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Impact of Phosphorylation and Pseudophosphorylation on the Early Stages of Aggregation of the Microtubule-Associated Protein Tau Dmitriy V. Prokopovich,† John W. Whittaker,†,‡ Micaiah M. Muthee,† Azka Ahmed,† and Luca Larini*,†,‡ †

Department of Physics and ‡Center for Computational and Integrative Biology, Rutgers University-Camden, Camden, New Jersey 08102, United States S Supporting Information *

ABSTRACT: The microtubule-associated protein tau regulates the stability of microtubules within neurons in the central nervous system. In turn, microtubules are responsible for the remodeling of the cytoskeleton that ultimately leads to the formation or pruning of new connections among neurons. As a consequence, dysfunction of tau is associated with many forms of dementia as well as Alzheimer’s disease. In the brain, tau activity is regulated by its phosphorylation state. Phosphorylation is a post-translational modification of proteins that adds a phosphate group to the side chain of an amino acid. Phosphorylation at key locations in the tau sequence leads to a higher or lower affinity for microtubules. In Alzheimer’s disease, tau is present in an abnormal phosphorylation state. However, studying the effect of phosphorylation experimentally has been extremely challenging as there is no viable way of exactly selecting the location and the number of phosphorylated sites. For this reason, researchers have turned to pseudophosphorylation. In this technique, actual phosphorylation is mimicked by mutating the selected amino acid into glutamate or aspartate. Whether this methodology is equivalent to actual phosphorylation is still open to debate. In this study, we will show that phosphorylation and pseudophosphorylation are not exactly equivalent. Although for larger aggregates the two techniques lead to similar structures, the kinetics of the process may be altered. In addition, very little is known about the impact that this may have on the early stages of aggregation, such as nucleation and conformational rearrangement. In this study, we show that the two methods may produce a similar ensemble of conformations, even though the kinetic and chemical details that lead to it are quite different.

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INTRODUCTION The microtubule-associated protein (MAP) tau is found in the central nervous system, specifically in the axon of the neuron.1−3 Tau is tasked with regulating the modifications of the cytoskeleton so that neurons can form or prune connections to other neurons. In addition, microtubules are essential for the axoplasmatic (or axonal) transport, a process where molecules and organelles produced in the body of the cells are transferred to their final location in the axon. As a consequence, tau is an extremely important protein in the human brain whose fine regulation is essential for the correct development of the brain itself. For these reasons, many diseases, some of them due to mutations (such as many tauopathies) and others sporadic (such as Alzheimer’s disease), are a consequence of the disruption of its correct regulation.1−7 Tau protein is present in the human body in six isoforms. All of these isoforms are produced by alternate splicing of the same gene. Tau is classified as an intrinsically disordered protein (IDP). IDPs are proteins that lack a well-defined tertiary structure but fluctuate instead among different conformations. In the case of tau, secondary structure is mostly absent as well. IDPs are generally rich in charged residues that make these proteins extremely soluble and have a small amount of © 2017 American Chemical Society

hydrophobic moieties, so that hydrophobic collapse is prevented.8−10 Therefore, it is expected that IDPs under normal circumstances would find it difficult to aggregate. Surprisingly, though, aggregated tau is involved in many diseases that lead to dementia and abnormal brain development. In particular, aggregated tau is a hallmark of Alzheimer’s disease, where it forms neurofibrillary tangles. Tau aggregates present a “fuzzy coat” that surrounds a region rich in β-sheet. The β-sheet-rich region, which composes the microtubulebinding region (MTBR) of the protein, shows a structure typical of amyloid fibers.11−15 The MTBR consists of four pseudorepeats (referred to as M1−M4 in Figure 1), which share similar amino acid sequences. On the basis of the structure of the MTBR, tau isoforms can be split into two groups, generally referred to as 3R and 4R (where R stands for “repeat”). 4R isoforms contain all four repeats, whereas 3R isoforms are missing the second repeat. Received: January 9, 2017 Revised: February 17, 2017 Published: February 20, 2017 2095

DOI: 10.1021/acs.jpcb.7b00194 J. Phys. Chem. B 2017, 121, 2095−2103

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The Journal of Physical Chemistry B

the two techniques comes from the measurements of electrophoretic mobility, which includes information about both size and conformation.36,42 Those studies have shown that the mobility varies in a similar way when experiments are conducted using either pseudophosphorylation or phosphorylation. For this reason, pseudophosphorylation is believed to be a reasonable approximation to phosphorylation that avoids the problem of having a mixture of tau phosphorylated at different sites. However, phosphorylation and pseudophosphorylation are not exactly equivalent. For instance, a phosphate group bears two negative charges, whereas Asp and Glu possess only one. In addition, the fact that structures may show a similar mobility does not necessarily lead to the conclusion that they adopt the same conformation. As tau is an IDP, different conformations may be adopted that have a similar size but highly different internal structures. We will show in this study that this is actually the case. Another reason for investigating their equivalence is to understand whether phosphorylated tau and pseudophosphorylated tau interact similarly during aggregation and microtubule binding. Several studies have shown consistently that phosphorylated tau and pseudophosphorylated tau may lead to similar aggregation propensity or microtubule affinity.39−42 However, phosphorylated tau found in pathological aggregates is generally a mixture of proteins phosphorylated at different locations and with a different number of phosphorylated sites. As a consequence, it is often impossible to assess if a pseudophosphorylated site is actually equivalent to phosphorylation at the same site.

Figure 1. Sequence of the MTBR region of tau. The four repeats, M1−M4, are highlighted. The residue numbers are reported and are based upon the longest isoform of tau. The fragment used in this study is shown at the bottom. The phosphorylated residues are underlined.

To perform its function, tau undergoes extensive phosphorylation. Phosphorylation consists of adding covalently bound phosphate groups to the amino acid side chains.16−19 The most common sites of phosphorylation in tau are amino acids serine, threonine, and tyrosine. In tau, this corresponds to as many as 85 sites, about 20% of the total number of amino acids. However, at any given time, only a few sites are phosphorylated. As the phosphorylation sites are turned over rapidly, it is difficult to measure accurately how many sites are phosphorylated. It is estimated that about 18 sites are phosphorylated in a healthy cell, whereas this number increases in cells affected by Alzheimer’s disease (an effect called hyperphosphorylation).3,20,21 In healthy cells, it is believed that multiple kinases, such as MAPK,22 GSK-3β,23 MARK,24 cdk2, and cdk5,25 are involved in the phosphorylation process. This would suggest that tau is initially primed by one kinase before a second kinase would phosphorylate a nearby site. In this way, the final phosphorylated state is a consequence of multiple events that involve different kinases (and maybe phosphatases). As a consequence, at any given time, multiple phosphorylated versions of the same protein exist, depending on which pathway was followed. This is a formidable problem to deal with experimentally. In fact, to obtain a well-defined phosphorylated state in vitro, it is required to know not only the kinases involved but also their order in the sequence. In addition, as the yield of conversion to a phosphorylated state is never 100%, this means that the product will contain a mixture of different phosphorylated states. In Alzheimer’s disease, tau is found in its hyperphosphorylated state. Even though tau is always phosphorylated in healthy cells, the number of phosphorylated sites in Alzheimer’s disease is 3−4 times higher than normal.26−28 The reason for this abnormal phosphorylation is unknown, though some studies have suggested that this may be a consequence of inhibition of phosphatases PP2A.29−31 To overcome the problem of phosphorylation in both in vitro and in cell culture, experimentalists have turned to pseudophosphorylation.32−46 Pseudophosphorylation consists of replacing selected amino acids with amino acids aspartate (Asp) or glutamate (Glu). In practice, this technique involves introducing mutations to the original sequence. The rationale behind this technique is that tau interactions are mostly electrostatic in nature. As a consequence, replacing a negatively charged side chain (such as a phosphorylated one) with another negatively charged side chain (such as Asp or Glu) should have minimal impact on the properties of the protein. In this study, we will characterize whether phosphorylation and pseudophosphorylation are actually equivalent at a molecular level. The strongest support of equivalence between

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METHODS Fragment Selection. In this study, we will focus on fragment PHF43 of the tau protein. This fragment was shown to be able to aggregate and form amyloid fiber.47 In addition, it corresponds to a full repeat, so that it can be used to assess if one repeat alone is enough to bind to microtubules. We will refer to the wild-type form of PHF43 as PHF43/WT. In addition, computational and experimental studies have shown that phosphorylation of Ser324 alone or phosphorylation of both Ser320 and Ser324 may lead to enhanced aggregation propensity.48 However, these studies have been performed on fragments of tau much shorter than the ones considered in our study. In this work, we will consider both the phosphorylated (PH43/P) and pseudophosphorylated fragments, modifying both Ser320 and Ser324 at once. For the pseudophosphorylated fragment, we will compare the mutation of Ser320 and Ser324 into glutamic acid (PH43/PGlu) and aspartic acid (PHF43/PAsp) in respective simulations. Simulation Details. Data was collected in canonical NVT ensemble using the Amber14 package.49,50 We used the Amber96 force field in association with the generalized Born implicit solvent model. This model was shown by Dill and coworkers51 to correctly reproduce the secondary structure of peptides. This choice was made necessary because of the large size of our system that would prevent otherwise a proper sampling of the ensemble of conformation if performed in fully atomistic resolution. For the phosphorylated amino acids, we used the force field developed by Homeyer et al.52 The cutoff for both the van der Waals interactions and the implicit solvent model is 2 nm. A Langevin thermostat kept the temperature constant. The equations of motion were integrated using the leap-frog algorithm with a time step of 1 ps. Constraints for 2096


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reported in the rest of the article is computed using this method (unless otherwise noted) as it is more accurate than the integration of the normalized distribution function. The main difference is that whereas conformations may be very close in size (and so they appear in the same area of the radius of gyration vs end-to-end distance plot), they may show a different secondary structure. Because of the fact that characterization of the secondary structure is of utmost importance to this work, we will generally report the cluster abundance. Hydrogen Bond Analysis. The number of hydrogen bonds is essential to estimate the amount of secondary structure in a protein. A hydrogen bond is considered present if the nitrogen, N, and oxygen, O, in two different peptide bonds are at a distance smaller than 3.5 Å and the angle N− OC is bigger than 120°.

hydrogen atoms connected to heavy atoms were computed with the SHAKE algorithm. To simulate the prionlike behavior of the peptide, we used a dimer. The backbone of one of the chains was frozen in its original position, whereas the second chain was free to sample different conformations. To avoid the case wherein the chains separate and stop interacting, we added a restraint potential. This potential was zero as long as the distance between the centers of mass of the chains was below 4 nm. Beyond this distance, a parabolic restraining potential was added. If the chains separated farther than 4.5 nm, a linear potential was implemented instead. To accurately sample the conformational space of the peptide, replica-exchange molecular dynamics was employed. This involved running multiple simulations of the same system in parallel at different temperatures. Every 3 ps conformations were exchanged between temperatures using a metropolis algorithm. The temperatures ranged from 279 to 503 K using 16 replicas. Only data collected at 300 K has been analyzed. The simulations were at least 1.2 μs long, but in any case, simulations from only the final 300 ns were analyzed. Equilibration details are reported in the Supporting Information. Radius of Gyration and End-to-End Distance. First, to have a qualitative description of the entire set of conformations, we have decided to plot the radius of gyration of the molecules against their end-to-end distance. The radius of gyration provides a rough estimate of the size of the aggregate, whereas the end-to-end distance allows estimation of the entropy of each conformation. The radius of gyration can be thought of as the radius of a sphere that encloses the protein. However, for any given radius, there can be multiple arrangements of the protein backbone. For this reason, we need a second parameter that characterizes this conformational ensemble. We chose the end-to-end distance in agreement with the theory of ideal polymer chains.53 On the basis of these considerations, we can expect that compact conformations will appear in the lower left corner of the plot, whereas the more extended conformations will populate the upper right corner. In addition, as only one conformation is available for a fully extended chain, we expected that the entropy will decrease as the radius of gyration of the molecule increases. This means that the number of conformations and, as a consequence, the possible values of the end-to-end distance decrease with increasing radius of gyration. Cluster Analysis. To gain a better understanding of the conformations populated by the molecule, we performed a cluster analysis using the Daura algorithm54 as implemented in the GROMACS package.55−58 The clustering algorithm compares the conformations found in our simulation and groups them according to their root-mean-square deviation (RMSD). The idea is that structures that are very close show a smaller RMSD. The RMSD of two molecules is defined as

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RESULTS Wild Type. We start analyzing the structure of the monomer of PHF43/WT in solution. Figure 2 shows that

Figure 2. Analysis of PHF43/WT. (Top) Normalized probability distribution of finding a protein in a specific conformation as defined by its radius of gyration and end-to-end distance. (Bottom) Histograms of the number of hydrogen bonds.

the monomer can easily populate multiple conformations ranging from compact (end-to-end distance about 10 Å) to extended structures (about 100 Å). As a comparison, a fully extended chain would be about 120 Å long. This is consistent with the intrinsically disordered nature of tau. To gain more detail about the structural features of these conformations, we computed the number of hydrogen bonds formed by the backbone. We found that very little secondary structure is present. Once more, this is consistent with the fact that tau is one of the most unstructured proteins known.1 However, this is

N

RMSD =

1 ∑ (Ai − Bi )2 N i=1

where N is the number of atoms in one molecule, Ai and Bi are the ith atoms of molecules A and B, respectively. In our study, we consider structures with a RMSD < 3 Å to belong to the same cluster. The algorithm also takes care of ranking the clusters according to their abundance, with the first cluster being the most abundant. The abundance of the population 2097


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The Journal of Physical Chemistry B in contrast with the β-rich conformations adopted by the peptide during aggregation. As a consequence, we decided to investigate how likely the peptide is to spontaneously adopt a β-rich structure. We found that a small population of folded proteins is indeed present (see Figure 2, top, highlighted region). A closer look at this small population of conformations shows that it forms a four-stranded β-sheet structure with a very high number of hydrogen bonds (Figure 3). This structure is also temporarily stabilized by two salt bridges, one being between E43 and K22 and the other between E43 and K3.

Figure 4. PHF43/PGlu mutation. (Top) Normalized probability distributions of finding a chain with a specific end-to-end and radius of gyration value. (Bottom) Histograms of the number of hydrogen bonds.

Figure 3. (Top) Wire-frame representation of the structure shown in Figure 2 (inset). (Bottom) Distribution of the hydrogen bonds for the same cluster.

Pseudophosphorylation: Glutamate. To evaluate the importance of phosphorylation, we turned to the case of pseudophosphorylated PHF43. Pseudophosphorylation is extensively used in the study of tau dynamics; thus, it is of paramount importance to understand how this mutation affects the protein structure. A comparison of PHF43/WT and PHF43/PGlu shows that the overall ensemble of conformations is not affected by the double mutation (Figure 4). The analysis of the number of hydrogen bonds shows no major difference from PHF43/WT. Once more, we could identify a cluster of conformations from the ensemble with a radius of gyration of about 10 Å. Once those conformations were singled out, we found that 46.6% of this region comprises β-rich structures that look similar in the same region. Looking at the hydrogen bond plot in Figure 5, it can be gathered that the most likely number of bonds is below 10. A very similar plot is seen in the analysis of the wild-type tau, once again supporting the ineffectiveness of the mutation in promoting the secondary structure. On the basis of the previous analysis, we can conclude that pseudophosphorylation does not significantly enhance the presence of β-rich structures. Pseudophosphorylation: Aspartate. Another form of pseudophosphorylation involves aspartate as the replacement for serine. The heat graph for aspartate looks similar to that of the wild type and the glutamate. In fact, the heat plot shows even less secondary structure than that of the wild type and

Figure 5. Distribution of the hydrogen bonds for the cluster highlighted in Figure 4. (Inset) Wire-frame representation of the most abundant structure.

glutamate. This is indicated by the absence of the structures with a radius of gyration of about 10 Å, as found in the previous analysis (Figure 6). Despite the lack of a defined region above the 10 Å point, we narrowed down which conformations most densely populate that area. The most popular conformation was found to be a triple-stranded β-sheet structure. Looking at the 2098


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Figure 6. PHF43/PASP mutation. Normalized probability distribution of finding a chain with specific end-to-end and radius of gyration values (top) and hydrogen bond distribution (bottom).

Figure 7. PHF43/P. Normalized probability distribution of finding a chain with specific end-to-end and radius of gyration values (top). Hydrogen bond distribution (bottom).

hydrogen bond plot in Figure 6, we can see that the number of bonds formed is very low, similar to that in the glutamate hydrogen bond plot. Although these secondary structures exist, there are not enough to conclude that aspartate is playing a crucial role in supporting secondary structure. Therefore, in terms of effectiveness in causing aggregation of PHF43, the use of glutamate is the more favorable because of its higher probability of forming the secondary structure. Actual Phosphorylation. The final set of simulations involves the addition of a bona fide phosphate group. The difference from the wild-type case is now dramatic (Figure 7, top). The tridimensional structure of the protein collapses to a very compact monomer. A phosphate group differs from aspartate and glutamate because of its larger size as well as its higher negative charge. Thus, each phosphate group is able to strongly coordinate two/three lysines at the same time. As a consequence, the backbone can adopt a very compact conformation thanks to strong ionic bonds formed with the phosphate. In addition, these conformations are stabilized by a large number of hydrogen bonds (Figure 7, bottom). Dimerization. To study dimerization, we selected one chain from the ensemble of conformations for the wild-type molecule. The structure selected shows a β-sheet conformation and is one of the most populated conformations that is rich in β-structure. It should be noted that, overall, this type of conformation represents only about 1% of the total conformations. Thus, it should be considered a rare intermediate. However, standard MD simulations as well as replica-exchange simulations show that, although rare, this conformation is long-living. This can be clearly seen from the equilibration plot in the Supporting Information (Figures S5 and S6; spot with a radius of gyration of about 10 Å). To speed up the simulation time, this conformation’s backbone was

frozen. A second chain, fully flexible, was added. The second chain was forced to lie within a sphere of radius 4 nm. As shown in Figure 8, the second chain does actually fold into a

Figure 8. PHF43/WT dimer. Normalized probability distribution of finding the flexible chain with specific end-to-end and radius of gyration values.

compact conformation. These conformations are characterized by a high number of hydrogen bonds. Another important aspect to note is that the dimers are actually unstable. That means that the frozen chain acts as a pattern that forces the other chain to fold (Figure 8) into a β-rich conformation (Figure 9, top). However, stable aggregates are unable to form (Figure 9, bottom). This is analogous to the behavior of a prion, suggesting that, although rare, these compact conformations can form and rapidly propagate similar to a prion infection. 2099


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Once two chains are close enough to interact, it is expected that this may lead to a structural transition. This is the idea of the “dock-lock” model,59−65 in which two chains first attach to each other (“dock” stage) and then a structural transition of the chains leads to a more stable aggregate (“lock” stage) (Figure 10). It is reasonable to expect that this is the case for tau. In

Figure 10. Proposed pathway. Step I requires the chain to convert into a compact monomer. Normalized probability distribution of finding a chain with specific end-to-end and radius of gyration values. In step II, the chain forms a complex with an incoming chain (“dock” stage). In step III, the second chain undergoes a structural transition. However, as dimers are unstable, the complex breaks and releases two monomers, which can further propagate the process, in the same way as prion infection.

fact, whereas tau is particularly unstructured in solution, its aggregated state shows a β-rich structure. This is also hinted at by our simulations involving a frozen chain. In that case, the βsheet conformation leads another chain to fold as well. If we assume that the same process takes place for phosphorylated and pseudophosphorylated tau, we can expect some differences. For example, although phosphorylated tau has a lower repulsion, the lock stage would be much slower as the salt bridges formed by lysine and phosphate are extremely strong. On the other hand, although the ionic bonds in pseudophosphorylated tau are weaker, the structural transition is much faster. As a consequence, we would expect phosphorylated tau to form many stable monomers that only slowly convert into dimers or larger structures. On the other hand, pseudophosphorylation would rely on a prionlike behavior where very rare transitions into compact conformations trigger a transition to nearby peptides, which in turn propagate the structural transition to other chains. Prionlike behavior of tau has actually been reported in the literature.66−68 As dimers formed at this stage are unstable, probably due to electrostatic repulsion, we would expect that the solution will be populated by long-living compact monomers that originate from the dissociation of these unstable dimers. In conclusion, both pseudophosphorylation and phosphorylation lead to a large population of compact monomers as found experimentally, even though the pathway is different. As a side note, this model would not predict a direct pathway that leads to the formation of amyloid fibers. In fact, this model postulates an intermediate state composed of compact monomers. In particular, for the wild-type and pseudophosphorylated chains, it is expected that these compact conformations would form around the original folded state. As the prionlike process propagates, a cloud of compact monomers would form around this initial compact structure. This cloud is expected to have an almost spherical symmetry, as there is no preferential direction of growth. Once this region reaches a critical threshold, it would collapse into an amyloid fiber. This is consistent with the existing experimental data.69

Figure 9. PHF43/WT dimer. (Top) The number of hydrogen bonds for the incoming chains. (Bottom) Distance between the centers of mass of a dimer. Most of the conformations are far apart. The frozen chain is shown in purple.

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DISCUSSION The data collected from the simulations of the monomers suggests that pseudophosphorylation and actual phosphorylation may lead to quite different conformations. Experimentally, it is extremely difficult to characterize the structure of a single peptide chain, in particular, when a well-defined native structure is not present. However, experiments suggest that aggregation products should not differ too much.32−46 As a consequence, we must explain how such a large difference in monomer conformations may lead to relatively similar aggregation pathways. To understand how this is possible, we will begin with a discussion of the wild type. As noted above, the wild-type chain adopts conformations that are similar to those of the pseudophosphorylated chain. However, the wild-type protein possesses a total charge of +4, whereas the pseudophosphorylated version has only +2 charge. This means that although the wild-type and the pseudophosphorylated peptide have similar conformations, the repulsion among the chains in the latter is much smaller. A similar argument applies to PHF43/P. As a consequence, this alone would lead to a faster aggregation if only charge repulsion is relevant. In this respect, pseudophosphorylation and phosphorylation would have a similar effect. 2100


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(5) Medina, M.; Avila, J. Further Understanding of Tau Phosphorylation: Implications for Therapy. Expert Rev. Neurother. 2015, 15, 115−122. (6) Ando, K.; Maruko-Otake, A.; Ohtake, Y.; Hayashishita, M.; Sekiya, M.; Iijima, K. M. Stabilization of Microtubule-Unbound Tau via Tau Phosphorylation at Ser262/356 by Par-1/Mark Contributes to Augmentation of Ad-Related Phosphorylation and Aβ42-Induced Tau Toxicity. PLoS Genet. 2016, 12, No. e1005917. (7) Meyer, V.; Dinkel, P. D.; Luo, Y.; Yu, X.; Wei, G.; Zheng, J.; Eaton, G. R.; Ma, B.; Nussinov, R.; Eaton, S. S.; et al. Single Mutations in Tau Modulate the Populations of Fibril Conformers Through Seed Selection. Angew. Chem., Int. Ed. 2014, 53, 1590−1593. (8) Uversky, V. N. A Decade and a Half of Protein Intrinsic Disorder: Biology Still Waits for Physics. Protein Sci. 2013, 22, 693−724. (9) Dias, C. L.; Karttunen, M.; Chan, H. S. Hydrophobic Interactions in the Formation of Secondary Structures in Small Peptides. Phys. Rev. E 2011, 84, No. 041931. (10) Narayanan, C.; Dias, C. L. Hydrophobic Interactions and Hydrogen Bonds in B-Sheet Formation. J. Chem. Phys. 2013, 139, No. 115103. (11) Jakes, R.; Novak, M.; Davison, M.; Wischik, C. M. Identification of 3- and 4-Repeat Tau Isoforms within the PHF in Alzheimer’s Disease. EMBO J. 1991, 10, 2725−2729. (12) Wischik, C. M.; Novak, M.; Edwards, P. C.; Klug, A.; Tichelaar, W.; Crowther, R. A. Structural Characterization of the Core of the Paired Helical Filament of Alzheimer Disease. Proc. Natl. Acad. Sci. U.S.A. 1988, 85, 4884−4888. (13) Conde, C.; Cáceres, A. Microtubule Assembly, Organization and Dynamics in Axons and Dendrites. Nat. Rev. Neurosci. 2009, 10, 319− 332. (14) Luo, Y.; Ma, B.; Nussinov, R.; Wei, G. Structural Insight into Tau Protein’s Paradox of Intrinsically Disordered Behavior, SelfAcetylation Activity, and Aggregation. J. Phys. Chem. Lett. 2014, 5, 3026−3031. (15) Qi, R.; Luo, Y.; Wei, G.; Nussinov, R.; Ma, B. Aβ “Stretchingand-Packing” Cross-Seeding Mechanism Can Trigger Tau Protein Aggregation. J. Phys. Chem. Lett. 2015, 6, 3276−3282. (16) Stoothoff, W. H.; Johnson, G. V. W. Tau Phosphorylation: Physiological and Pathological Consequences. Biochim. Biophys. Acta, Mol. Basis Dis. 2005, 1739, 280−297. (17) Noble, W.; Hanger, D. P.; Miller, C. C.; Lovestone, S. The Importance of Tau Phosphorylation for Neurodegenerative Diseases. Front. Neurol. 2013, 4, No. 83. (18) Šimić, G.; Babić Leko, M.; Wray, S.; Harrington, C.; Delalle, I.; Jovanov-Milošević, N.; Bažadona, D.; Buée, L.; de Silva, R.; Di Giovanni, G.; et al. Tau Protein Hyperphosphorylation and Aggregation in Alzheimer’s Disease and Other Tauopathies, and Possible Neuroprotective Strategies. Biomolecules 2016, 6, 6−28. (19) Schwalbe, M.; Kadavath, H.; Biernat, J.; Ozenne, V.; Blackledge, M.; Mandelkow, E.; Zweckstetter, M. Structural Impact of Tau Phosphorylation at Threonine 231. Structure 2015, 23, 1448−1458. (20) Hanger, D. P.; Anderton, B. H.; Noble, W. Tau Phosphorylation: The Therapeutic Challenge for Neurodegenerative Disease. Trends Mol. Med. 2009, 15, 112−119. (21) Buée, L.; Bussière, T.; Buée-Scherrer, V.; Delacourte, A.; Hof, P. R. Tau Protein Isoforms, Phosphorylation and Role in Neurodegenerative Disorders. Brain Res. Rev. 2000, 33, 95−130. (22) Drewes, G.; Lichtenberg-Kraag, B.; Döring, F.; Mandelkow, E. M.; Biernat, J.; Goris, J.; Dorée, M.; Mandelkow, E. Mitogen Activated Protein (MAP) Kinase Transforms Tau Protein into an AlzheimerLike State. EMBO J. 1992, 11, 2131−2138. (23) Hanger, D. P.; Hughes, K.; Woodgett, J. R.; Brion, J.-P.; Anderton, B. H. Glycogen Synthase Kinase-3 Induces Alzheimer’s Disease-Like Phosphorylation of Tau: Generation of Paired Helical Filament Epitopes and Neuronal Localisation of the Kinase. Neurosci. Lett. 1992, 147, 58−62. (24) Drewes, G.; Trinczek, B.; Illenberger, S.; Biernat, J.; SchmittUlms, G.; Meyer, H. E.; Mandelkow, E.-M.; Mandelkow, E. Microtubule-Associated Protein/Microtubule Affinity-Regulating Kin-

CONCLUSIONS In this study, we have tackled the problem of phosphorylation and how it correlates with the aggregation propensity of the MAP, tau. On the basis of our results, we have shown that tau aggregation proceeds through multiple stages characterized by molecular rearrangements. In the wild type, these rearrangements are extremely unlikely to take place, but phosphorylation, and its most common experimental counterpart pseudophosphorylation, may favor these transitions and lead to enhanced aggregation. We have also shown that, although pseudophosphorylation and phosphorylation may not be exactly equivalent when considering a single chain in solution, when more chains are considered the end product is essentially the same.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.7b00194. Details about simulation and equilibration; supplementary figures reporting the change in conformation over time (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: (856) 225-6267. Fax: (856) 225-6624. ORCID

Luca Larini: 0000-0003-1552-4264 Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Dr. Larini is supported by the start-up funds from Rutgers University-Camden. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) (grant no. MCB150005), which is supported by National Science Foundation (grant no. OCI-1053575). This study used the computational resources provided by the Center for Computational and Integrative Biology (CCIB) through the National Science Foundation (grant no. DBI-1126052). This research has been supported by RDI2 through its ELF/Caliburn early adopter program.

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REFERENCES

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