Conformational Memory of a Protein Revealed by Single-Molecule

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Conformational Memory of a Protein Revealed by Single-Molecule Spectroscopy Mario Schörner, Sebastian Reinhardt Beyer, June Southall, Richard J. Cogdell, and Jürgen Köhler J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.5b07494 • Publication Date (Web): 29 Sep 2015 Downloaded from http://pubs.acs.org on September 30, 2015

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The Journal of Physical Chemistry

Conformational Memory of a Protein Revealed by Single-Molecule Spectroscopy

Mario Schörner1, Sebastian Reinhardt Beyer1, June Southall2, Richard J. Cogdell2, Jürgen Köhler*1

1

Experimental Physics IV and Bayreuth Institute for Macromolecular Research (BIMF), University of Bayreuth, Germany

2

Institute of Molecular, Cell & Systems Biology, College of Medical Veterinary and Life Sciences, University of Glasgow, United Kingdom

*corresponding author: Prof. Dr. Jürgen Köhler Experimental Physics IV University of Bayreuth 95440 Bayreuth Germany phone: +49-921-55-4000 email: [email protected]

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Abstract Proteins are supramolecular machines that carry out a wide range of different functions many of which require flexibility. Up until now spontaneous conformational fluctuations of proteins have always been assumed to reflect a stochastic random process. However, if changing between different conformational states was random than it would be difficult to understand how conformational control of protein function could have evolved. Here we demonstrate that a single protein can show conformational memory. This is exactly the process that can facilitate the evolution of control of switching between two conformational states that can then be used to regulate protein function.

Keywords: conformational memory, protein dynamics, single-molecule spectroscopy, fluorescence intermittency


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Introduction Proteins perform multiple functions in living organisms many of which require conformational changes, such as seen with transporters, catalysts, or signal transducers etc.

1-5

. The current

picture is that proteins can assume many different conformations or conformational substates in order to fulfil their tasks

6,7

. These structural fluctuations can be rapid at room temperature

and introduce a microscopic randomness that gives rise to rich dynamics, covering many orders of magnitude in time

8–10

. Generally these fluctuations can be made visible by optical

spectroscopy of chromophores that are embedded in the protein matrix. Since the energies of the electronic energy levels of a chromophore are very sensitive to its interactions with the local surroundings, conformational fluctuations of the protein lead to changes in the chromophore-protein interactions that show up as spectral fluctuations (spectral diffusion) of the probe molecule. Therefore, optical spectroscopy provides a versatile tool with which to monitor the microscopic structure and relaxation dynamics of a protein via the spectral diffusion of the probe molecule

8,10–13

. However, as a consequence of the conformational

heterogeneity, protein ensembles exist in a broad variety of structures, which manifests itself as a dramatic increase in dynamic heterogeneity reflecting the distribution of the associated barriers that separate the various structures. In order to elucidate information that is commonly washed out by ensemble averaging single-molecule techniques have been exploited as well

14–21

. This allows these dynamic processes to be observed that are usually

obscured by the lack of synchronization within an ensemble, because a single protein that undergoes conformational fluctuations is at any time in a distinct, well-defined substate. Here we exploit the fluorescence fluctuations of the intrinsic chromophores of the LH2 complex in order to follow the conformational dynamics of its protein matrix. The lightharvesting complex 2 (LH2) from the photosynthetic purple bacterium Rps. acidophila accommodates 27 BChl a molecules that are arranged in two concentric rings displaced with respect to each other along the common symmetry axis perpendicular to the plane of the rings

22,23

. The two pigment pools are labelled B800 and B850, according to their room-

temperature absorption maxima in the near infrared

24

. Upon excitation of the B800 pigments



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the energy is transferred to the B850 molecules in less than 1 ps. The B850 assembly is a strongly coupled multichromophoric system with delocalised electronically excited states that can sense conformational fluctuations in a large part of the protein backbone

25,26

. This

avoids the need to disrupt the protein's structure by the attachment of exogenous fluorescent labels. Fluorescence intermittency, also termed blinking, refers to the fluctuations of the fluorescence intensity of a chromophore between high and low signal levels, commonly termed "on" and "off", and is a ubiquitous phenomenon in single-particle spectroscopy. It covers essentially all experimentally accessible time scales, and has been observed for a large variety of materials including semiconductor quantum dots, organic dye molecules, as well as organic multichromophoric systems, polymers, or pigment-protein complexes

21,27–29

.

Fluorescence intermittency of single LH2 complexes has been addressed recently exploiting an anti-Brownian electrokinetic trap (ABEL) for immobilising the complexes in solution 30. The authors recorded simultaneously the fluorescence intensity, emission spectra and the fluorescence lifetime of an individual complex. This allowed them to identify different emissive states that were associated with a photoactivated, reversible quenching pathway that most likely reflected a conformational change of the protein. Since in the ABEL trap the feedback signal that compensates for the diffusion of the protein is generated from the emission of the sample, the observation times are typically limited to some ten seconds before the particle crosses over to the off state and gets lost from the trap. It is, however, possible to extend the observation time by immobilizing LH2 in a polymer film under conditions where this does not significantly perturb the structure of the protein

31,32

. This

approach has enabled us to follow the dynamics of the LH2 fluorescence intermittency over an unprecedented nine orders of magnitude in time. The concomitant huge statistical data base that this has produced has allowed us to uncover a remarkable memory phenomenon within these fluorescence intensity fluctuations of the intrinsic chromophores embedded within LH2 and, moreover, to show a strong correlation between the residence times at the various signal levels.


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Results and Discussion Intensity Levels We recorded the fluorescence intensity from 40 individual LH2 complexes for 3600 s per complex. In fig.1a-c we show examples of fluorescence-intensity traces for three different LH2 complexes.

Figure 1: a)-c) Three representative fluorescence-intensity traces from different individual LH2 complexes. The data have been obtained by time-tagged time-resolved single-photon detection and subsequent change-point analysis (CPA). The traces shown have been reconstructed from the data with 100 ms binning time (red lines) for better display. For convenience the fluorescence intensity on the vertical axis is given as a time-averaged measure in counts per second. Left: Full data set recorded for one hour. Right: Expanded view of a short part of the trajectory that is highlighted on the left hand side (yellow bar). From top to bottom the number of the different fluorescence-intensity levels that have been identified are 2, 3 and 4. These levels (black lines and dots) are referred to as A, B, C, and OFF (vide infra). d)-f) Distributions of the intensities for the LH2 complexes featuring 2 (d), 3 (e), and 4 (f) signal levels, respectively.

Applying a change-point analysis (CPA)33 allows us to also uncover intensity levels that appear for only a short period, as indicated by the arrows in fig.1a-c. For the examples shown in fig.1a-c this reveals changes of the fluorescence intensity between 2, 3, and 4 distinct levels, respectively. For the total of the 40 complexes studied, the distribution of the number of the observed intensity levels ranges from 1 to 9. For the 6 complexes that feature only a single intensity level it was impossible to unambiguously distinguish different intensity levels either because of signal-to-noise limitations or simply because the particular complex



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did not change its emission status during the full observation period of 3600 s. For another 6 complexes, where the algorithm assigns 5, 7, or 9 intensity levels, the differences between the signals in the various levels are very small and might give rise to an over-interpretation of the data. Therefore, the following analysis and discussion of the data will be focused on the 28 complexes that feature 2, 3, or 4 distinct intensity levels and which represent 70% of the complexes studied. For each complex that features 2-level blinking, i.e. "on" and "off", the time-averaged signal level in the on- and off-states is shown in fig.1d by the red (on) and blue (off) bars, respectively. The corresponding time-averaged intensity distributions for each complex featuring 3-level or 4-level blinking are shown in fig.1e, f. For the complexes that feature 2level blinking we find an average count rate of 98±32 counts per second (cps) for the onstate and 26±10 cps for the off-state. These count rates nicely reproduce the averaged highest and lowest intensity levels of the 3-level and 4-level blinking complexes. For the lowest but one intensity of the 3- and 4-level blinking systems we find an average count rate of 47±15 cps and 39±15 cps, respectively, which are effectively the same within the limits of experimental accuracy. Finally, for the highest but one intensity level of the 4-state blinking complexes we obtain an average value of 61±21 cps. Based on this analysis and the characteristic probability densities for the residence times that have been found for each level upon analysis of the blinking statistics (see accompanying paper) we have made the following assignments. The level with the highest intensity will be referred to as A, and the one with the lowest signal, which corresponds to the background level, will be termed OFF. The level with a count rate around 40 cps that is observed for both 3- and 4-level blinking will be referred to as B, and finally the additional level with an intensity around 60 cps, that is observed only for 4-level blinking, will be termed C.

Table 1: Intensities (mean ± standard deviation) observed in the various signal levels for the three types of blinking averaged over time and complexes. 2 intensity levels 3 intensity levels 4 intensity levels A [counts/s] 98 ± 32 100 ± 35 99 ± 18 B [counts/s] 47 ± 15 39 ± 15 C [counts/s] 63 ± 21 OFF [counts/s] 26 ± 10 20 ± 6 22 ± 4 6 Environment ACS Paragon Plus

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The assignments and the average intensity levels, as well as their standard deviations are summarized in Table 1. It is useful to note here that in order to be compatible with the previous paper by the Moerner group

30

, we have named the different intensity levels in the

order A < C < B < OFF. This should avoid confusion between their paper and the present study.

Sequences

3-level systems. For the LH2 complexes that feature three distinct intensity levels we find initially 38% of the complexes starting with their fluorescence intensity in level A, 34% starting in level B, and 28% starting in OFF, fig.2a-c. Consistent with previous experimental data we hypothesize that we can associate the various intensity levels with specific conformational substates of the protein, where the energy landscape of the protein conformations is connected to the measured fluorescence intensity levels of the intrinsic reporter chromophores

17,30,34,35

. The variation in the starting conditions is then ascribed to

the fact that upon embedding the LH2 complexes in a polymer matrix the proteins experience different local surroundings and get trapped in a distinct starting conformational substate, see fig.2d-f. In order to search for correlations between successive intensity jumps we have analysed the chronological order of the signal levels and determined the occurrence of sequences such as A-B-A, A-B-OFF, A-OFF-A, or A-OFF-B. This was repeated for all other permutations of the intensity levels. Statistically this can be described with the concept of conditional probabilities for which we will use the notation pA-B(B-A), which, as an example, refers to the conditional probability that the system is going to change from B to A if it has made the transition from A to B in the previous jump. For this particular conditional probability we have recorded a remarkable 90% instead of 50% which one would expect for stochastically independent random events.



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Figure 2: a)-c) Pictorial representation of the probabilities of the intensity jumps starting from A (a), B (b), or OFF (c), respectively. The total number of LH2 complexes that are initially in level A (B, OFF) is 829 (749, 598) corresponding to a relative fraction of 38% (34%, 28%). The numbers given in the panels correspond to the probabilities (in per cent) to observe a distinct intensity change and have an accuracy of ±3%. Next to each panel the total probability for returning to the initial state within two consecutive intensity jumps is given. d)-f) Sketch of the energy landscape of the protein embedded in the polymer matrix. The numbers denote the conditional probability to jump back with the second intensity change to the initial state. g)-l) Correlations between the residence times in the levels X1-Y-X1. The residence time for the intermediate level is given by the colour code.

Similarly, we find pA-OFF(OFF-A)=65% for the conditional probability if the intermediate level corresponds to the OFF state. The total probability that a system that resides initially in state A jumps back and forth in two consecutive jumps is given by p2(A) = pA-B(B-A) p1(B) + pAOFF(OFF-A)p1 (OFF),

where the indices 1 and 2 refer to the first and second jump, for which

we obtain a value of p2(A) = 79%. The results of this analysis are summarized in fig.2a-c in a pictorial representation and reveal a strong preference for the system to return with the second jump to the intensity level that was occupied prior to the first jump. In all but one case, both the conditional probabilities for a jump back to the initial state as well as the total probabilities for returning to the initial state within two consecutive jumps clearly exceed the 8 Environment ACS Paragon Plus

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expected probability of 50% for stochastically independent events. In addition we have searched for correlations in the corresponding residence times in the three sequential levels. In order to distinguish the two X-states in a sequence like X-Y-X they will be referred to as X1 and X2 and have the associated residence times τX1 and τX2, respectively. Correlating the residence times, τX1 and τX2, during sequences of the type X-Y-X yields two striking features, fig.2g-l: First the lack of short residence times if the protein does not reside initially in state A, and 2) clearly observable correlations between the residence times for some sequences of intensity jumps. For example for the sequence A1-B-A2 (fig.2g) we find: i) Long times τA1 are followed by short times τB; ii) short times τA1 are followed by long times τB; iii) long (short) times τA1 are followed by long (short) times τA2. In general we find: i) For sequences of the type A1-X-A2 or B1-A-B1 the residence times in all three states are correlated (fig.2g, h, i); ii) For sequences OFF1-X-OFF2 the residence times in the intermediate state are short if X=A (fig.2k), whereas all residence times are uncorrelated for X=B (fig.2l). However, even though there is no memory in the residence times the conformational memory is conserved in this configuration, i.e. p2(OFF) = pOFF-A(A-OFF) p1(A) + pOFF-B(B-OFF)p1(B) = 78%, see also fig.2c. iii) For sequences B1-OFF-B2 neither correlations in the residence times nor any conformational memory is observed (fig.2j). Here the observed conditional probability pB1-OFF(OFF-B) = 53% and is close to the expected 50% for stochastically indenpendent events, (fig.2b. iv). For all other permutations of X-Y-Z no correlations are observable in the corresponding residence times.

4-level systems. For the 4-level systems the distribution across the various initial intensity levels is 37% (A), 19% (C), 25% (B), and 19% (OFF), fig.3a-d. For the A state in the 4-level systems

we

observe

pA-B(B-A)=70%,

pA-C(C-A)=80%,

and

pA-OFF(OFF-A)=50% for the conditional probabilities, and p2(A) = pA-B(B-A) p1(B) + pA-C(CA)p1(C) + pA-OFF(OFF-A)p1(OFF) = 68% for the total probability to return to the A state. Also if the systems start from another initial intensity level than A all probabilities clearly exceed the value of 33% that would be expected for independent events. The results for all the intensity levels in the 4-level systems are summarized in fig.3a-d in a similar way as in fig.2. 9 Environment ACS Paragon Plus


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Figure 3: a)-d) Pictorial representation of the probabilities of the intensity jumps starting from A (a), C (b), B (c), or OFF (d), respectively. The total number of LH2 complexes that are initially in level A (C, B, OFF) is 717 (373, 484, 382) corresponding to a relative fraction of 37% (19%, 25%, 19%). The numbers given in the panels correspond to the probabilities (in per cent) to observe a distinct intensity change and have an accuracy of ±3%. Next to each panel the total probability for returning to the initial state within two intensity jumps is given. e)-p) Correlations between the residence times in the levels X1-Y-X1. The residence time for the intermediate level is given by the colour code.

Again we do not observe short residence times if the LH2 complex does not reside initially in state A. Correlations in the residence times are not as pronounced as for the 3-level systems but are still observable for some sequences. Most clearly for A1-OFF-A2 and OFF1-A-OFF2, where the residence times in all three states are correlated (fig.3g, n). In particular we find for the sequence A1-OFF-A2: i) long times τA1 are followed by short times τOFF; ii) short times τA1 are followed by long times τOFF; iii) long (short) times τA1 are followed (mainly) by long (short) times τA2 (fig.3g). For the sequence OFF1-A-OFF2: i) long times τOFF1 are followed by short times τA and ii) and those short times τA are then followed again by long times τOFF2 (fig.3n). Finally, some weaker correlations are observable for the sequence B1-A-B2: i) long times τB1 are often followed by short times τA and ii) short times τA are often followed by long times τB2 (fig.3k). For all other sequences a statement about correlations between the residence times


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cannot be made. From a detailed analysis of all the data we can deduce the following generalizations: For both the 3- and 4-level systems a) A-X-A is highly likely (measured p2(A) =79% instead of expected 50% for the 3-level systems and measured 68% instead of expected 33% for the 4level systems); b) OFF-X-OFF is highly likely (p2(OFF) = 78% instead of 50% for the 3-level systems and 73% instead of 33% for the 4-level systems), and c) If state A is involved in the intermediate step, i.e. Y-A-Y, a strong memory is observed for back and forth jumps from a particular state Y; d) for the 4-level systems there is no significant memory for transitions between B and C. If we associate the measured fluorescence intensity levels of the intrinsic reporter chromophores with specific conformational substates of the protein, it is then clear that not all of these substates exhibit memory in their structural dynamics. Fluctuations to and from the conformational substate that corresponds to the measured fluorescence intensity level A show the strongest memory effect. This memory effect becomes even more clear if we ask how many intensity jumps are required until the probability for staying within one sequence Y-X-Y drops below 10%. Remarkably, in the three level systems this takes 20 jumps for the sequences A-B-A and B-A-B, and even in the four level systems this takes 6 jumps for the same sequences. Here, the slowest decay of the memory is found for the sequence C-A-C, which needs 8 jumps to become less probable than 10%. For statistically independent jumps of these intensities the respective probabilities would be 0.520 ≈ 10-6, 0.336 ≈ 1.3⋅10-3, and 0.338 ≈ 1.4⋅10-4. More detailed information on this point is provided in the supplementary information, see fig.SI1. If evolution is to act to optimize control of such fluctuations between conformational states of a protein then a memory such as demonstrated in this paper would be an important precondition. Without this memory the randomness would make the possibility of evolving control over conformational states much more difficult if not impossible. An important example of where such changes in protein conformation are used to regulate function is the case of non-photochemical quenching seen in the light-harvesting system of plants 11 Environment ACS Paragon Plus

36,37

. Too


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much light is harmful for plants and they have evolved a way to be able to switch the efficiency with which their light-harvesting complexes deliver excitation energy to the reaction centres. This switching appears to involve changes in the conformation of the light-harvesting complexes

38

. Conformational memory such as that shown here may well have allowed this

control over the efficiency of the light-harvesting system to have evolved in plants.


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Materials and Methods Materials and Sample Preparation. The LH2 complexes of Rps. acidophila (strain 10050) were isolated and purified as described previously

39

. After purification the LH2 complexes

were aliquoted and stored at -80°C in a buffer (20 mM TRIS/HCL, pH 8, 0.2% LDAO) in the dark. The stock solution was diluted with pure buffer in three steps to a concentration of 10-11 M, where in the last dilution step 2% polyvinyl alcohol (PVA) was added. Subsequently the material was spincoated on a fused quarz substrate forming a thin film with a thickness of about 100 nm 40 in which the LH2 complexes were embedded.

Experiments on Single Complexes. For the optical experiments the sample was mounted in a recipient that was flushed with helium gas which reduced the concentration of oxygen to less than 1 ppb and minimizes irreversible photobleaching of the complexes. The LH2 complexes were excited at 800 nm with the output from a continuous Titanium:Sapphire laser (Model 3900s, Spectra Physics) that was reflected into a home-built scanning confocal microscope (objective: Euromex Holland, S.PlanM 60x, NA = 0.85) via a fused quarz wedge that served as beam splitter. The excitation fluence was adjusted to 2.2×1020 photons/s, corresponding to 85 W/cm² to avoid saturation of the LH2 complexes

41

. The red-shifted

emission from the LH2 complexes was collected with the same objective and directed through a long-pass filter (LP808, AHF Analysentechnik AG) towards an avalanche photo diode (Micro Photon Devices, PD5CTC). In order to select the individual complexes a confocal image of a sample region of about 30 × 30 µm2 was acquired with the microscope. Subsequently, the confocal spot of the microscope was adjusted to coincide with the position of one of the complexes found in the image and the fluorescence intensity of this complex was registered as a function of time. All experiments were carried out at room temperature.

Data Acquisition and Analysis. Time-tagged time-resolved single-photon detection was achieved using a TCSPC module (Pico Quant Timeharp 200) in TTTR mode. This provided a temporal resolution of 100 ns. The flow of the emitted photons from an individual LH2



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complex was analysed according to the change-point analysis (CPA) algorithm developed by Watkins et al.

33

and that was adapted for handling the timeharp-t3r-files. In order to retrieve

those moments when changes in the emitted intensity occur, a generalized likelihood ratio test is recursively applied to the arrival times of the individual photons in the entire recorded fluorescence-intensity trace. The number of different intensity levels that are present in a photon time trace are then determined using a Bayesian information criterion together with expectation-maximization clustering.

Acknowlegement M.S., S.R.B., and J.K. thankfully acknowledge financial support by the Deutsche Forschungsgemeinschaft (GRK1640) and the State of Bavaria within the initiative “Solar Technologies go Hybrid”. J.S. and R.J.C. thank the BBSRC for support.

Supporting Information Available - Long Term Memory of Conformational Changes This information is available free of charge via the Internet at http://pubs.acs.org


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