Molecular Mechanisms of Alzheimer's Biomarker ... - ACS Publications

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The Journal of Physical Chemistry

Molecular Mechanisms of Alzheimer’s Biomarker FDDNP Binding to Aβ Amyloid Fibril Niyati D. Parikh and Dmitri K. Klimov∗ School of Systems Biology, George Mason University, Manassas, VA 20110 E-mail: [email protected]

∗

To whom correspondence should be addressed

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Abstract Using isobaric-isothermal replica exchange molecular dynamics and all-atom explicit water model we examined binding of FDDNP biomarkers to Aβ amyloid fibril fragment. Our results can be summarized as follows. First, FDDNP ligands bind with high affinity to Aβ fibril and hydrophobic effect together with π-stacking interactions are the dominant factors governing FDDNP binding. In comparison, electrostatic interactions and hydrogen bonding play minor role. Second, our simulations reveal strong tendency of bound FDDNP molecules for self-aggregation. Accordingly, about twothird of all bound ligands form aggregated clusters of various sizes and ligand-ligand interactions make considerable contribution to FDDNP binding. Third, FDDNP ligands bind to two distinct sites on Aβ fibril. Primary binding sites (NT) are located at the N-terminals of Aβ10-40 peptides, whereas secondary ones (CE) occur on the concave fibril edge near fibril channels. The NT sites are characterized by strong hydrophobic and π-stacking interactions, favorable binding entropy resulting from multiple FDDNP binding orientations and propensity for self-aggregation, but relatively weak van-der-Waals interactions. In contrast, the CE sites offer stronger van-der-Waals binding interactions, but weaker hydrophobic and aromatic interactions and less favorable binding entropy. By comparing our data with previous studies we suggest that the primary binding locations identified by us are likely to occur in other Aβ fibril polymorphic structures. We also show that FDDNP binds via distinct mechanisms to Aβ fibrils and monomers. We argue that FDDNP binds with stronger affinity to benign Aβ monomers than to the fibrils raising questions about FDDNP ability to selectively label amyloid deposits.

Keywords: ligand binding, amyloid imaging, Alzheimer’s disease, replica exchange, molecular dynamics.


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Introduction Alzheimer’s disease (AD) is an age-related, degenerative disorder causing losses in mental functions. One of AD characteristics is an appearance in brain tissues of insoluble extracellular aggregated deposits of Aβ peptides, which themselves are the products of normal cellular proteolysis. 1,2 Although Aβ peptides have various alloforms, the most abundant are 40residue species, Aβ1-40. According to amyloid cascade hypothesis Aβ aggregation proceeds through a complex sequence of structural transitions, which starts with the oligomerization of monomeric Aβ species and terminates with the formation of amyloid fibrils. 3 Recently, the molecular structures of amyloid fibrils have been resolved, which showed a remarkable degree of polymorphism. 4–6 Independent of specific polymorphic form and similar to other amyloids, Aβ fibrils are built upon extensive cross β-structure stabilized by a network of backbone hydrogen bonds (HBs). Importantly, recent experimental evidence suggested that Aβ cytotoxicity is mainly determined not by the fibrils, but by Aβ oligomers. 7 However, even if Aβ fibrils are not the primary cytotoxic species, they still represents the reservoir of Aβ monomers or oligomers and are the hallmark structures manifesting AD pathology. 8,9 One of the main challenges in antemortem AD diagnostics is the lack of established molecular biomarker capable of imaging the formation of Aβ amyloid deposits. 10 Potential molecular probe addressing this challenge is a radiofluorinated compound FDDNP, which demonstrated in vivo and in vitro binding to Aβ fibrils. 11–13 Consecutive biophysical and structural studies have indicated that FDDNP can also label prion plaques, 14 the fibrils formed by tau protein and its fragments, 15,16 and cerebral amyloid angiopathy deposits, 17 thus suggesting its broader utility as amyloid fibril biomarker. In this capacity, FDDNP ligands used with positron emission tomography (PET) can serve as a non-invasive detection tool visualizing fibril deposits in living brain. 18,19 Practical use of FDDNP for amyloid imaging depends on thorough understanding of its binding mechanism and assessment of its selectivity with respect to different amyloidogenic proteins and their structural species. 20,21 In general, experiments have indicated that FDDNP 3 ACS Paragon Plus Environment


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binds to Aβ fibrils mainly via hydrophobic interactions utilizing several binding sites. 12 However, the exact number of binding sites and evaluation of their binding affinities remain highly controversial. 12,21,22 Furthermore, the locations and amino acid compositions of the binding sites on Aβ fibrils are unknown. Even more important question pertains to the selectivity of FDDNP, which ideally should distinguish Aβ fibril species from benign monomers. Additionally, as an imaging agent FDDNP should not interfere with Aβ aggregation and fibril elongation, although some experimental studies indirectly support such possibility. 13 One may expect that the knowledge of atomistic binding mechanisms of FDDNP may benefit the rational development of its derivatives with improved imaging properties and selectivity. In recent years, molecular dynamics (MD) simulations performed at all-atom resolution and with explicitly treated water have proved to be a valuable tool for studying the mechanisms of aggregation 23–29 and ligand binding. 30–36 In our previous study, we have used replica exchange molecular dynamics (REMD) simulations to probe binding of FDDNP ligands to amino-truncated Aβ10-40 monomers. 37 We showed that at two concentrations FDDNP binds with high affinity to the two sites in Aβ10-40 monomer located near the central hydrophobic cluster and at the peptide C-terminal. In line with the experiments on FDDNP binding to Aβ fibrils, hydrophobic effect appears to be the dominant factor controlling ligand binding to Aβ monomer. We also found that ligand self-aggregation provides significant contribution by enhancing FDDNP binding affinity. To complement experimental studies and extend our previous molecular simulations beyond Aβ monomers, we performed in this paper all-atom explicit water REMD simulations examining binding of FDDNP biomarker to Aβ fibrils resolved from solid-state NMR experiments 4 (Fig. 1). We show that FDDNP ligands bind with high affinity to Aβ fibrils utilizing two distinct binding sites located on the fibril lateral surfaces and on its edge. Consistent with previous experiments our structural analysis points to significant “smear” in FDDNP binding positions. The analysis of binding energetics suggests that hydrophobic effect and π-stacking interactions are the main driving factors in binding. Because our simulation sys-


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tems contained multiple FDDNP ligands, we observed strong propensity of these ligands for self-aggregation at fibril binding sites that provided significant contribution to FDDNP binding affinity. Comparing with our previous simulations probing FDDNP binding to Aβ monomers, we argue that this ligand can label different Aβ species, from benign monomers to amyloid fibrils.

Methods Molecular model and simulations: Aβ fibril and FDDNP ligands (Fig. 1) were modeled using all-atom explicit water CHARMM22 force field with CMAP corrections. 38 Parameterization of FDDNP (Fig. 1b) was performed using CHARMM General Force Field as reported previously. 37 In all, the simulation system included six amino-truncated Aβ10 − 40 peptides (P1-P6) forming a two-fold symmetry hexameric fibril fragment and six FDDNP ligands (Fig. 1a). Selection of hexameric fibril fragment is computationally efficient and facilitates the comparisons with monomeric systems (see Discussion). Heavy atoms in peptide backbones were constrained to their coordinates determined from the solid-state NMR measurements. 4 The constraints were implemented through soft harmonic potentials with the spring constant kc = 0.6kcal/(mol˚ A2 ). 25 Selected value of kc permits backbone fluctuations with the amplitude of about 1.3 ˚ A at 330K, which are comparable with the fluctuations of atoms on the surface of folded proteins. 39 The constraints were not applied to amino acid side chains. Constraints mimic high structural stability of amyloid fibrils 40 and prevent fibril disintegration at high REMD temperatures (see below). Our selection of amino-truncated Aβ10-40 peptide is motivated by three considerations. First, in two-fold symmetry Aβ1-40 amyloid structure the N-terminal is unstructured and unresolved. 4 Second, Aβ1-40 and Aβ10-40 peptides form similar two-fold symmetry fibril structures. 4,41 Finally, by selecting the same Aβ10-40 peptides used in our previous study 37 we can directly compare FDDNP binding to Aβ monomers and fibrils.



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The simulation system has the initial dimensions of 97.93˚ Ax78.34˚ Ax48.96˚ A and contained 9130 TIP3P water molecules and six sodium ions (Fig. 1a). The total number of atoms was 30,438. It follows then that the ligand concentration was 27 mM and the nominal ligand:Aβ stoichiometric ratio was 1:1. The molecular dynamics simulations were performed using NAMD program 42 and periodic boundary conditions. We used isobaric-isothermal (NPT) ensemble, in which temperature was controlled by underdamped Langevin bath with the damping coefficient γ = 5ps−1 . Pressure was controlled by the Nose-Hoover Langevin piston method with the piston period of 200 f s and decay of 100 f s. The integration step was 1 f s. Electrostatic interactions were computed using Ewald summation, whereas van der Waals interactions were smoothly switched off in the interval from 8 to 12 ˚ A. Covalent bonds were constrained by the SHAKE algorithm. Replica exchange protocol:

To accelerate conformational sampling we used isobaric-

isothermal (NPT) replica exchange molecular dynamics (REMD). Its formalism is described in detail elsewhere. 43,44 R = 42 replicas were distributed exponentially in the temperature range from 330 to 450K. Pressure in all replicas was maintained at the constant value of P = 1atm. Exchanges were attempted every 2 ps between all neighboring replicas along temperature scale. Four independent REMD trajectories were generated resulting in the cumulative simulation time of 3.36 µs or 80 ns per replica. The average acceptance rate of ≈18% was obtained. The initial non-equilibrated parts in REMD trajectories were discarded from thermodynamics analysis reducing the cumulative sampling to ≈ 3.3µs. Each REMD trajectory started with random initial distributions of unbound ligands. Analysis of REMD convergence is presented in the Supporting Information. Computation of structural probes:

Molecular interactions were probed by defining

contacts between Aβ fibril and ligands and between ligands. A contact between amino acid and ligand is formed, if the distance between the centers of mass of amino acid side chain and one of FDDNP structural groups (G1, G2, or G3 in Fig. 1b) is less than 6.5 ˚ A. This cut-off distance approximately corresponds to the onset of hydration of side chains. Similarly, a 6 ACS Paragon Plus Environment

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ligand-ligand contact occurs, if the distance between the centers of mass of any FDDNP structural groups from two ligands is less than 6.5 ˚ A. A ligand is directly bound to the fibril if it makes at least one contact with amino acids. A ligand resides in extended bound shell, i.e., it is indirectly bound to the fibril, if it is included in the cluster of self-aggregated ligands, of which at least one is directly bound to Aβ fibril. Hydrogen bond (HB) between Aβ donor (D) and FDDNP acceptor (A) atoms occurs, if the distance rDA ≤ 3.5˚ A and ∠DHA ≥ 120◦ . To detect Aβ-ligand HBs we treated FDDNP nitrogen and fluorine atoms as acceptors. π-stacking interactions between aromatic side chains and FDDNP ligands were probed as follows. For two aromatic rings we defined the normalized normal vectors n~1 and n~2 and computed the angle between the rings γ from the scalar product n~1 n~2 = cosγ. Then, we obtained the probability distribution P (γ) of γ conformational states observed in simulations and created by two aromatic rings forming γ angles. The challenge is to extract information from P (γ) about the values of γ corresponding to energy minimized ring conformations. To interpret P (γ) we aligned the direction of n~1 with the z-axis and using spherical coordinates generated a uniform random distribution of n~2 orientations resulting in a reference random Pr (γ). Because random distribution Pr (γ) represents the case when there are no interactions between the rings, a difference between P (γ) and Pr (γ) implicates π-stacking interactions. If observed P (γ) deviates from Pr (γ), we checked if the preference of γ to adopt specific values corresponding to energy minimized conformations can reproduce observed P (γ). For example, if P (γ) reveals two peaks, we attempted to recreate them with Pg (γ) generated by assuming that the angles γ follows the superposition of two Gaussian distributions. To determine the mean and standard deviation of Gaussian distributions we minimized the root-mean square deviation (RMSD) between observed P (γ) and generated Pg (γ). To obtain solvent accessible surface area (ASA) we used VMD program and took into account all atoms including hydrogens, while setting the probe radius to 1.4˚ A. 45 Following our earlier study 35 hydrophobic accessible surface area (hASA) of amino acid or ligand is defined as a sum of ASA values for all apolar atoms. Relative hASA of amino acid X is



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obtained by dividing the hASA of amino acid X by its ASA in the reference triplet state Gly-X-Gly. 46 Polar accessible surface area (pASA) of amino acid or ligand is the sum of ASA values for all polar atoms. Its relative value for amino acid is defined in the same way as relative hASA. The energetics of ligand-fibril and ligand-ligand interactions was probed using namdenergy module in VMD. Thermodynamic averages of structural quantities (denoted as < .. > unless otherwise stated) and free energy landscapes were computed using multiple histogram method. 47 To calculate binding free energy ∆Fb from the dependence of free energy F (rb ) on the ligand-fibril distance rb , we used the method of Pomes and coworkers. 48 The data in the paper are reported at 330K. Mapping binding sites:

To characterize FDDNP binding to the fibril we considered

two clustering algorithms. The first was designed in-house to map the binding sites on Aβ fibril. To this end, we considered the average number of contacts formed by amino acid i in the peptide m with the ligands, < Cl (i; m) >, and the probability Pcb (i, j; m, n) for amino acids i and j from the peptides m and n to be cross-bridged by a bound ligand. In general, a binding site is comprised of amino acids i and j that satisfy two criteria: (i) < Cl (k; m) >≥ 0.25Cl,max , where k = i, j and Cl,max is the maximum value of < Cl (i; m) >, and (ii) Pcb (i, j; m, n) ≥ 0.25Pcb,max , where Pcb,max is the maximum value of Pcb (i, j; m, n). Operationally, to identify a binding site we first selected an amino acid i satisfying the criterion (i) and then keep adding amino acids j satisfying both criteria until all are exhausted. If criterion (i) applies only to i and/or criterion (ii) is not satisfied, then a binding site incorporates single amino acid i. When a binding site consists of more than one amino acid, all its amino acids are linked through cross-bridging ligands. Thus, our clustering procedure can locate several independent binding sites on the surface of Aβ fibril. To complement the first clustering algorithm we used standard Daura et al. algorithm implemented in VMD to cluster FDDNP ligands. 49 To this end, bound FDDNP ligands were clustered by computing the RMSD between their heavy atoms. Appropriate RMSD cut-off Rc = 11˚ A was selected by requiring the ligand clustering algorithm to produce four top 8 ACS Paragon Plus Environment

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clusters bound to four top affinity binding sites with the same ranking identified by the first algorithm. Combination of two clustering algorithms is optimal, because the first is efficient in identifying binding sites, but it cannot describe well ligand positions, while the second easily maps ligand positions, but is inefficient for grouping amino acids into binding sites.

Results FDDNP binding to Aβ fibril We begin our analysis of FDDNP interactions with Aβ fibril by examining binding probabilities. Using REMD and the procedure described in Methods we have computed the average probability Pb for FDDNP molecule to bind to Aβ fibril (Fig. 1a). We found Pb to be 0.69 ± 0.03 at 330K implying that out of six ligands < L >= 4.1 ± 0.2 are bound to the fibril. If binding is restricted to hydrophobic and aromatic amino acids, then binding probability Pb is marginally reduced to 0.61 ± 0.04. If binding is restricted to exclusively aromatic amino acids, then Pb is further reduced to 0.43 ± 0.03. Because approximately half of Aβ10-40 amino acids are hydrophobic and only five out of 31 are aromatic, these results suggest a preference for binding to hydrophobic and aromatic amino acids. Next, we evaluated the binding affinities of FDDNP structural groups G1-G3 (Fig. 1b). The average binding probabilities Pb,G1 , Pb,G2 , and Pb,G3 are equal to 0.55 ± 0.03, 0.50 ± 0.03, and 0.48 ± 0.03, respectively. Therefore, all FDDNP groups have similar binding affinities. On an average, upon binding a FDDNP ligand forms contacts with < Ca >= 2.4 ± 0.2 amino acids indicating that a single biomarker molecule bound to Aβ fibril typically interacts with multiple amino acids. In addition, we have computed the average number of hydrogen bonds (HBs) < Nhb > between a FDDNP molecule and the fibril (see Methods). The results show that HBs make minor contribution to binding (< Nhb >≈ 0.3 ± 0.0). Given high hydrophobicity of FDDNP molecules (apolar atoms represent about 73% of the solvent accessible surface area 37 ), the ligands may self-aggregate upon binding forming 9 ACS Paragon Plus Environment


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molecular clusters. To probe this possibility, we have analyzed the formation of bound FDDNP clusters composed of Sc ligands (1 ≤ Sc ≤ 6). (A ligand cluster is bound, if it includes at least one bound ligand. ) The distribution of the average number of ligands < L(Sc ) > participating in the bound clusters of the size Sc is shown in Fig. 2. The total number of FDDNP molecules in the bound clusters determined from Fig. 2 is < Lc >= 4.7 ± 0.1. This number is slightly larger than the number of bound ligands < L >= 4.1 reported above. The discrepancy reflects an existence of few ligands in the extended bound shells (< Lc > − < L >≈ 0.6), which are included in the bound clusters, but do not form direct interactions with Aβ fibril. With the account of all ligands forming bound clusters, the binding probability Pb increases to 0.78 ± 0.02. Broad distribution of the number of ligands forming clusters in Fig. 2 underscores high propensity of FDDNP molecules for self-aggregation. Indeed, the number of bound ligands < L(1) >, which do not interact with other ligands and form the clusters of size Sc = 1, is fairly small (≈ 1.8). This result implies that about two-thirds of all ligands aggregate upon binding to Aβ fibril. Consequently, when using the data in Fig. 2 and calculating the average size of bound FDDNP cluster < Sc >, we find < Sc >=1.7, i.e., a typical cluster includes more than one ligand. These results are consistent with the average number of ligand-ligand contacts formed by a bound ligand, < Cll >≈ 1.0 ± 0.1. It is worth noting that Fig. 2 reveals a monotonic decrease in the population of clusters with the increase in Sc suggesting that even though ligand self-aggregation is favorable, formation of large clusters is suppressed. In addition, Fig. 2 presents the distribution of the number of ligands forming unbound clusters. On an average, there are few unbound ligands (< Lu >= 1.3), of which slightly less than half (≈ 0.6) are aggregated. Therefore, although unbound FDDNP ligands also reveal self-aggregation propensity, it becomes enhanced upon binding to the fibril as the fraction of aggregated ligands increases from 0.46 to two-third.


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FDDNP binding interactions To analyze binding energetics we have computed van-der-Waals and electrostatic interaction energies between FDDNP ligands and Aβ fibril. Table 1 demonstrates that consistent with el > make a small contribulow polarity of FDDNP molecule electrostatic interactions < Elp

tion to ligand-fibril energetics (about 20%). If bound FDDNP ligands tend to self-aggregate, binding energetics must be affected by ligand-ligand interactions. This expectation is confirmed by the computations of ligand-ligand van-der-Waals < Ellvdw > and electrostatic < Ellel > energies listed in Table 1. It follows that electrostatic interactions also play minor role in ligand-ligand interactions (≈ 10%). More importantly, comparison of ligand-fibril and ligand-ligand interaction suggests that the latter contributes about 30% of total binding energy. To complement the analysis of binding energetics we have examined the changes in accessible surface area (ASA) of a ligand upon binding. In a free (unbound and unaggregated) ˚2 . Due to binding to the fibril its ASA is reduced by state FDDNP has the ASA of 523A 162 ± 4˚ A2 or 31%. The loss of ligand ASA attributed to ligand self-aggregation upon binding is 110 ± 8˚ A2 or 21%. Thus, in total, binding of FDDNP to the fibril reduces the ligand ASA by 272±12˚ A2 that constitutes more than 50% of the ASA in unbound state. Consistent with the computations of binding energetics, the loss in ASA related to ligand-ligand interactions represents about 40% of total binding-induced loss in ASA. One can also decompose the loss in ASA with respect to FDDNP structural groups (Fig. 1b). In free state the ASA of the groups G1, G2, and G3 are 159, 168, and 196 ˚ A2 . Upon binding to the fibril, these ASA are reduced by 53±1, 51±2, and 58±1 ˚ A2 that represents the respective losses of 33, 30, and 36% of free state ASA. These findings support our conclusion that all FDDNP groups participate about equally in binding to the fibril. Our results suggest that the contributions of electrostatic interactions and hydrogen bonds to binding are minor. Yet, the limitation of the prior analysis is that it does not consider hydrophobic effect and does not distinguish π-stacking interactions from van-der11 ACS Paragon Plus Environment


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Waals energies. To address these issues we determined the amino acids demonstrating high binding affinity with respect to FDDNP. As a relevant measure we used the average number of contacts formed by amino acid i in the peptide m with the ligands, < Cl (i; m) >. Fig. 3a indicates striking variations in < Cl (i; m) > among fibril amino acids, with most contributing little to FDDNP binding (< Cl (i; m) >≈ 0), whereas few showing high < Cl (i; m) > values. The list of 16 top affinity amino acids (< Cl (i; m) >> 0.2) is given Table 2. It is seen that out of 16 amino acids nine are aromatic, eight are hydrophobic, and none are polar or charged. To further emphasize dramatic differences in amino acid binding affinities with respect to FDDNP, we plot in Fig. 3b < Cl (i) >, the number of ligand contacts formed by amino acids i averaged over six peptides in Aβ fibril fragment. The largest < Cl (i) > (≥ 0.1) are observed for Tyr10 (0.46), Val12 (0.35), His14 (0.17), Ile32 (0.14), Phe19 (0.11), and Leu34 (0.10), of which four amino acids are hydrophobic and three are aromatic. For comparison, the values of < Cl (i) > for five charged Aβ amino acids are much smaller, 0.06 (Lys16), 0.04 (Lys28 and Asp23), 0.02 (Glue22), and 0.01 (Glu11). In general, the average fibril ASA lost due to ligand binding is 552 ± 37˚ A2 , of which 416 ± 36˚ A2 (or 75%) is attributed to the loss of hydrophobic ASA and only 135 ± 36˚ A2 (or 25%) is due to the loss in polar ASA. By dividing Aβ amino acids into hydrophobic (Val,Ala,Leu,Ile,Met), aromatic (Tyr,His,Phe), polar (Asn,Gly,Ser,Gln,Asn), and charged (Lys, Glu, Asp) categories, we can evaluate their contribution to the changes in fibril ASA caused by ligand binding. We found that, on an average, the losses of ASA per amino acid are 48 ˚ A2 for aromatic residues, 16 ˚ A2 for hydrophobic residues, 7 ˚ A2 for charged residues, and 6 ˚ A2 for polar residues. These results together with Fig. 3 and Table 2 strongly indicate that FDDNP mainly binds to aromatic and hydrophobic amino acids. We have established that among all amino acids FDDNP ligands bind with highest affinity to Tyr10. To assess the contribution of π-stacking interactions, we have computed the probability distribution P (γ) of the conformational states, in which the normals to the bound FDDNP G1 group and Tyr10 aromatic ring form an angle γ. To properly interpret


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this distribution presented in Fig. 4 we have also computed P (γ) for the case, when G1 group of FDDNP does not form a contact with Tyr10 aromatic ring. Fig. 4 shows that the two distributions are clearly distinct suggesting that the orientation of the bound FDDNP with respect to Tyr10 aromatic ring is not random. In fact, the two peaks in the bound P (γ) distribution are indicative of preferential orientation of FDDNP G1 with respect to Tyr10 side chain. Similar bimodal distributions P (γ) were obtained for other aromatic amino acids with high binding affinity (His14 and Phe14, data not shown). In the Discussion we interpret this observation as an evidence of π-stacking interactions. Therefore, hydrophobic effect and π-stacking interactions appear as dominant factors governing FDDNP binding. Finally, REMD simulations afford direct evaluation of the free energy of FDDNP binding to Aβ fibril. To this end, we computed the free energy of FDDNP molecule F (rb ) as a function of the distance to the fibril surface, rb . The free energy F (rb ) in Fig. 5 shows a well defined minimum of Fmin ≈ −2.9RT at rb,min ∼ 5˚ A reflecting binding to the fibril. We used the approach developed by Pomes and coworkers 48 to estimate the binding free energy ∆Fb = FB −FU . To this end, we divided the rb axis into the bound state B (3.5˚ A ≤ rb ≤ rb,0 = 14.5˚ A) and unbound state U (rb,0 < rb ≤ ru,0 = 25.5˚ A), computed partial partition functions by integrating over these states, and obtained corresponding free energies FB and FU . The described procedure evaluates the free energy of binding to Aβ fibril as ∆Fb = −1.5±0.2RT , which is consistent with the corrected probability of binding Pb = 0.78. It is important to note that ∆FB takes into account all energetic and entropic contributions to binding as well as ligand self-aggregation.

FDDNP binding sites on Aβ fibril To identify specific FDDNP binding sites on Aβ fibril, we have used the procedure described in the Methods. Specifically, by considering the average number of contacts formed by amino acid i in the peptide m with the ligands, < Cl (i; m) >, and the probability Pcb (i, j; m, n) for amino acids i and j in the peptides m and n to be cross-bridged by binding ligand, we have 13 ACS Paragon Plus Environment


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identified four distinct binding sites, BS1-BS4 (Fig. 6a). BS1 is located at the N-terminals of the peptides P2, P4, and P6 (Fig. 1a) and consists of eight amino acids, including Tyr10 and Val12 from P2, P4, and P6 and His14 from P2 and P4. The probability for FDDNP ligand to bind to BS1 is Pb (BS1) = 0.20 ± 0.02. BS2 is related to BS1 by the two-fold fibril symmetry and is located on the opposite side of Aβ fibril at the N-terminals of the peptides P1, P3, and P5. BS2 have the composition almost identical to BS1 consisting of seven amino acids - Tyr10 and Val12 from P1, P3, and P5 and His14 from peptide P3. The probability for a ligand to bind to BS2 is Pb (BS2) = 0.15 ± 0.02. The last two binding sites, BS3 and BS4, are located on the concave edge of the fibril (Figs. 1a and 6a). BS3 is composed of only two amino acids (Phe19 and Ile32 from P5), for which Pb (BS3) = 0.05 ± 0.02. BS4 is related to BS3 by the two-fold fibril symmetry and incorporates three amino acids, Phe19, Ile32, and Leu34 from P6. Its Pb (BS4) = 0.05 ± 0.01. Clustering procedure also identifies the fifth, marginal binding location, BS5, located on the convex fibril edge and composed of a single amino acid Leu34. However, because its binding probability is low (Pb (BS5) < 0.03) and it involves only one amino acid, we exclude this binding site from further analysis. Because of two-fold fibril symmetry the binding probabilities and compositions of BS1 and BS2 and of BS3 and BS4 are similar. (Some variations in their Pb and compositions reflect minor differences in the conformations of fibril peptides. ) Consequently, we combine BS1 and BS2 together and refer to them as N-terminal (NT) fibril binding sites (Fig. 6b). The probability for FDDNP ligand to bind to the NT sites, Pb (N T ), is 0.35±0.03. Similarly, BS3 and BS4 are combined to form concave edge (CE) fibril binding sites (Fig. 6c). The probability for a ligand to bind to these sites is Pb (CE) = 0.10 ± 0.02. Taking into account their binding probabilities we refer to the NT and CE as primary and secondary binding sites. Together, the four binding sites capture approximately 65% of all fibril binding ligands. If we take into account ligand self-aggregation and formation of bound clusters, the probabilities for FDDNP molecules to bind (directly or indirectly) to the NT and CE binding sites increase to 0.42 and 0.12, respectively. With this correction, the NT and CE binding sites capture


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almost 70% of FDDNP ligands binding to Aβ fibril. Using the procedure described in the Methods we obtained FDDNP ligand clusters matching the NT (BS1, BS2) and CE (BS3, BS4) binding sites. Fig. 6b,c shows the centroid structures of FDDNP ligands bound to these locations. To characterize the position of FDDNP ligand, we computed the angle κ between the fibril axis and the FDDNP axis defined as the vector connecting G2 and G3 centers of mass (Fig. 1). In Fig. 6b κ for the FDDNP centroids is 17◦ for BS1 and 82◦ for BS2 suggesting that the orientations of FDDNP at the NT binding sites are “smeared”. To substantiate this conclusion, Fig. 7 presents the probability distribution P (κ) of κ conformational states formed by the ligands bound to the NT bindings sites. For comparison, the same figure shows the distribution Pr (κ) generated assuming truly random orientations of ligands with respect to fibril axis. Given close similarities between P (κ) and Pr (κ) (the RMSD between them is 0.005) we conclude that FDDNP molecules adopt nearly random orientations upon binding to the NT sites, which is indicative of significant “smear” in the bound positions of FDDNP. The “smear” of ligand positions at the binding sites can be directly evaluated using the following procedure. We identified the ligands bound to BS1 (see Methods), computed their centers of mass R~cm , and calculated standard deviation in their positions δRcm =< (R~cm − < R~cm >)2 >1/2 , where < .. > implies averaging over the cluster. Then the value of δRcm averaged over BS1 and BS2 characterizes the “smear” of FDDNP positions when bound to the NT sites. We found that δRcm (N T ) = 5.9 ± 0.1˚ A. Same computations for the CE sites give smaller value δRcm (CE) = 4.9 ± 0.2˚ A. Similar results are obtained if we extend δRcm computations to the ligands participating in the clusters bound to the NT or CE locations. These calculations demonstrate that the ligands bound to the CE sites exist in more conformationally restricted states. In contrast, the ligands bound to the NT sites are relatively “smeared” on the fibril surface without well-defined orientation. To get further insight into the properties of binding sites, we analyzed their energetics. Table 3 presents the binding energies for the NT and CE sites decomposed into van-der-



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Waals and electrostatic contributions as well as ligand-fibril and ligand-ligand interactions. Three observations follow from this table. First, in all cases electrostatic binding interactions are much weaker than van-der-Waals terms. This result is consistent with the analysis of binding energetics for Aβ fibril above. Second, the total binding energy for the CE site, < Elp (CE) > + < Ell (CE) >= −24.5 ± 1.7kcal/mol, is lower than that for the NT sites, < Elp (N T ) > + < Ell (N T ) >= −20.8 ± 1.1kcal/mol. This result is unexpected, because from the corresponding binding probabilities, Pb (N T ) and Pb (CE), we deduce that the NT has a higher binding affinity than CE. Third, the contribution of ligand-ligand interactions to the total binding energy for the NT site is larger than for the CE site (−8.8kcal/mol or 42% vs −6.7kcal/mol or 27%). Thus, our analysis suggests that the binding energetics for the NT and CE binding sites is quite different. These observations are rationalized in the Discussion.

Discussion Mechanism of FDDNP binding to Aβ fibril In this study we have used all-atom REMD simulations to probe binding of FDDNP biomarkers to the fragments of Aβ fibril. It is useful to briefly summarize our results. At 330K FDDNP ligand binds with high affinity to the fibril as demonstrated by a large binding probability Pb = 0.69. Interestingly, all ligand structural groups G1-G3 (Fig. 1b) participate about equally in binding. Due to high hydrophobicity FDDNP ligands upon binding tend to self-aggregate (Fig. 2). As a result about two-thirds of bound ligands are aggregated forming clusters with the average size of < Sc >=1.7 and a typical bound FDDNP molecule interacts, on an average, with one other ligand. Corrected for self-aggregation the binding probability Pb increases to 0.78. Consistent with high binding affinity the ASA of a bound FDDNP ligand is reduced by 272 ± 12˚ A2 or by more than 50% of the unbound free state value. This reduction in ASA is attributed to both ligand-fibril and ligand-ligand 16 ACS Paragon Plus Environment

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interactions. We have also performed a thorough analysis of FDDNP binding energetics (Table 1). Electrostatic interactions make fairly small contribution to the binding energy (from 10 to 20%) and there are very few HBs formed between bound FDDNP and the fibril. Importantly, binding energetics depends on ligand-fibril as well as ligand-ligand interactions, which contribute about 30% of the total binding energy. We identified the amino acids, which are most frequently interact with FDDNP ligands. According to Fig. 3b and Table 2 the high affinity amino acids are exclusively hydrophobic and/or aromatic. These results are further supported by the analysis of the decrease in absolute ASA per amino acid caused by FDDNP binding. In particular, for an average aromatic and hydrophobic amino acids the reduction ˚2 , whereas for charged and polar amino acids it is only 7 or 6 in their ASA is 48 and 16 A ˚ A2 . To substantiate the importance of hydrophobic effect, below we utilize the following approach. We can define the loss in the relative hydrophobic accessible surface area (hASA) of amino acid i in the peptide m caused by ligand binding as < δhASA(i; m) >=< hASAu (i; m) > − < hASAb (i; m) >, where < hASAb (i; m) > and < hASAu (i; m) > are the relative hASA values with FDDNP ligands bound or removed (see Methods). Similarly, we introduce the loss in the relative polar accessible surface area (pASA) of amino acid i in the peptide m caused by ligand binding, < δpASA(i; m) >. Fig. 8a examines the correlations of < δhASA(i; m) > and < δpASA(i; m) > with the average number of contacts formed by amino acids i in the peptides m with the ligands, < Cl (i; m) >. The latter is taken as a measure of binding affinity of amino acid. When < δhASA(i; m) > and < δpASA(i; m) > are plotted against < Cl (i; m) > for all 186 amino acids in Aβ fibril fragment, a clear correlation emerges. Fig. 8a shows that the loss in the relative hydrophobic accessible surface area < δhASA(i; m) > strongly correlates with < Cl (i; m) > (the correlation coefficient 0.98), whereas the correlation observed between the loss in the relative polar accessible surface area < δpASA(i; m) > and < Cl (i; m) > is considerably weaker (correlation factor 0.61).



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Interestingly, weak correlation between < δpASA(i; m) > and < Cl (i; m) > is exclusively attributed to polar hydroxyl groups in tyrosine side chains. When they are excluded, the correlation factor drops to 0.27 suggesting that the interactions of FDDNP with Tyr10 hydroxyl group is a byproduct of the interactions with Tyr10 aromatic ring. According to Fig. 3b aromatic amino acids occupy the first (Tyr10), third (His14), and fifth (Phe19) places in the ranking of amino acid binding affinity with respect to FDDNP. This finding indicates that π-stacking interactions may play a critical role in FDDNP binding. To probe their importance we have analyzed the orientations of FDDNP and Tyr10 aromatic rings quantified by the angle γ between their normals. Fig. 4 showed that the probability distributions P (γ) of γ conformational states for bound and unbound FDDNP ligands are strikingly different supporting the formation of π-stacking interactions. To get further insight we map in Fig. S4a from Supporting Information the unbound distribution P (γ) onto the distribution Pr (γ) generated by considering random orientations of FDDNP G1 rings with respect to Tyr10 aromatic ring (see Methods). Fig. S4a reveals a good agreement between unbound P (γ) and Pr (γ) resulting in small RMSD of 0.002. Therefore, as expected unbound FDDNP ligands adopt random orientations with respect to Tyr10 side chains. It is more important to interpret the probability distribution P (γ) of γ conformational states for bound ligands, which shows two distinct peaks in Fig. 4. Following the approach described in the Methods we have assumed that the angles γ are represented by the superposition of two Gaussian distributions, which correspond to energy minimized (preferred) conformations of the rings. By minimizing the RMSD between Pg (γ) generated using assumed distribution of γ angles and observed P (γ), we recovered the preferred orientations of FDDNP aromatic rings bound to Tyr10 side chains. The two distributions, P (γ) and RMSD minimized Pg (γ), are shown in Fig. S4b. The minimum RMSD of 0.005 corresponds to the conformational state, in which FDDNP aromatic ring is tilted at the angles γB = 18◦ or 162◦ with respect to Tyr10 ring with the standard deviation in γ angles of 39◦ . Interestingly, these reconstructed conformations for the bound FDDNP correspond to a herringbone


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configuration of two aromatic rings forming π-stacking interactions as predicted theoretically. 50,51 Because the recovered orientation of FDDNP and Tyr10 aromatic rings is fairly well described by the herringbone configuration and similar P (γ) distributions were observed for other high affinity aromatic amino acids (His14 and Phe14), we surmise that π-stacking interactions play a critical role in FDDNP binding to Aβ fibril. Therefore, hydrophobic effect and π-stacking interactions are together the dominant factors governing FDDNP binding.

FDDNP biomarker binds to Aβ fibril at two distinct locations We have determined specific locations of FDDNP binding sites on Aβ fibril. Taking into account a two-fold fibril symmetry we grouped the four binding sites BS1-BS4 in Fig. 6a into two locations. The primary location referred to as NT binding sites includes BS1 and BS2 and is located at the N-terminals of Aβ peptides forming a fibril (Fig. 6b). The probability for FDDNP ligands to bind to the NT sites Pb (N T ) is 0.35 ± 0.03. The secondary location referred to as CE binding sites is composed of the binding sites BS3 and BS4 found at the fibril concave edge (Fig. 6c). The probability for a ligand to bind to these sites is Pb (CE) = 0.10 ± 0.02. The NT and CE binding sites represent approximately 65% of all binding ligands that increases to 70% if ligand self-aggregation is taken into account. The NT binding sites incorporate three amino acid types occurring at the N-terminal of Aβ, Tyr10, Val12, His14. The CE binding sites are also composed of three amino acid types, Phe19, Ile32, and Leu34. Although both the NT and CE sites are composed of hydrophobic and aromatic amino acids, there are profound differences between them. We showed that the CE total binding energy represented by the sum of van-der-Waals and electrostatic energies < Elp (CE) > + < Ell (CE) >= −24.5kcal/mol is noticeably lower than the NT total binding energy (−20.8kcal/mol). Yet, according to the computation of binding probabilities the NT binding affinity is more than three-fold higher than of CE. This observation suggests differences in the mechanisms of binding to these sites. An apparent distinction between the NT and CE binding sites is related to their geome19 ACS Paragon Plus Environment


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tries. The NT amino acids Tyr10, Val12, and His14 form a shallow groove shown in Fig. 8b. The average distance between the centers of mass of Tyr10 and His14 side chains is 13.7˚ A, whereas the depth of the groove measured by Val12 indentation is only 1.7˚ A(Fig. 8b). The observation that the NT groove is shallow has important consequence revealed by the analysis of the probability distribution P (κ) of conformational states with κ angles formed between the FDDNP and fibril axes (Fig. 7). By comparing P (κ) with the random distribution we concluded that FDDNP ligand does not adopt well defined orientation when bound to the NT. In contrast, CE amino acids are located near the entrances of two channels running parallel to the fibril axis and passing through the peptides P1,P3,P5 or P2,P4,P6 (Fig. 6a). We assumed that a ligand is inserted into the channel, if the centers of mass of G2 or G3 are positioned closer to the fibril center of mass along the fibril axis than the centers of mass of BS3 or BS4 amino acids. With this definition, about 35% of the ligands bound to BS3 and 7% bound to BS4 (both from CE) are inserted into the channels. The findings presented above can be rationalized, if one assumes that FDDNP ligand bound to the NT binding sites has smaller entropic loss ∆Sb (N T ) compared to CE. The factors contributing to small ∆Sb (N T ) are (i) a “smear” in ligand position and (ii) ligand self-aggregation. “Smear” of NT ligand positions can be directly evaluated by computing their standard deviation δRcm as shown in the Results. For the NT δRcm (N T ) = 5.9 ± 0.1˚ A. For comparison, the “smear” in the positions of ligands bound to the CE is reduced to δRcm (CE) = 4.9 ± 0.2˚ A. If one excludes the CE ligands inserted into the channel, which are already confined to restricted conformational space, δRcm (CE) decreases further to 4.6˚ A. Therefore, the ligands bound to the NT locations have larger conformational freedom than those bound to the CE. This point is consistent with nearly random orientations of FDDNP molecules bound to the NT (Fig. 7). The second factor favoring small NT entropic loss ∆Sb (N T ) compared to CE is related to FDDNP self-aggregation. To illustrate it consider the distributions of the average number of ligands < L(Sc ) > participating in the bound clusters of the size Sc (Fig. S5 from Supporting


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Information). For the NT binding sites < L(Sc ) > peaks at Sc = 2 with the average cluster size < Sc (N T ) > being 1.9. For the CE < L(Sc ) > reaches maximum at Sc = 1 and < Sc (CE) > is 1.5 (data not shown). These findings suggest that self-aggregation of FDDNP molecules bound to NT is more pronounced than at CE. Further support for this conclusion comes from the loss of ligand ASA due to ligand-fibril and ligand-ligand interactions. Due to binding to the fibril at the NT sites, the ASA of FDDNP molecule is reduced by 28% ˚2 ), whereas for the CE it is 44% (230 ˚ (145A A2 ). In contrast, due to ligand-ligand interactions ˚2 ) at the the ASA of FDDNP decreases 22% (114˚ A2 ) at the NT sites, but only 14% (75 A CE sites. Relatively small entropic loss associated with FDDNP binding to the NT binding sites is not the sole reason responsible for their high affinity. The second contributing circumstance is hydrophobic effect. When FDDNP molecule binds, the hydrophobic and polar ASAs of NT amino acids are reduced, on an average, by 13 and 3 ˚ A2 . In contrast, the losses in hydrophobic ˚2 . Thus, the loss in hydrophobic ASA and polar ASAs for CE amino acids are 7 and 2 A at the NT is twice larger than at the CE suggesting that the hydrophobic effect plays more important role in the binding to the NT. The third contributing reason is that the NT sites include Tyr10 amino acids, which have the strongest affinity with respect to FDDNP ligands implicating, according to Fig. S4, π-stacking interactions. In summary, the energetics of the NT and CE binding sites appears quite different. The NT is characterized by weaker van-der-Waals binding interactions, but stronger hydrophobic and aromatic interactions and more favorable binding entropy attributed to FDDNP binding “smear” and propensity for self-aggregation. In contrast, the CE sites offer stronger van-der-Waals binding interactions, but weaker hydrophobic and aromatic interactions and less favorable binding entropy. The FDDNP propensity for self-aggregation at the CE is also weaker than at the NT. Finally, it is important to comment on the applicability of our results obtained at 330K to physiological conditions. Although we cannot directly probe FDDNP binding to Aβ fibril at the temperatures lower than 330K, we can consider instead the temperature dependence



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of FDDNP binding to Aβ monomer (unpublished data and data from 37 ). We found that the composition of binding sites in Aβ monomer changes marginally as the temperature decreases from 330 to 310K. Specifically, out of six binding amino acids at 330K and 310K, five are common and all six at each temperature are hydrophobic. We also recomputed the average number of contacts with FDDNP ligands, < Cl (i) >, formed by amino acids i in Aβ monomer at 310K. The correlation factor between < Cl (i) > at 330K and 310K is 0.99. Although binding mechanisms for Aβ fibrils and monomers are not identical, these results and the fact that our in silico Aβ fibril structure is constrained to that obtained at physiological conditions (295K 4 ) suggest that the fibril binding mechanisms are similar at 310 and 330K.

Broader implications It is important to consider our results in the context of previous studies. X-ray crystallography experiments have resolved the atomic structures of amyloid fibrils complexed with the close analog of FDDNP, DDNP ligand. 16 In those experiments, the fibrils were assembled from VQIVYK fragment of tau protein. It was found that DDNP ligands are inserted into long channels running parallel to the fibril axis interacting with aromatic (Tyr) and hydrophobic (Ile, Val) amino acids. Importantly, the same amino acids also participate in the binding sites identified in our REMD simulations. The total loss of fibril ASA caused by DDNP was 242˚ A2 , which compares to only 134˚ A2 computed for our system. Because all bound DDNP ligands were inserted into fibril channels, it is not surprising that a single DDNP ligand occupies much larger fibril ASA than bound FDDNP molecule in our simulations, which rarely enters the channels. Thus, with respect to the amino acids types participating in binding there is a good agreement between the experiment and our simulations. Earlier experiments probing FDDNP binding to Aβ1-40 fibrils have concluded that FDDNP tends to localize in the hydrophobic clefts with part of the molecule exposed to 22 ACS Paragon Plus Environment

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water. 12 In line with those findings, our simulations suggest that about 50% of FDDNP ASA in the bound state remain hydrated and hydrophobic effect together with π-stacking interactions are the main factors governing FDDNP binding. Interestingly, the importance of hydrophobic and aromatic interactions has been also recognized in the simulations probing binding of thioflavin T (ThT) and PIB ligands to Aβ9-40 fibrils and short peptide self-assembly mimics. 33,52 Our REMD simulations have identified Aβ N-terminals (NT) located on the lateral fibril surfaces as primary binding sites, whereas the fibril edges (CE sites) have far weaker binding affinities. Qualitatively, such binding modes are similar to those observed in the simulations of PIB binding to Aβ9-40 fibrils, in which this biomarker primarily binds to the lateral fibril grooves formed by hydrophobic and aromatic side chains and only weakly to the fibril edges. 33 Similar to FDDNP, PIB avoids binding to charged or polar binding sites. However, the specific binding sites for FDDNP and PIB ligands are different. PIB strongly binds to the groove formed by Val18 and Phe20, whereas FDDNP is localized near the shallow Tyr10-Val12-His14 groove (NT binding sites). Another difference is related to the conformations adopted by bound ligands. PIB and ThT contain a conjugated benzothiazole ring and a separate dimethylaminobenzene ring and show tendency to align their long axes parallel to the groove upon binding. In contrast, FDDNP, which contains a single conjugated ring, does not adopt well defined orientation upon binding to the NT sites. It is unlikely that this difference arises, because FDDNP and PIB bind to different grooves as they have similar depths (1.7 and 1.5 ˚ A). It is more likely that rigid FDDNP cannot make multiple π-stacking interactions compared to more flexible PIB and ThT, which conform better to groove geometries. A recent molecular dynamics study has explored binding of FDDNP to a short amyloidogenic NFGAILS fragment from hIAPP peptide. 36 This study has demonstrated that FDDNP binds to two locations on the lateral sides of the amyloid β-sheet. Consistent with our results decomposition of binding energetics has showed that van-der-Waals interactions



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make far larger contribution than electrostatic interactions. Finally, it is worth noting that existence of the CE binding sites for FDDNP suggests that this biomarker can act as a weak anti-aggregation agent. This proposal is consistent with previous experimental studies. 53 Our study used Aβ10-40 peptide, in which nine amino-terminal residues are truncated. A natural question pertains to applicability of our data to a full-length Aβ1-40. We believe that, because the truncated N-terminal is highly polar (seven out of nine amino acids are polar or charged) and unstructured, it is unlikely to serve as FDDNP binding site in the two-fold symmetry fibril. A second question is related to the fibril polymorphism. Solid-state NMR experiments distinguished at least three Aβ polymorphic amyloid structures, which were recently analyzed in silico. 54 The two-fold symmetry fibril structure considered here was obtained in vitro under agitated conditions, 4 whereas under in vitro quiescent conditions three-fold symmetry fibrils form. 5 In both structures, Aβ peptides adopt strand-turn-strand folds, in which the NT binding sites (Tyr10, Val12, His14) exist in simular conformations. At the same time, due to large staggering shift of β-sheets, the conformations of the CE sites in the three-fold symmetry fibril are different. Then, because in the three-fold symmetry structure the NT binding site is exposed to water (as indicated by ASA computations) and thus available for FDDNP interactions, we predict that the NT binding sites are present in both in vitro polymorphic amyloids. The third, in vivo Aβ fibril polymorph has also three-fold symmetry, but features structured N-terminal and distinct conformations of the C-terminal. 6 Interestingly, the structure of the high affinity NT binding site remains largely unchanged and exposed to water. Indeed, the average RMSD between the NT binding sites in two-fold symmetry in vitro fibril (used by us) and three-fold symmetry in vivo fibril is just 0.8 ˚ A. Moreover, the NT accessible surface areas in these fibrils are also similar (1108 vs 865 ˚ A2 ). Consequently, we predict that the NT sites are still available for FDDNP binding in the in vivo Aβ fibril. However, the structure of the CE binding site (Phe19, Ile32, Leu34) is different in the in vivo three-fold symmetry fibril implying that the CE locations are specific to two-fold symmetry polymorphs. Taken together, these arguments indicate that NT, but


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not CE, binding sites identified for the in vitro fibril are “functional” in in vivo fibrils. At the same time, because the N-terminal in the in vivo structure is ordered and contains two aromatic amino acids, we cannot rule out its additional participation in FDDNP binding. A third question concerns with the comparison of FDDNP binding to different Aβ species. Such comparison is facilitated by our previous REMD study, which probed binding of FDDNP to Aβ10-40 monomer. 37 We note here that it is misleading to evaluate our previous and current simulations based on nominal ligand:peptide stoichiometric ratio, because in the Aβ fibril fragment two central peptides (P3,P4 in Fig. 1a) are mostly buried. Instead, we consider FDDNP concentrations and available ASA. In the fibril system, [FDDNP] is 27 mM, while similar concentration (32 mM) was used in our previous simulations, 37 in which three FDDNP ligands were coincubated with Aβ monomer. Furthermore, the concentrations of free FDDNP in the fibril and monomer systems are almost identical (6 and 5 mM). More importantly, as appropriate substitution of ligand:peptide stoichiometric ratio, we use protein binding ASA avaliable per one FDDNP ligand. If the total ASA of the fibril hexamer is 11,855 ˚ A2 and there are six ligands in the system, the binding ASA per one ligand is 1976 ˚ A2 . ˚2 , 37 therefore when coincubated For comparison, the average ASA of Aβ monomer is 3329 A ˚2 . These estimates argue with three FDDNP molecules the ASA per ligand becomes 1110 A that we can approximately compare FDDNP binding propensities in both systems keeping in mind that fibril system may slightly overestimate FDDNP binding due to larger available ASA. We first compare the mechanisms of binding to Aβ fibril and monomer. According to our previous study FDDNP binds to Aβ monomer primarily via hydrophobic interactions using two distinct sites located near the central hydrophobic cluster and in the C-terminal. 37 Self-aggregation of ligands plays important role in the binding to Aβ monomer. It follows then that the FDDNP binding sites in Aβ monomer and fibril are generally different except for the fibril secondary (CE) binding location, which has partial overlap with the monomer binding sites. It is also clear that hydrophobic effect is important for binding FDDNP to



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both the fibril and monomer, but π-stacking interactions provide critical contribution only for binding to the fibril. Indeed, an analysis of FDDNP binding to Phe20 in the monomer does not reveal any preferences in the orientations of FDDNP with respect to Phe20 aromatic rings ( 37 and unpublished data). Finally, ligand self-aggregation plays significant role upon binding to both Aβ structural species. Taking these observations together, we surmise that FDDNP binds to Aβ monomers and fibrils utilizing different mechanisms. To evaluate FDDNP binding affinities with respect to different Aβ species, we compare the probabilities Pb of binding to the fibril and monomer corrected for self-aggregation. The respective values are 0.78 ± 0.02 and 0.85 ± 0.05. 37 According to Fig. 5 the free energy of FDDNP binding to the fibril is ∆Fb (F ) = −1.5 ± 0.2RT , but with respect to the monomer ∆Fb (M ) is more favorable being equal to −2.4 ± 0.0RT (Fig. 5). Therefore, the difference in binding free energies ∆∆Fb = ∆Fb (F ) − ∆Fb (M ) is 0.9 ± 0.2RT > 0. To provide independent estimate, we determined the FDDNP binding free energy using atomic solvation parameters 55 and changes in ASA caused by ligand binding (see Supporting Information). According to our computations, the difference in the free energies of binding to Aβ fibril fragment and monomer, ∆∆Gb , is about 2.3RT > 0 at 330K. Thus, unexpectedly, these results implicate somewhat stronger FDDNP binding affinity with respect to Aβ monomer compared to the fibril. We have argued above that given simulation conditions the fibril ∆Fb may slightly overestimate the binding affinity. Then, this consideration and the results cited above implicate poor selectivity of FDDNP biomarker, which while being designed to image Aβ amyloid fibrils may equally well label benign Aβ monomers. The ability of FDDNP to label Aβ oligomers is unclear, although it is conceivable that given low structural order of small oligomers they are recognized by FDDNP similar to Aβ monomers.


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Conclusions Using all-atom REMD simulations we have examined binding of FDDNP biomarkers to Aβ amyloid fibril fragment. In summary, our results are three-fold. First, REMD simulations suggest that FDDNP ligands bind with high affinity to Aβ fibril and hydrophobic effect coupled with π-stacking interactions are the dominant factors governing FDDNP binding. Electrostatic interactions and hydrogen bonding appear to play minor role. Second, our simulations reveal a strong tendency of bound FDDNP molecules for self-aggregation. Accordingly, about two-thirds of all bound ligands are forming aggregated clusters of various sizes and ligand-ligand interactions make considerable contribution to FDDNP binding energetics. Third, FDDNP ligands bind to two distinct sites on Aβ fibril. Primary binding sites (NT) are located on the fibril sides at the N-terminals of Aβ10-40 peptides, whereas secondary ones (CE) occur on the concave fibril edge near the fibril channels. According to our analysis, the NT sites are characterized by strong hydrophobic and π-stacking interactions, favorable binding entropy resulting from multiple FDDNP binding orientations and propensity for self-aggregation, but relatively weak van-der-Waals binding interactions. In contrast, the CE sites offer stronger van-der-Waals binding interactions, but weaker hydrophobic and aromatic interactions and less favorable binding entropy. By comparing our data with previous studies we argued that the primary binding locations identified by us are likely to occur in various Aβ polymorphic fibrils. We have also compared the mechanisms of FDDNP binding to Aβ fibrils and monomers and concluded that they are different. Finally, we proposed that FDDNP binds with stronger affinity to benign Aβ monomers compared to Aβ fibrils raising questions about the ability of this biomarker to selectively label amyloid deposits. The last point suggests that further work may be needed to improve FDDNP selectivity with respect to Aβ fibrils, a signature AD species.



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Acknowledgement The authors declare no conflict of interest. Supporting Information: Further details concerning the model and methods used and additional data are provided in Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

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Table 1: Energetics of FDDNP binding

interaction vdw ligand-fibril, van-der-Waals, < Elp > el ligand-fibril, electrostatic, < Elp > ligand-fibril, total, < Elp >

energy, kcal/mol −10.6 ± 0.3 −3.0 ± 0.2 −13.7 ± 0.5

ligand-ligand, van-der-Waals, < Ellvdw > ligand-ligand, electrostatic, < Ellel > ligand-ligand, total, < Ell >

−6.3 ± 0.5 −1.1 ± 0.1 −7.4 ± 0.6

Table 2: Amino acids in Aβ fibril with high binding affinity to FDDNPa

amino acid i peptide m < Cl (i; m) > Tyr10 P4 0.70 Tyr10 P3 0.55 Tyr10 P2 0.51 Val12 P4 0.46 Val12 P2 0.43 Tyr10 P1 0.40 Val12 P6 0.37 Tyr10 P6 0.35 Val12 P5 0.33 Val12 P3 0.31 His14 P2 0.27 His14 P4 0.26 Tyr10 P5 0.25 Ile32 P5 0.25 Phe19 P6 0.24 Val12 P1 0.22 a

amino acids are selected using the criterion < Cl (i; m) >> 0.2



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Table 3: Energetics of FDDNP binding sites

interaction vdw ligand-fibril, van-der-Waals, < Elp > el ligand-fibril, electrostatic, < Elp > ligand-fibril, total, < Elp >

NT energies, kcal/mol CE energies, kcal/mol -9.5 ± 0.3 -14.4± 0.6 -2.5 ± 0.2 -3.4± 0.2 -12.0 ± 0.4 -17.8± 0.8

ligand-ligand, van-der-Waals, < Ellvdw > ligand-ligand, electrostatic, < Ellel > ligand-ligand, total, < Ell >

-7.4 ± 0.6 -1.3 ± 0.2 -8.8 ±0.7

-6.1± 0.8 -0.6± 0.1 -6.7± 0.9

Figure captions Fig. 1 (a) A unit cell of all-atom explicit water simulation system designed to probe FDDNP binding to Aβ fibril. Aβ10-40 peptides P1-P6 forming hexameric fibril fragment are shown in cartoon representation and shades of blue. Six FDDNP ligands are colored in yellow, whereas water molecules are depicted in grey. Fibril axis shown by blue arrow is defined as a line connecting the centers of mass of the peptides P1,P2 and P5,P6. The fibril fragment shows its concave edge, on which Aβ C-terminals are indented along the fibril axis. Convex edge features protruding C-terminals. (b) Chemical structure of FDDNP biomarker. Molecule can be divided into conjugated aromatic ring (G1), fluoro terminal (G2), and dicyano terminal (G3). Nitrogen and fluorine atoms are in blue and green, carbons and hydrogens are in grey and white, respectively. Fig. 2 Distribution of the average number of ligands < L(Sc ) > participating in the FDDNP clusters of the size Sc . Data in dark grey are for the clusters bound to the fibril, whereas data on light grey represent unbound ligand clusters. The plot reveals strong propensity of FDDNP for self-aggregation. Vertical bars indicate sampling errors. Fig. 3 (a) A heatmap presenting the average number of contacts < Cl (i; m) > formed by amino acid i in the peptide m with FDDNP ligands. The values of < Cl (i; m) > are colorcoded according to the right scale. (b) Number of ligand contacts < Cl (i) > formed by amino


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acids i in a generic fibril Aβ peptide, i.e., < Cl (i) > is obtained by averaging < Cl (i; m) > over six peptides. Both panels identify Aβ amino acids with high binding affinity with respect to FDDNP ligands. FDDNP molecules show strong preference for binding to aromatic and hydrophobic amino acids. Fig. 4 Probability distributions P (γ) of the conformational states, in which the normals to FDDNP G1 group and Tyr10 aromatic ring form an angle γ. Distributions in red and black correspond to FDDNP ligands, which are either bound or unbound to Tyr10, respectively. A striking difference between the two P (γ) is taken as an evidence of π-stacking interactions between FDDNP and Tyr10. Vertical bars indicate sampling errors. Fig. 5 Free energy of FDDNP molecule F (rb ) as a function of the distance rb to the surface of Aβ fibril (in black) or monomer (in red, data from 37 ). Bound B and unbound U states separated by the boundary at rb = rb,0 are used for computing Aβ fibril binding free energy ∆Fb . For Aβ fibril and monomer rb,0 are 14.5 and 11.0 ˚ A, respectively. To compute FU we assumed that F (rb ) = 0 for rb,0 < rb . The figure suggests that ∆Fb for Aβ monomer is more favorable than for the fibril. Vertical bars indicate sampling errors. Fig. 6 (a) Surface view of hexameric Aβ fibril fragment colored according to the average number of contacts < Cl (i; m) > formed by amino acid i in the peptide m with FDDNP ligands. Color scale is the same as in Fig. 3a. Concave (left) and convex (right) views are taken along the fibril axis. The figures shows that FDDNP binds to few specific locations represented by the binding sites BS1-BS4. (b) Top and bottom lateral views of Aβ fibril fragment showing primary NT binding sites, BS1 and BS2, located at the Aβ N-terminals (in red). (c) Concave edge view of Aβ fibril fragment showing secondary CE binding sites, BS3 and BS4 (in green). Bound FDDNP molecules in (b,c) are in yellow. Fig. 7 Probability distribution P (κ) of conformational states, in which FDDNP molecules bound to the NT binding sites form angle κ with the fibril axis (dashed line). Solid line represents the distribution Pr (κ) generated using random orientations of ligands with respect to the fibril axis. Similarity between P (κ) and Pr (κ) indicates lack of preferred orientations 37 ACS Paragon Plus Environment


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of FDDNP ligands bound to the NT binding sites. Vertical bars indicate sampling errors. Fig. 8 (a) Scatter plot probing a correlation between the losses in the relative hydrophobic accessible surface area of amino acid i in the peptide m caused by ligand binding, < δhASA(i; m) >, and the average number of contacts < Cl (i; m) > with FDDNP ligands (data in blue). Correlation between the losses in the relative polar accessible surface area caused by ligand binding, < δpASA(i; m) >, and < Cl (i; m) > is represented by data in red. Strong correlation between < δhASA(i; m) > and < Cl (i; m) > implicates hydrophobic effect in governing FDDNP binding to Aβ fibril. (b) View of the NT binding site in Aβ fibril along the fibril axis. Orange lines mark the average distances between the centers of mass of Tyr10, Val12, and His14 side chains. The red arrow measures the depth of the NT groove formed by the side chains of these amino acids.


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(a)

P2 P4 P6 P1 P3 P5

dge convex e fib ril a

xi

s edge concave

(b) G1 G3

G2

Figure 1

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Figure 2 ACS Paragon Plus Environment

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(a)

(b)



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Figure 4


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B

U

rb,0

ru,0

Figure 5



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(a) channel

channel

BS2

BS3

BS4 BS1

channel

BS1 convex edge

concave edge

Figure 6


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(b) NT (BS1)

NT (BS2)

(c) CE (BS3)

CE (BS4)

Figure 6



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Figure 7


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(a)

(b)

Tyr10

His14 Val12



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Molecular Mechanisms of Alzheimer's Biomarker ... - ACS Publications

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