Spontaneous Fluctuations Can Guide Drug Design Strategies for

Publication Date (Web): June 21, 2018 ... Structure-based “rational” drug design strategies fail for diseases associated with intrinsically disord...
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Spontaneous Fluctuations can Guide Drug Design Strategies for Structurally Disordered Proteins Barun Kumar Maity, Vicky Vishvakarma, Dayana Surendran, Anoop Rawat, Anirban Das, Shreya Pramanik, Najmul Arfin, and Sudipta Maiti Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.8b00504 • Publication Date (Web): 21 Jun 2018 Downloaded from http://pubs.acs.org on June 22, 2018

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Spontaneous Fluctuations can Guide Drug Design Strategies for Structurally Disordered Proteins Barun Kumar Maity#, Vicky Vishvakarma#, Dayana Surendran#, Anoop Rawat#, Anirban Das#, Shreya Pramanik$, Najmul Arfin†, Sudipta Maiti#* #Department of Chemical Sciences, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400005, India $

UM-DAE Centre for Excellence in Basic Sciences, Univ. of Mumbai, Kalina, Mumbai 400098,

India †

Center for Interdisciplinary Research in Basic Sciences, Jamia Milia Islamia, New Delhi,

110025, India.

ABSTRACT: Structure-based ‘rational’ drug-design strategies fail for diseases associated with intrinsically disordered proteins (IDPs). However, structural disorder allows large amplitude spontaneous intramolecular dynamics in a protein. We demonstrate a method that exploits this dynamics to provide quantitative information about the degree of interaction of an IDP with other molecules. A candidate ligand molecule may not bind strongly, but even momentary interactions can be expected to perturb the fluctuations. We measure the amplitude and frequency of the equilibrium fluctuations of fluorescently labeled small oligomers of hIAPP (an IDP associated with Type II diabetes) in a physiological solution, using nanosecond fluorescence cross-correlation spectroscopy. We show that the inter-terminal distance fluctuates at a characteristic timescale of 134 ± 10 ns, and 6.4 ± 0.2 % of the population is in the ‘closed’ (quenched) state at equilibrium. These fluctuations are affected in a dose-dependent manner by a series of small molecules known to reduce the toxicity of various amyloid peptides. The degree of interaction shows the following order: resveratrol < epicatechin ~ quercetin < congo red < epigallocatechin-3-gallate. Such ordering can provide a direction for exploring the chemical space for finding stronger-binding ligands. We test the biological relevance of these measurements by measuring the effect of these molecules on the affinity of hIAPP for lipid vesicles and cell membranes. We find that the ability of a molecule to modulate intramolecular fluctuations correlates well with its ability to lower membrane affinity. We conclude that structural disorder may provide new avenues for rational drug design for IDPs. 1 ACS Paragon Plus Environment

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1. INTRODUCTION Intrinsically disordered proteins (IDPs) are associated with many of the human diseases in search of urgent cures, such as Alzheimer’s, Parkinson’s and Type II diabetes1-4. Designing small molecule drug candidates for these IDPs has proven to be a challenge, since rational drug design relies on the structure of the target protein, but these proteins are fully or partially disordered under physiological conditions. However, many of these disordered proteins/ protein segments can bind specific peptides or molecules in vivo and acquire specific structures, which is important for their function5-7. In fact, strong binding can happen even without an IDP acquiring a structure8, 9. Hence, it is possible that many of the IDPs will bind appropriately designed molecules, which can act as modulators of their (toxic) functions10. However, in absence of any structural cues, designing such a molecule for a particular IDP from scratch is rather difficult. Of course, many IDPs acquire a cross beta-sheet structure when they form large aggregates in the diseased state11-16. However, the smaller oligomeric aggregates, which are still considerably disordered, are thought to be the key toxic species17-20. Therefore, the challenge of designing a ligand for disordered proteins remains important. Such designing so far has had to depend on indirect assays (e.g. aggregation assays), or biological assays (e.g. cell viability assays)21, 22. However, aggregation assays can be misleading since formation of large aggregates may not be causally related to toxicity. Biological assays can be long drawn, and may not imply a direct interaction of the molecule with the IDPs. Ion mobility mass spectrometry can yield information about ligand interaction, but only if the ligand substantially changes the charge or conformational state of the IDP23. Therefore, most promising small molecules, which can provide a starting point for drug development (e.g. EGCG, resveratrol etc.), have been found by screening, and not by design21, 22. Here, we propose to utilize the intrinsic dynamics of a disordered protein as a guide for ligand selection. The disorder of an IDP should allow it considerable large-scale low-frequency fluctuations, and this intra-molecular diffusion would be manifest at different length and temporal scales. We expect this dynamics to be sensitive to ligand interactions, even if such interactions are weak and momentary. Thus, a quantitative measurement of the dynamics (amplitude and timescale of fluctuation) can be a measure of the degree of interaction of a given small molecule with an IDP. In fact disorder has already been proposed as a parameter for drug design, but only in the context of molecular dynamics simulation studies24, 2 ACS Paragon Plus Environment

25

. Here we

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demonstrate an effective experimental approach using ns timescale Fluorescence Correlation Spectroscopy (FCS). FCS is a proven technique for measuring equilibrium fluctuations26-28. It is capable of following fast dynamics in the ns to µs time scale even at very low concentrations29-31. Spontaneous molecular fluctuations of unfolded proteins and IDPs have been measured using fluorescence correlation spectroscopy (FCS)32-34. This temporal regime is difficult to access by the single molecule Förster resonance energy transfer (smFRET) technique, and falls in the nuclear magnetic resonance (NMR)-inaccessible time window35, 36. We note that the changes in the donor lifetime may reflect ligand interactions, but only if the average distance between the FRET pairs change due to such interactions37, 38. This is not a requirement for fluctuation-based assays, as explained below. Other techniques, such as triplet-triplet energy transfer or tryptophan quenching, can also monitor the dynamics at sub-µs timescale, but have relatively low sensitivity 39, 40

. The sensitivity of FCS allows it to probe low concentrations of the peptides, which makes it

rather advantageous especially for IDPs, as they are typically aggregation prone even at µM concentration range41. Distance dependent stochastic quenching of a fluorophore can be mediated by photo-induced electron transfer32 or Förster Resonance Energy Transfer (FRET) processes, and gives rise to fluorescence fluctuations. The temporal autocorrelation of this fluctuation would decay with the characteristic time scale of the fluctuations. The window of such measurements (few ns to few µs) is bounded at the lower end by the fluorescence lifetime (which gives rise to the anti-bunching effect in FCS), and at the higher end by singlet-triplet photo-physics and/or diffusion. The autocorrelation function can be fit approximately to a twostate model, where the two most useful parameters are the fraction of the population in the dark state (f1), and the timescale (τ1) of the fluctuation (see Equation 1). Upon ligand interaction, both of these quantities may be affected. We note that transition between the ‘open’ (bright) and the ‘closed’ (dark) state (whose spatial separation is determined by the quenching process) is spanned by a continuum of conformational states, some of which may be quasi-stable. A two state model effectively divides them into two groups. While the autocorrelation data remain valid irrespective of the model chosen, the values of f1 and τ1 do depend on the model. However, while absolute values of f1 and τ1 may be difficult to interpret in a multi-state scenario, changes in these values serve as a robust measure of the degree of interaction of the IDP with a potential ligand. Our goal in this study is to produce a quantitative comparison of the degree of interaction 3 ACS Paragon Plus Environment

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of a given set of small molecules with an IDP, even if none of them binds stably to it. Such ordering of a set of candidate molecules can provide a logical direction to drug design. Here, we study hIAPP in its putatively toxic oligomeric state18. We chose several small molecules, which are known to reduce the toxicity of many amyloids as test ligands, though none of these molecules has been reported to bind strongly to hIAPP. Our test set was comprised of EGCG, epicatechin, congo red, resveratrol and quercetin, and we measured the degree to which these molecules affect the fluctuation dynamics of hIAPP oligomers. Finally, we tested if perturbation of the dynamics has any bearing on any functional property of the IDP, such as its ability to bind artificial or cellular membranes. Membrane binding is hypothesized to be a key step in the pathway mediating the toxicity of these oligomers42,

43

, and it has been shown to increase many folds when the monomers form

oligomers, for amyloid beta (an IDP associated with Alzheimer’s disease)44,

45

and also for

hIAPP46. We have previously shown that attachment to vesicular membranes can be measured by FCS44, and that to cell membranes can be measured by fluorescence imaging45. We performed both these measurements in presence of these small molecules, and compared our results with our fluctuation based measures of ligand interaction.

2. MATERIALS AND METHODS 2.1. Materials. Materials for peptide synthesis, solvents, and other reagents were procured from standard sources as detailed in the SI. 2.2. Peptide synthesis, FRET labeling, and purification. Dye labeled hIAPP specimens were synthesized using an automated solid phase peptide synthesizer, purified with an HPLC, and characterized with a mass spectrometer. Details are given in the SI. 2.3. Instrumental set up. We have constructed the cross correlation FCS instrument in house31, and details are given in the SI. Briefly, a 532 nm laser beam excites the sample in a confocal geometry. The emission is split into two channels using a beam splitter and the fluorescence is detected by single photon avalanche photodiodes. The signal is collected and processed using appropriate cross-correlator hardware. 2.4. Fitting methods. The correlation functions were fitted with Equation-147 to measure the fraction of the dark state population (f1) and the characteristic time of spontaneous fluctuations (τ1). The other parts of Eq.1 are the triplet component (fraction = f2 and characteristic time = τ2), 4 ACS Paragon Plus Environment

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and the diffusion component (N = number of particles in the probe volume, g1 and g2 = amplitudes of two diffusing components, τD1 and τD2 = characteristic diffusion times for the two components, a = structure factor for the optical probe volume which is assumed to be a Gaussian ellipsoid, bl = background level)27. The equation is valid if the time scales of different processes are well separated. For ease of visualization, the amplitudes of the correlation functions in the graphs were adjusted by multiplying with suitable normalization constants. These constants were obtained by tail-fitting the data in the longer time scale (1.97 µs onwards). The tail-fitting excludes the fast fluctuation process by setting f1 = 0 in Equation 1.  

 

   ∗  ∗ 

 = 

  

 ∗ ∑   











∗



   



 + "#

................................... (1) 2.5. Statistical analysis. Statistical significance for differences between the dark state fractions of the dynamics population for DA-hIAPP oligomers in absence and in presence of small molecules was calculated with one-way ANOVA comparison as a paired two-tailed Student’s ttest. Differences were considered significant with p < 0.05.

3. RESULTS 3.1. Measurement of spontaneous fluctuation of hIAPP oligomers. We have employed fluorescence correlation spectroscopy (FCS) at the ns timescale to investigate spontaneous fluctuation of hIAPP oligomers. hIAPP is labeled with a donor fluorophore D (Rhodamine-B) at the C-terminal, and with a quencher molecule A (Dabcyl) at the N-terminal. The two termini were chosen for labeling as they are appropriate for probing the perturbation on the global dynamics, and also because the termini (which are reasonably hydrophilic) are expected to be accessible. Förster Resonance Energy Transfer (FRET) can take place between D and A. When the distance between the donor and the quencher molecules is much less than the Förster radius, the donor molecule is in the dark state. As the distance increases it converts to the bright state (this description simplifies an essentially multistate ensemble to a two state one). Given the Förster radius of the chosen dye pair (R0 =2.1 nm in water), the bright-dark conversion represents large amplitude fluctuations of the molecule in the range of 2 nm. The temporal crosscorrelation of the fluorescence fluctuations of the donor (D, control specimen, black) and the 5 ACS Paragon Plus Environment

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donor-acceptor (DA, FRET specimen, red) labeled hIAPP specimens are shown in Fig. 1A. Fig. 1A also shows data from free Rhodamine-B dye (green). The data suggest that both D and DA labeled hIAPP oligomers are similar in size, as the red and black traces are nearly identical in the ms-µs region where diffusion dominates the autocorrelation (the traces have been normalized as described in experimental section). But in the sub-µs domain, the correlation function displays an extra amplitude component for DA, and also to a lesser extent, for D (see Fig. 1B, which presents a magnified view of the faster time scale blue shaded region of Fig. 1A). The extra amplitude of the DA specimen represents the average fraction of the population that is in the dark state. Fig. 1B shows that for DA-hIAPP, this is about 6.4 ± 0.2 %. It also shows that the kinetics of the spontaneous fluctuations, which lead to transitions between the dark and the bright states, has a time scale of 134 ± 10 ns. In comparison, the D specimen shows an extra amplitude of 2.7 ± 0.5 % with a time scale of 215 ± 73 ns in the fluctuation time domain.

Figure-1: (A) Normalized correlation function as a function of delay time of 100 nM hIAPP oligomers (only donor labeled: black; both donor and acceptor labeled: red) and free RhB dye (green) in PBS. (B) Magnified view of the intra-molecular fluctuation region of fig. (A). (C): characteristic fluctuation time of 100 nM DA labeled hIAPP as function of viscosity, error bars represent the standard deviation (n ≥ 3) (D) Normalized correlation function vs delay time of 100 6 ACS Paragon Plus Environment

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nM DA labeled hIAPP oligomers at two different optical probe volumes (smaller, blue; larger, green) To test whether this difference in fluctuations between D and DA labeled hIAPP oligomers is indeed due to intramolecular diffusive fluctuations, we measured the fluctuation time of DA labeled oligomers at different viscosities of the medium. The viscosity was controlled by mixing glycerol and phosphate buffer solution (PBS) in different proportions. We observed that the fluctuation time constant varied approximately linearly with the viscosity of the medium (Fig. 1C). This is consistent with our interpretation that the extra amplitude of the correlation function in the ns time domain is due to large amplitude intramolecular motions. Such intramolecular diffusion should be independent of the probe volume. We tested this by varying the probe volume (Fig. 1D, smaller volume: blue; larger volume: green). We did not observe any change either in f1 or in τ1. On the other hand, the diffusion timescale of the whole molecule changed by a factor of 1.3, as can be expected. 3.2. Effect of small molecules on spontaneous fluctuations. We then measured the correlation functions of 100 nM DA labelled hIAPP oligomers in PBS at pH 7.4 in presence of different potential small molecule ligands (Epigallocatechin 3-gallate (EGCG), epicatechin, quercetin, congo red and resveratrol). We observed that the inherent fluctuations of hIAPP oligomers were perturbed to a considerable extent by 400 nM of quercetin, a small organic molecule abundant in many pigmented plants (available for human consumption through red wine, green tea etc.). The extra amplitude f1 in the correlation function decreased from 6.4 ± 0.2 % to 4.9 ± 0.2% (Fig. 2). This effect increased at higher concentrations of quercetin (Figs. S3A and S3C), and the time scale τ1 of fluctuations decreased with increasing concentration (Fig. S3D). In addition, the correlation functions in the ms-µs domain (representing diffusion of the molecule through the optical probe volume) overlapped, indicating that the size distribution of oligomers in solution was independent of quercetin (Fig. S3B). EGCG, derived from green tea, strongly perturbed the spontaneous fluctuations. It reduced the dark state fraction from 6.4 ± 0.2 % to 1.6 ± 0.3% at 400 nM concentration (Fig. 2). The time scale of fluctuations became slightly smaller (97 ± 45 ns) (Fig. S5D, Table-1), and the population in the dark state also went down in a dose dependent manner (Figs. S5A and S5C). However, the diffusion time (ms-µs timescale) remained similar with the concentration of EGCG 7 ACS Paragon Plus Environment

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(Fig. S5B). Two other small molecules, epicatechin and congo red also affected the inherent dynamics of the hIAPP oligomers and reduced the population of the dark state (Figs. S2 and S4). Congo red affected the dark state population in a dose dependent manner up to 2 µM concentration (Figs. S4A and S4C). Epicatechin showed a measurable effect at 80 nM but its effect remained unaltered at higher concentrations (Figs. S2A and S2D). Congo red lowered the average hydrodynamic size of the population (Fig. S4B), but epicatechin did not show any effect

Figure-2: (A): Normalized correlation function as function of delay time of 100 nM hIAPP oligomers in PBS determined by two channels cross-correlation FCS. Correlation curve (data, circles; fitted curve, solid line), (black, DA labeled hIAPP oligomers; blue, in presence of 400 nM quercetin; dark green, 400 nM EGCG). (B) The fraction of population in the dark state of DA labeled hIAPP oligomers in presence of 400 nM concentration of different small molecules (green, only hIAPP oligomers, n = 8; blue, with resveratrol, n = 3; cyan with epicatechin, n = 4; magenta, with quercetin, n = 3; yellow, with congo red, n = 3; dark yellow, with EGCG, n = 8; error bars are from fits to the averaged traces). on it (Fig. 2B). Resveratrol showed minor effects on the ns timescale dynamics of the oligomers in the concentration range tested (Figs. 2B, S1A and S1C). The fraction present in the dark state decreased, but the time scale of fluctuations remained almost similar up to a resveratrol 8 ACS Paragon Plus Environment

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concentration of 2 µM (Figs. S1C and S1D). We examined if the observed changes in the correlation function were due to direct interactions between the donor fluorophore and the small molecules. However, donor-only labeled hIAPP oligomers did not show any decay in this time domain in presence of small molecules at 2 µM concentration (Fig. S6). In addition, there was no alteration in the triplet state population as well as in the lifetime of the free donor dye in presence of the small molecules (2 µM is the maximum concentration used in our experiments) (Fig. S7). Hence, direct donor-small molecules interactions, if at all present, did not have any measurable effect on the ns time scale fluctuations observed here. Taking the percentage change of the fraction of the dark state ‘f1’ as a measure, our results allowed us to calculate the effectiveness of the different small molecules in perturbing the spontaneous dynamics of hIAPP oligomers (at small molecule concentration 400 nM at hIAPP concentration 100 nM). The ‘f1’ score yielded the following order of efficacy: resveratrol < epicatechin ~ quercetin < congo red < EGCG (Fig. 2B). 3.3. Effect of small molecules on modulating hIAPP membrane interactions. We measured the membrane affinity of Rhodamine-B labeled hIAPP oligomers in presence of the small molecules (all at 1 µM concentration) using small unilameller vesicles (SUVs). The SUVs were made of POPC, POPG and cholesterol in 1:1:1 molar ratio, and the affinity values were measured with an FCS based assay developed in-house44. All the molecules reduced the membrane affinity (Fig. 3B), though to different extents. Resveratrol, which did not perturb the inherent fluctuations significantly, reduced membrane affinity from 86 ± 1% (in absence of small molecules) to 80 ± 3%, while epicatechin, quercetin and congo red lowered it to 76 ± 2%, 71 ± 1%, and 76 ± 1% respectively. EGCG showed the strongest effect and lowered membrane affinity to 66 ± 2 % (Fig. 3B). It is evident that there is good correlation between the vesicle affinity results and the degree of perturbation of spontaneous fluctuations. We also measured the affinity of hIAPP oligomers for the cell membrane of RN46A cells. Confocal microscopy can be used to quantify the attachment of oligomers to cell membranes, as described previously45, 48. The cells were incubated with 200 nM Rhodamine-B labeled hIAPP (D) oligomers for 30 min and imaged before and after incubation, in absence and presence of each of the small molecules (each at 1 µM concentration). The change in the brightness of the cells upon incubation with the fluorescently labeled hIAPP measures the extent of its attachment 9 ACS Paragon Plus Environment

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to the cells. When the cells were incubated with only hIAPP oligomers (no small molecules present), there was a significant change in the brightness after 30 min (compare Fig. 3C, before incubation, with Fig. 3F, after incubation). But in presence of 1 µM quercetin (Fig. 3D, before incubation; Fig. 3G, after) and EGCG (Fig. 3E, before incubation; Fig. 3H, after), the extent of

Figure 3: (A) Vesicle binding affinity of hIAPP oligomers in PBS, autocorrelation curves (green, only hIAPP oligomer; magenta, hIAPP oligomer + 1µM quercetin; dark yellow, hIAPP oligomer + 1 µM EGCG). (B) vesicle binding percentage of hIAPP oligomer in presence of 1 µM concentration of different small molecules (green, only hIAPP oligomer; blue, with resveratrol; cyan, with epicatechin; magenta, with quercetin; yellow, with congo red; dark yellow, with EGCG). The data are mean ± SEM values from three different experiments. Single asterisk: p < 0.05, double: p < 0.001. The comparisons are with respect to the control (DA, donor-acceptor in absence of any small molecules). (C, D, E) confocal images of RN46A cells before incubating with Rhodamine labeled hIAPP oligomer. (F, G, H) after incubating with 200 nM hIAPP oligomers, in presence of 1 µM quercetin, and in presence of 1 µM EGCG respectively. Scale bar: 20 µm. (I) Change in fluorescence intensity due to incubation with hIAPP oligomers in presence of 1 µM small molecules; green, only hIAPP oligomers (n = 51); blue, with resveratrol (n = 23); cyan with epicatechin (n = 45); magenta, with quercetin (n = 45); yellow, with congo red (n = 54); dark yellow, with EGCG (n = 39). Asterisk: p < 0.001. The comparisons are with 10 ACS Paragon Plus Environment

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respect to the control (DA, donor-acceptor in absence of any small molecules). attachment went down considerably. We quantified the relative extent of attachment on the basis of the changes in the image brightness (Fig. 3I). The change in brightness with the donor labeled hIAPP (D, no small molecules present) has been normalized to 100%. The attachment to cell membrane goes down the most with EGCG (change in brightness 44 ± 5 % of D, Fig. 3I). Epicatechin and quercetin also show significant effects (60 ± 3 % and 65 ± 2 % of D respectively), while the effect of congo red and resveratrol is small (change in brightness: 87 ± 6 % and 80 ± 3 % of D respectively) (Figs. 3I and S8). The effects of the small molecules on cell membrane affinity are in good agreement with their effects on vesicle membrane affinity. Together the vesicle and cell binding assays correlate well with the degree of perturbation of fluctuations of hIAPP by the small molecules as depicted in Fig. 2B.

4. DISCUSSIONS The lack of a structure of a protein molecule usually implies enhanced large amplitude dynamics. While an IDP may have some residual structure which can be targeted for rational ligand design, a completely orthogonal approach would be to harness the dynamics for this purpose. While the dynamical parameters may not directly suggest a specific design for an appropriate ligand, they can potentially serve as a direct and sensitive assay for evaluating a series of candidate ligands, which can provide an iterative approach for drug design. Our results here test this hypothesis using hIAPP as a model IDP. We have tested the effect of a group of small molecules on hIAPP dynamics, namely resveratrol, EGCG, epicatechin, quercetin, and congo red. These are already known to affect the fibrillization kinetics of many amyloid proteins and peptides (including hIAPP)22, 49, 50, but there is little evidence of their interaction with small hIAPP oligomers. EGCG is known to protect cells from hIAPP toxicity21. Quercetin has been shown to be effective in slowing aggregation kinetics of a hIAPP fragment (hIAPP 8-37) and in protecting RINm5F cells from hIAPP toxicity51. Aitken et al. has shown that in presence of 20-fold molar excess of congo red compared to hIAPP, enhancement of ThT fluorescence (a reporter of aggregation) gets completely inhibited, and co-incubation of 28 µM hIAPP with 3.4 fold molar excess of congo red reduces cytotoxicity significantly52. Epicatechin, a derivative of EGCG, and resveratrol, a 11 ACS Paragon Plus Environment

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polyphenolic stilbene derivative mostly found in grapes and red wine, are also known to delay the lag time of hIAPP aggregation. However, they are not as effective as EGCG in terms of retarding the aggregation kinetics or in reducing the amount of amyloid formation. The effect of epicatechin is similar to that of quercetin, but weaker than that of EGCG53, 54. However, these data are from different studies, and from the available data, it is difficult to quantitatively compare their efficacy to interact with hIAPP. This is what our experimental approach achieves. In our experiments, the decay of the correlation function of the donor-acceptor (DA) labeled hIAPP oligomers in the ns time domain, compared to the donor-only (D) labeled hIAPP oligomers, manifests a stochastic quenching process which is likely due to intra-molecular diffusion (Fig. 1B). This time scale varies linearly with the viscosity of the medium (Fig. 1C), which is consistent with such a diffusive process. In addition, this time scale is independent of the probe volume, which is also consistent with our explanation (Fig. 1D). We note that the D oligomers (i.e. without acceptors) also show some decay in the ns domain, though the fraction in the dark state is smaller. It is likely that this is due to collisional quenching by some part of the peptide, or due to a change in the local environment surrounding the donor, caused by spontaneous fluctuations in the oligomers. The photo-physics of the donor is unlikely to contribute to this, since the free dye shows a flat correlation trace in the ns domain (Figs. 1A and 1B). Together, our data suggests that the decay observed in the ns time domain for DA labeled oligomers reports spontaneous intra-molecular fluctuations. Our results show that all the small molecules except for resveratrol perturb the inherent fluctuation of DA labeled hIAPP oligomers in a dose dependent manner. EGCG strongly interacts with the small oligomers of hIAPP (size: 1.2-1.5 nm) and perturbs their dynamics significantly, even at 80 nM concentration (Figs. S5A and S5C). Quercetin also interacts with the small oligomers, but the effect is smaller than that of EGCG. Congo red has the second highest effect among the tested molecules. Since we have probed the dynamics in presence of all the molecules under identical conditions, we are able to order them in terms of their ability to perturb the inherent dynamics. This order is: resveratrol < epicatechin ~ quercetin < congo red < EGCG. There is considerable agreement of the known effects of the small molecules on the aggregation kinetics and / or toxicity with our interaction measurements, with notable exceptions (e.g. resveratrol). Resveratrol does not show any significant effect on the inherent dynamics of oligomers up to 2 µM concentration. Both the time scale of fluctuations and the dark state 12 ACS Paragon Plus Environment

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fraction of the dynamic population remain similar (Fig. S1). Most likely, interactions observed by other techniques only happen at higher concentrations or with larger aggregates, or the effect of such interaction is not global enough to perturb the inter-terminal distance. Our results identify the molecules which are able to directly interact with the small oligomers of hIAPP and change their global dynamics. Such information may be useful for specific drug design strategies. Some of the small molecules also seem to have an effect on the process of aggregation, as shown by our results (Figs. S1-S5). The correlation functions for different concentrations of epicatechin and quercetin overlap in the ms-µs region, which reports the average hydrodynamic size of the peptide. This indicates that they do not change the size of the oligomers. On the other hand, the decay of the correlation function in the ms-µs time domain becomes faster in presence of congo red. Also, the average fluorescence count, which is a measure of the amount of peptide present in the solution, decreases with an increase of concentration of congo red. This is possibly due to the formation of larger aggregates (which precipitate) in the presence of this molecule. Our results show that the order of efficacy of small molecules in perturbing the dynamics of hIAPP oligomers can be measured quantitatively. However, a priori it is not clear whether the effect on dynamics has any functional significance. We tested the correlation of the dynamics data with the affinity of hIAPP oligomers to bilayer membranes. We note that such affinity likely has a bearing on the toxicity of the peptide42. We first chose small unilamellar vesicles (SUVs) as a mimic of the cell membrane, since it is difficult to measure cell membrane binding under welldefined control conditions. We have separately observed that hIAPP has very high membrane affinity in its oligomeric state, while it is much lower in the monomeric state46. This establishes that the property of the peptide is not dictated by the dye. Our results here reveal that in presence of all the molecules, the affinity of small hIAPP oligomers to SUVs decreases (Figs. 3A and 3B). Significantly, the order of efficacy is reasonably well correlated with that constructed on the basis of their effects on the ns time scale dynamics. To test the relevance of these measurements for a real biological system, we then measured the relative affinity of hIAPP to RN46A cell membranes (Figs. 3C-I). We performed confocal imaging with Rhodamine-B labeled peptide for this estimation. The extent of the attachment of the peptide was measured by the brightness of the confocal images of the cells. The brightness gets reduced in presence of all the small 13 ACS Paragon Plus Environment

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molecules (Figs. 3C-I and S8), showing that the small molecules are effective (to different extents) in reducing the interaction of the hIAPP oligomer with live cells. Interestingly, the cell binding assay shows a similar order of efficacy as that of vesicle binding, and both agree quite well with the fluctuation data.

5. CONCLUSIONS Spontaneous fluctuations of intrinsically disordered proteins (IDPs) can be measured using nsscale FCS, and can be employed to probe transient interactions with small molecules. This measure of interaction agrees well with the effect of these small molecules on vesicle binding as well as on cell membrane attachment of IAPP. We conclude that our method is an effective in vitro assay which can provide a direction in the chemical space for developing candidate drug molecules for intrinsically disordered proteins.

ASSOCIATED CONTENT Supporting Information Materials, Peptide synthesis, FRET dye labeling and purification, Instrumental set up, Experimental methods, Dose dependence of the effect of small molecules on the dynamics and the hydrodynamic size of DA-hIAPP oligomers, Effect of small molecules on D-hIAPP oligomers, Effect of small molecules on the triplet state of free Rhodamine-B, Cell attachment studies in presence of small molecules

AUTHOR INFORMATION Corresponding Author *email: [email protected] ORCID: 0000-0002-6540-7472

Present Address A.R., University of Southern California, Los Angeles 90033, California

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The authors declare no competing financial interest.

ACKNOWLEDGEMENTS The work was made possible by institutional funding from Tata Institute of Fundamental Research, Mumbai, 400005, India. We thank Prof. R. V. Hosur and Prof. Samir Maji for their kind donation of a few of the small molecules.

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Spontaneous Fluctuations can Guide Drug Design Strategies for Structurally Disordered Proteins Barun Kumar Maity#, Vicky Vishvakarma#, Dayana Surendran#, Anoop Rawat#, Anirban Das#, Shreya Pramanik$, Najmul Arfin†, Sudipta Maiti#* #Department of Chemical Sciences, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400005, India $

UM-DAE Centre for Excellence in Basic Sciences, Univ. of Mumbai, Kalina, Mumbai 400098,

India †

Center for Interdisciplinary Research in Basic Sciences, Jamia Milia Islamia, New Delhi,

110025, India.

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