for V-shaped, open conformations of Hsp90, and high FRET efficiency (intense acceptor fluorescence) for more compact, closed conformations. This allows us to obtain steady-state information, like the population of closed conformations, and also the kinetics of conformational changes ( Schmid et al, 2016 ; Roy et al, 2008 ; Lerner et al, 2018 ; Mazal and Haran, 2019 ). Example traces obtained from three Hsp90 molecules under different conditions are displayed in Figure 2b : First for the point mutant A577I, then in the presence of the cochaperone Aha1, and lastly under macro-molecular crowding by Ficoll400.…”

Section: Resultsmentioning

confidence: 99%

Controlling protein function by fine-tuning conformational flexibility

Schmid

Hugel

2020

eLife

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In a living cell, protein function is regulated in several ways, including post-translational modifications (PTMs), protein-protein interaction, or by the global environment (e.g. crowding or phase separation). While site-specific PTMs act very locally on the protein, specific protein interactions typically affect larger (sub-)domains, and global changes affect the whole protein non-specifically. Herein, we directly observe protein regulation under three different degrees of localization, and present the effects on the Hsp90 chaperone system at the levels of conformational steady states, kinetics and protein function. Interestingly using single-molecule FRET, we find that similar functional and conformational steady states are caused by completely different underlying kinetics. We disentangle specific and non-specific effects that control Hsp90’s ATPase function, which has remained a puzzle up to now. Lastly, we introduce a new mechanistic concept: functional stimulation through conformational confinement. Our results demonstrate how cellular protein regulation works by fine-tuning the conformational state space of proteins.

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“…The FRET pair configuration displayed in Figure 2a (top) results in low FRET efficiency (little acceptor fluorescence) for v-shaped, open conformations of Hsp90, and high FRET efficiency (intense acceptor fluorescence) for more compact, closed conformations. This allows us to obtain steady-state information, like the population of closed conformation, and also the kinetics of conformational changes 29,38–40 . Example traces obtained from three Hsp90 molecules under different conditions are displayed in Figure 2b: for the point mutant A577I, in the presence of the cochaperon Aha1, or under macro-molecular crowding by Ficoll400.…”

Section: Resultsmentioning

confidence: 99%

Same Equilibrium. Different Kinetics. Protein Functional Consequences

Schmid

Hugel²

2019

Preprint

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12 In a living cell, protein function is regulated in several ways, including post-translational 13 modifications (PTMs), protein-protein interaction, or by the global environment, e.g. by 14 crowding or phase separation. While site-specific PTMs act very locally on the protein, spe-15 cific protein interactions typically affect larger (sub-)domains, and global changes affect the 16 whole protein non-specifically. 17 Herein, we directly observe protein regulation with three different degrees of localization, 18 and present the effect on the Hsp90 chaperone system on the level of conformational equi-19 libria, kinetics and protein function. Interestingly, we find by single-molecule FRET that sim-20 ilar functional and conformational steady-state observations are caused by completely dif-21 ferent underlying kinetics. Solving the complete kinetic rate model allows us to disentangle 22 specific and non-specific effects controlling Hsp90's ATPase function, which has remained 23 puzzling up to date. Lastly, we introduce a new mechanistic concept: functional stimulation 24 through conformational confinement. Our results highlight how cellular protein regulation 25 works by fine-tuning the conformational state space of proteins. 26Protein function is essential for life as we know it. It is largely encoded in a protein's amino-acid 27 chain that dictates not only the specific 3D structure, but also the conformational flexibility and dy-28 namics of a protein in a given environment. Precise regulation of protein function is vital for every 29 living cell to cope with an ever-changing environment, and occurs on many levels pre-and post-30 translationally 1,2 . After translation by the ribosome, protein function depends strongly on post-31 translational modifications (PTMs) 3 , but also on binding of nucleotides 4 , cofactors 5 , various protein-32 protein interactions (PPIs) 6 , and global effects, such as temperature 7 , macro-molecular crowding 33 and phase separation 8 , redox conditions 9 , osmolarity 10 etc. Importantly, this regulation occurs on 34 very diverse levels of localization. Global effects affect the whole protein non-specifically, PPIs act 35 at a given interface and site-specific modifications are very localized. Nevertheless, all of them in-36 fluence the molecular properties that determine the 3D conformation, the conformational dynamics, 37 and thereby also the function of a protein [11][12][13][14][15] . The chaperone protein Hsp90 16 is an excellent test 38 system to investigate diverse regulation mechanisms 17 . It was recently discussed that a single 39 PTM can functionally mimic a specific co-chaperone interaction in human Hsp90 18 . Here we take a 40 next step and disentangle how a PTM-related point mutation, a co-chaperone interaction, and 41 macro-molecular crowding affect the function, kinetics, and thermodynamics of this multi-domain 42 protein. 44Hsp90 is an important metabolic hub. Assisted by about twenty known cochaperones, yeast Hsp90 45 is involved in the maturation of 20% of the entire...

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Single-molecule FRET methods to study the dynamics of proteins at work

Cited by 113 publications

References 94 publications

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Controlling protein function by fine-tuning conformational flexibility

Same Equilibrium. Different Kinetics. Protein Functional Consequences

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