Initial Substrate Binding of γ-Secretase: The Role ... - ACS Publications

Feb 6, 2017 - that are all known to be essential for enzymatic activity. This ...... molecular simulations through multi-level parallelism from laptop...
1 downloads 0 Views 2MB Size
Subscriber access provided by UB + Fachbibliothek Chemie | (FU-Bibliothekssystem)

Article

Initial Substrate Binding of #-Secretase: The Role of Substrate Flexibility Shu Li, Wan Zhang, and Wei Han ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.6b00425 • Publication Date (Web): 06 Feb 2017 Downloaded from http://pubs.acs.org on February 8, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Chemical Neuroscience is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

Initial Substrate Binding of γ-Secretase: The Role of Substrate Flexibility Shu Li,† Wan Zhang†, and Wei Han*,† †

Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking

University Shenzhen Graduate School, Shenzhen, 518055, China

*Corresponding author: email: [email protected]; phone: +86-755-26032949

1 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: γ-Secretase cleaves transmembrane domains (TMD) of amyloid precursor protein (APP), producing pathologically relevant amyloid-β proteins. Initial substrate binding represents a key step of the γ-secretase cleavage whose mechanism remains elusive. Through long timescale coarse-grained and atomic simulations, we have found that the APP TMD can bind to the catalytic subunit presenilin 1 (PS1) on an extended surface covering PS1’s TMD2/6/9 and PAL motif that are all known to be essential for enzymatic activity. This initial substrate binding could lead to reduction in the vertical gap between APP’s ε-cleavage sites and γ-secretase’s active center, enhanced flexibility and hydration levels around the ε-sites and the presentation of these sites to the enzyme. There are heterogeneous substrate binding poses in which the substrate is found to bind to either the N- or C-terminal parts of PS1, or both. Moreover, we also find that the stability of the binding poses can be modulated by the flexibility of substrate TMD. Especially, the APP substrate, when deprived of bending fluctuation, does not bind to TMD9 at PS1’s C-terminus. Our simulations have revealed further that another substrate of γ-secretase, namely notch receptors, though bearing a rigid TMD, can still bind to PS1 TMD9, but by a different mechanism, suggesting that the influence of substrate flexibility is context-dependent. Together, these findings shed light on the mechanism of initial substrate docking of γ-secretase and the role of substrate flexibility in this process.

Keywords: γ-Secretase; amyloid precursor protein; amyloid-β peptide; intramembrane protease; substrate recognition; notch signal

2 ACS Paragon Plus Environment

Page 2 of 33

Page 3 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

Introduction γ-Secretase is an important protease that cleaves single transmembrane helical domains (TMD) of various integral membrane proteins, including in particular the amyloid precursor protein (APP).1 The cleavage of APP produces a mixture of 37 to 49 amino acid peptides known as amyloid β-proteins (Aβ) which are linked closely to Alzheimer’s disease (AD).2 γ-Secretase is comprised of four subunits, Pen-2, Aph-1, nicastrin and presenilin. This enzyme complex has altogether 19 transmembrane helical domains (TMD) embedded in the lipid bilayer membrane and an additional extracellular domain from nicastrin. Presenilin, containing nine TMDs, is the catalytic subunit of γ-secretase (Figure 1).3 To be functionally active, γ-secretase must execute endoproteolysis of the loop between TMD6/7 of its own presenilin. 4 Hence, presenilin in its functional form is actually a heterodimer of a N-terminal fragment (NTF) containing the first six TMDs of presenilin and a C-terminal fragment (CTF) containing the last three TMDs. As the catalytic dyad of presenilin consists of D257/385 of TMD6/7, the catalytic center is located at the interface between the NTF and CTF of presenilin and excluded from the external surface of the enzyme.

FIGURE 1. Overview of structure (PDB ID: 5FN2) of γ-secretase. The left and right panels show the views of the structure from its side and top, respectively. Nicastrin, Aph-1, Pen-2 and the CTF and NTF of PS1 are colored red, yellow, tan, blue and orange, respectively. α-helices, β-sheets and loops are shown as cylinders, ribbons and threads, respectively. In the side view, TMD2/6/9 of PS1 are numbered and highlighted with green dashed boxes. The PAL loop is also highlighted with cyan dashed boxes. The gray dashed lines indicate the boundaries of membrane. In the top view, the extracellular domain of nicastrin is omitted for clarity. All the TMDs of PS1 are numbered and two catalytic residues of PS1 are shown as green beads.

3 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

A central question regarding the cleavage mechanism is how a substrate could be recognized and recruited by γ-secretase which possesses such a complex architecture.3 The recognition and recruitment of a substrate has been shown to involve initial substrate binding to γ-secretase on sites distinct from theγ-secretase active sites5 and subsequent translocation of the substrate to the active site6,7 probably coupled with a large scale conformational change of the enzyme. 8,9 Important details of the translocation process have been revealed very recently in an insightful mutagenesis study by Selkoe and co-workers who showed that the substrate needs to occupy three amino acid-binding pockets in the active sites prior to catalysis.10 As the starting point of these complex processes of substrate recognition and recruitment, initial binding of a substrate to the external surface of γ-secretase is an important molecular event that could have profound impact on the enzymatic activity.11 Elucidating the mechanism of the initial substrate docking to γ-secretase should be valuable for the design of therapeutic interventions that modulate the production of Aβ.12 Previous biochemical experiments have provided information at the residue level of substrate docking to γ-secretase.6,7,13,14 It was suggested that several residues in presenilin 1 (PS1) were involved in initial substrate binding. These residues are scattered over TMD2/6 of the NTF and TMD9 of the CTF that are in close proximity in PS1, together forming an extended surface that is potentially responsible for initial substrate binding (Figure 1).13,15,16 Despite this finding, little is known about where and how exactly the substrate positions itself on the γ-secretase surface and how the positioning of the scissile bonds in the substrate could be affected by the substrate binding. Moreover, both experiments and atomic simulations revealed considerable bending flexibility of the APP TMD that has been proposed to be critical to enzymatic activity.17,18,19,20,21 However, it remains elusive how the substrate flexibility influences γ-secretase cleavage, particularly at the stage of substrate docking. Addressing these mechanistic questions relies on knowledge on structural details of substrate binding. Recent advances in cryogenic-electronic microscopy (cryo-EM) resulted in the very first high-resolution structures of γ-secretase, and provided vital information on the accessibility of the TMDs of the enzyme for initial substrate binding and on potential routes for substrate entry into the active site.22,23 Notwithstanding this, the structural details of γ-secretase in a complex with any substrate have yet to be elucidated. 4 ACS Paragon Plus Environment

Page 4 of 33

Page 5 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

In the present study, we investigate through multiscale molecular dynamics (MD) simulations the initial binding of substrates to γ-secretase and the role of substrate flexibility in this process. To sample enzyme-substrate interactions extensively, we employ the coarse-grained (CG) MARTINI force field24,25 that has been used to model accurately structures of the APP TMD in membrane and micelle, and dimerization of the APP substrates and substrate binding of other intramembrane proteases.19,26,27 In particular, the CG model is further developed here to model more realistically bending flexibility of substrate transmembrane helices. The resulting substrate binding poses are then examined through atomic MD simulations to reveal details of structural variations of bound substrates. With this approach, we explore how the APP binds to γ-secretase and how the binding is affected by change in the flexibility of the APP TMD. Moreover, a comparison between the binding poses of the APP and another notable γ-secretase substrate, namely notch receptors,28,29 has also been made. Together, these simulations generate important insights into the mechanism and the functional importance of initial substrate docking to γsecretase.

Results and Discussion APP can bind to γ-secretase on an extended surface composed of TMD2/6/9 and the PAL loop of Presenilin 1 Correctly modeling and accounting for the flexibility of the APP TMD is particularly important. In MARTINI, TMD flexibility is controlled by forces restraining consecutive backbone parts in helical conformations. Yet, default parameters (see Methods) for the forces are still not optimal, as demonstrated by difference between extents of bending fluctuation of the APP TMD captured by MARTINI simulations and by simulations using the all-atom CHARMM36 force field (Figure S1a).30,31 In response, we recalibrated the force parameters to reproduce the TMD flexibility based on atomic simulation results (see Methods and Figure S1a and S2). With the recalibrated parameters (Table S1), the binding of the APP TMD spanning residues 26-55 to the TMDs of γ-secretase in the 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine lipid bilayer was examined on a microsecond timescale CG simulations, starting with ~70 Å separation between the substrate and the enzyme. A total of 80 independent simulations (Table 5 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

S2) were performed to sample broadly possible binding modes. These simulations showed that the APP TMD can bind to PS1 as well as other γ-secretase subunits including nicastrin, Pen-2 and Aph-1 (Figure 2). The corresponding binding probabilities were estimated according to the centroid distribution of the bound substrate (see Methods and Figure S3). The probabilities for the APP TMD in contact with PS1, Pen-2 and Aph-1 are similar (~29-36%) while the chance for the APP TMD to bind to nicastrin is small (~2%). In general, these results agree with photoaffinity mapping experiments that demonstrated APP’s ability to bind not only to the catalytic domain PS1, but also to other subunits of γ-secretase complexes, including Pen2, nicastrin32 and Aph-1.33

FIGURE 2. Probability distribution of centroids of APP substrates in membrane plane obtained from the last 600 ns of CG simulations. The TMD structure (PDB ID: 5FN2) of γ-secretase, shown as cylinders which are overlaid with the distribution. Nicastrin, Aph-1, Pen-2 and the CTF and NTF of PS1 are colored red, yellow, tan, blue and orange, respectively. This color scheme will be applied to all the other figures in this paper. The TMDs of PS1 are numbered.

It has been shown that the substrate can be cleaved by the enzyme only when it is bound to PS1.32 To shed light on APP substrate recognition by PS1, we proceeded to examine probabilities of residue-residue contacts formed between PS1 and the APP TMD. As shown in Figure 3, in the NTF of PS1, specific contacts were found to arise often between region 125-138 and the Nterminal part of the APP TMD. This region was thought to belong to the first hydrophilic loop connecting TMD1/27 but was more recently shown in the high-resolution structures to be the Nterminal part of TMD2.22 Previous mutagenesis studies showed that mutations in helical region 129-132 impede the binding of substrate-mimicking helical peptide inhibitors and negate the enzymatic activity.7 Other substrate-docking sites in the NTF including TMD2/6 were also 6 ACS Paragon Plus Environment

Page 6 of 33

Page 7 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

identified by TMD-swapping mutagenesis experiments.13 Consistent with these experimental findings, significant involvement of other parts of TMD2 and TMD6 in interaction with the substrate was also observed in our simulations. Notably, we observed direct contacts between residues 260-270 of TMD6 and the C-terminal region 46-53 (in Aβ numbering) of the APP TMD, which appear to bring the ε-cleavage sites (positions 48-50) of the substrate where the cleavage by γ-secretase is initiated to proximity to the catalytic residue D257 of the enzyme. Finally, TMD3/4 also interact occasionally with the substrate but their significance in substrate recognition has not been reported.

FIGURE 3. Probability of residue-residue contacts between APP TMD and NTF (top) or CTF (bottom) of PS1. A contact between two residues occurs if the shortest distance between particles of the residues is shorter than 0.47 nm. The probability was averaged over the last 600 ns of CG simulations.

In the CTF of PS1, residues 443-451 of TMD9 were observed be in contact with residues 2638 of the substrate TMD, consistent with mutagenesis studies which showed that the C-terminal parts of TMD9 could serve as initial binding sites and that mutations in this region block the binding of peptide-based inhibitors that mimic the substrate.15 Moreover, there is a significant opportunity to observe contacts between the C-terminal part (residues 47-55) of the substrate harboring the ε-cleavage sites and region 428-433 overlapping with the highly conserved P433AL435 motif close to the catalytic center. This motif was suggested by previous experimental studies to play crucial roles as a subsite in substrate transfer.15,34,35 Our observation implies that the ε-cleavage sites of APP may already be anchored to PAL upon the initial substrate binding in

7 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

such a way that subsequent translocation of the substrate to the catalytic center could be facilitated. We investigated also the role of salt bridges in the interactions between the APP TMD and PS1. As the APP TMD has four lysine residues while PS1 has 15 negatively-charged residues, there could be 60 different types of intermolecular salt bridges. However, only a few of these were found to occur in more than 5% of PS1-substrate complexes (Figure 4). In particular, salt bridge interaction is most likely to arise between K28 from the APP TMD and D450 from PS1 TMD9. Although the probability of observing this salt bridge is moderate (~13%), our observation suggests that electrostatic interactions between APP and PS1 are specific. Notably, it has been demonstrated by mutagenesis studies that the PS1 D450 is critical for γ-secretase to initially recruit helical substrates7 while APP K28 appears essential for substrate positioning on PS1.36 Our finding that these two residues play particularly important roles in substrate-PS1 electrostatic interactions also agrees with these experimental discoveries.

FIGURE 4. Probability of salt bridge interactions between APP TMD and PS1. A salt bridge contact is considered to occur if positively charge CG beads of a residue is closer than 0.6 nm to negatively charged CG beads of another.

Considering the close proximity of TMD2/6/9 and the PAL loop, our results suggest that the APP TMD is capable of binding to an extended surface area (~4500 Å2) on PS1. An extended binding surface rather than a narrow site may enhance the chance of substrate recognition by PS1. Nonetheless, of 80 simulations of substrate binding only 17 ended up with the formation of stable substrate-enzyme complexes in which the substrate is in contact with the extended surface, whereas in the other simulations stable binding between the substrate and the other parts of the enzyme arose. This implies that due to the substrate binding outside of PS1, only one out of every five substrate-enzyme encounters could lead to a successful introduction of the substrate to this recognition surface. 8 ACS Paragon Plus Environment

Page 8 of 33

Page 9 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

Based on the cryo-EM structure of γ-secretase (Figure 1), it was also proposed that TMD1/5 of PS1 could harbor initial binding sites.32 We did not however observe the substrate binding to these TMDs. Given that the TMD1/5 are on the concave side of the γ-secretase complex, it is likely that the topology of the enzyme precludes substrate access. To rule out this possibility, we performed further simulations of substrate binding to an isolated PS1. In these simulations, the substrate was observed to bind with low probabilities to TMD1/5 (Figure S4). Together, our results suggest that the TMD1/5 sites in PS1 are inefficient in forming direct interactions with the APP substrate.

Initial docking of substrates to PS1’s recognition surface reduces but does not eliminate the vertical gap between ε-cleavage sites of substrate and the active center of γ-secretase A prerequisite for the proteolytic reaction is the translocation of the cleavage sites to the active center of PS1. It has been suggested that after its initial docking to γ-secretase, the substrate could move laterally in the enzyme bringing these cleavage sites to the active center.6,16 Hence, upon the initial binding, a small difference between the cleavage sites and the active center at their vertical levels in the membrane could ease the translocation of the cleavage sites.37,38 In this context, we examined the vertical distance between the active center of PS1 and the ε-cleavage sites in APP where γ-secretase starts to process APP. In particular, we investigated two scissile amide bonds in the ε-cleavage sites, one located between T48 and L49 and the other between L49 and V50. The cleavage of these two scissile bonds triggers sequential cleavages of APP that lead eventually to the production of Aβ42 and Aβ40, respectively. Figure 5 shows the positional distributions of the T48-L49 and L49-V50 scissile bonds of APP and the active center of PS1 along a normal to the membrane plane before and after the substrate binds to the recognition surface of PS1. The average vertical distances between the two scissile bonds of free APP and the active center were calculated to be approximately 7.0 Å and 8.5 Å. When the substrate interacts with the recognition surface, the vertical distances between both scissile bonds and the active center decrease by approximately 1.5 Å, indicating that the interactions between the substrate and the recognition surface can indeed bring the ε-cleavage sites closer to the active center in the vertical direction. There still exists on average a 5-7 Å gap 9 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 33

between the vertical positions of the ε-cleavage sites and the active center in the binding complexes, and in addition, the horizontal separation between the sites and the active center is about 10-15 Å. Thus, after the initial binding of the substrate the ε-cleavage sites may still have to move both vertically and horizontally eventually to reach the active center.

FIGURE 5. Distributions of vertical positions of the T48-L49 (red curves) and L49-V50 (blue curves) scissile bonds of APP and the active center of PS1 (black curves). Shown are (a) the distributions for a free APP and unbound γ-secretase; (b) the distributions from binding complexes in which the APP substrate interacts with the recognition surface of PS1. The centroids of backbones of T48 and L49 were used to represent the T48-L49 bond and the centroids of backbones of L49 and V50 were used to represent the L49-V50 bond. The centroid of D257/358 of PS1 was used to represent the active center. Z-Cartesian coordinates, as indicated by the insets, were used to represent vertical positions. The centroid of POPC bilayer membrane was chosen as the origin of the system. All the distributions were calculated in terms of relative positions (∆z) with respect to where the distributions of the active center are maximized. Red and blue dashed lines denote, respectively, the average vertical positions of the T48-L49 and L49-V50 bonds of the free APP with respect to that of the active center of unbound γ-secretase.

It has been suggested that the translocation of the substrate to the active center is coupled with significant structural change of γ -secretase because according to available high-resolution structures of unbound γ-secretase, the active center is occluded from access of the substrate.22 This postulate also finds support in numerous biochemical6,7 and structural studies.8,9 In the present study, we simulated the very initial process of the binding of the substrate to γ-secretase in a substrate-binding mimicking conformation that is available experimentally.22 As the CG 10 ACS Paragon Plus Environment

Page 11 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

model employed here by MARTINI can only allow proteins to undergo very limited fluctuation around chosen conformations,25 we are unable to provide details of the structural change of γsecretase or the movement of the substrate during the translocation process. Despite this limitation, the analyses conducted here imply that it could still be necessary to eliminate further the vertical gap observed between the cleavage sites and the active center even after the initial substrate binding. This might be achieved by, in addition to the lateral movement, other kinds of substrate motions, such as the bending of the substrate TMD in its center as has been proposed previously.6,17,18

APP docks to PS1’s NTF and CTF in multiple poses that permit presentation of ε-cleavage sites to γ-secretase In view of the large area of the recognition surface on PS1, we wondered how exactly the APP TMD interacts with this surface. Clustering analysis was performed to obtain representative docking poses of the APP substrate in contact with the protein surface. Using a root mean square distance (RMSD) cutoff of 0.35 nm, the analysis identified 46 distinct representative binding poses (Figure 6). Inspection of the binding poses reveals that the bound substrate is usually positioned either on the N-terminal fragment side (~54%) or on the C-terminal fragment side of the recognition surface (~30%). On the CTF side, the substrate binds simultaneously to the surface portion composed of TMD9 and the PAL-containing loop of the CTF; on the NTF side, it interacts with the cleft between TMD2 and TMD6 of the NTF. In some (~16%) of the cases the substrate was also found to form contacts with both sides of the recognition surface, either embedding itself in the cleft between the NTF and the CTF, or locating itself on an extended surface area across TMD2/6 and the PAL loop (Figure S5). Together, our results suggest the existence of multiple major binding modes during the initial stage of substrate docking.

11 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 33

FIGURE 6. Observed representative binding poses of APP substrate. All substrate poses are overlaid. The poses are colored dark gray, gray and white when the substrate binds to the PS1 NTF, the PS1 CTF, and both, respectively. Nicastrin, Aph-1, Pen-2 and the CTF and NTF of PS1 are colored red, yellow, tan, blue and orange, respectively. The TMDs of the PS1 NTF and CTF are numbered.

To evaluate further the potential relevance of the observed binding poses to the function of γsecretase, we investigated the positioning of the ε -cleavage sites for subsequent substrate translocation and cleavage in these poses. It is of particular interest to learn whether the faces of the APP transmembrane helix in which the T48-L49 and L49-V50 scissile bonds reside interact with γ-secretase upon the initial docking of the substrate (Figure 7a). It has been suggested by recent mutagenesis studies that this interaction between the substrate and the enzyme dictates where the ε-cleavage occurs.39 We considered here the TMD’s face harboring a scissile bond may interact with γ-secretase if both neighboring residues upstream and downstream of this bond interact with the enzyme (Figure 7a). As shown in Figures 7b-d, in nearly half of the binding poses there is a significant chance (>50 %) of observing interaction of γ-secretase with either of the T48-L49 and L49-V50 faces of the APP TMD. In the NTF-binding poses, the substrate is more likely to interact with the enzyme through its T48-L49 face than through its L49-V50 face. In the CTF-binding poses on the other hand, the T49-L50 face of the TMD seems more likely to be part of the binding interface. The overall chances to observe γ-secretase interacting with the T48-L49 or L49-V50 face are about 30% and 23%, respectively. This result implies that upon the initial docking of the APP substrate, the T48-L49 and L49-V50 scissile bonds are equally likely to be presented to the enzyme for cleavage. This is in accord with the experimental finding that γ-secretase normally generates roughly the same amount of products of cleavage at the T48-L49 or the T49-L50 sites of the APP substrate.10,38 12 ACS Paragon Plus Environment

Page 13 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

FIGURE 7. Binding of T48-L49 and L49-V50 scissile bonds at ε-cleavage sites of γ-secretase upon initial substrate docking to the recognition surface of PS1. (a) Scheme of scissile bonds of APP and their binding to enzyme. Shown on the left are the side and top views of the APP TMD represented as helical ribbons. The atoms of the side chains of T48, L49 and V50 are shown as spheres. Oxygen, carbon and hydrogen atoms are shown in red, ice blue and white, respectively. Blue and red arrows indicate the locations of the T48-L49 and L49-V50 bonds, respectively. Shown on the right are the situations in which either of the scissile bonds binds to the enzyme. A given bond is thought to bind to the enzyme if neighboring residues both upstream and downstream of this bond are in contact with the enzyme. (b)-(d) The probabilities of observing the binding of either the T48-L49 bond (blue bars) or the L49-V50 bond (red bars) to the enzyme in the binding poses in which the substrate interacts with either the PS1 CTF (b), or the PS1 NTF (d), or both (c).

The binding poses sampled in this study seem to indicate that the APP substrate may be able to bind independently to the CTF and NTF of PS1 at the stage of initial substrate docking. This finding agrees with early seminal work by Annaert et al who showed through GST-pulldown experiments that APP can bind to the PS1 NTF and CTF separately. 40 In addition, a recent photoaffinity mapping experiment showed that both the NTF and CTF can be cross-linked separately with the same residues of the APP, suggesting the coexistence of different binding modes for these two PS1 fragments.32 13 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 33

On the other hand, several biochemical studies, including the early GST-pulldown study discussed above suggest that the substrate could bind jointly to distinct fragments of PS1.6,7,13,40 In particular, mutagenesis studies showed that the N-terminal parts of TMD2 and TMD9 could form binding sites which could recruit the substrate cooperatively.7 However, none of the binding poses sampled here permit the substrate to interact with the two APP binding regions at the same time. This is attributed to the large separation between these two regions in unbound γ-secretase as seen in the cryo-EM structure.22 Hence, a conformational rearrangement of γ-secretase is required to bring the two APP binding regions into close proximity and may be triggered by the initial substrate docking. Indeed, as revealed by low-resolution cryo-EM studies, significant changes in the structures of γ-secretase were observed upon its interaction with substratemimicking inhibitors.8 Nevertheless, it remains an open question as to which of the poses observed for the substrate binding could induce this structural change.

Binding of substrate to PS1’s recognition surface enhances flexibility and hydration levels of ε-cleavage sites In addition to the positioning of the cleavage sites, the local stability and hydration levels of the TMD around these sites could also be important factors that affect the catalytic activity.18,19 Destabilizing local helices may help to liberate scissile bonds from helical hydrogen bonding (HB) for cleavage;39 increased water accessibility at the cleavage sites may favor the hydrolysis at these positions and may also render local helices more flexible.18,38 In this regard, we sought to understand how the docking of the substrate to the recognition surface of PS1 could affect local stability and hydration levels around the ε-cleavage sites. To this end, all-atom simulations of binding complexes were performed using representative binding poses obtained from the CG simulations. For comparison, a free APP substrate in membrane was also simulated (Table S2). These atomic simulations permit accurate modeling of formation and breakage of hydrogen bonds and hydration of proteins. The stability of local helices during the simulations was monitored using occupancy of backbone hydrogen bonds formed between carbonyl oxygen of a given residue and amide groups of the third and fourth downstream residues (see the caption of Figure 8). As shown in Figure 8a, the occupancy of helical HBs involving the carbonyl oxygen atoms of I47-V50 decreases 14 ACS Paragon Plus Environment

Page 15 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

when the substrate binds to the recognition surface. In particular, the HB occupancies of the two ε-cleavage sites, namely the carbonyl oxygens of T48 and L49, are reduced by one fifth and one half, respectively. It should be noted that the reduction in the HB occupancy was observed for all the three major types of binding poses. Consistent with reduced stability of the substrate TMD around the ε-cleavage sites, our hydration analysis (see the Figure 8 caption) also revealed a significant increase in hydration levels at these sites and several upstream residues when the substrate docks on the recognition surface (Figure 8b). In particular, the average counts of water molecules within 0.35 nm of carbonyl oxygen of T48 and L49 are increased by as much as 6-10 fold in the binding complexes.

FIGURE 8. Enhanced flexibility and hydration levels around ε-cleavage sites upon initial substrate docking to γsecretase. (a) Occupancy of helical HBs involving carbonyl groups of residues around the cleavage sites. A hydrogen bond between a donor and an acceptor is thought to occur if the distance between the donor and the acceptor is < 0.35 nm and the donor-hydrogen-acceptor angle is > 120o. The carbonyl group of residue i is thought to be involved in a helical HB if this carbonyl group forms an HB with the amide group of residue i+3 or i+4. (b) Number of hydration water molecules surrounding carbonyl groups in V40-L49. A water molecule is thought to hydrate a carbonyl group if its oxygen atom is closer than 0.35 nm to that of the carbonyl group. Blue bars show the results for unbound APP calculated according to three independent 100-ns atomic simulations. Orange, gray and yellow bars show the results for APP in contact with either or both the PS1 CTF, or the PS1 NTF. In each case, the results were obtained using the first two most populated binding poses from the CG simulations, each pose being examined through three independent 100-ns atomic simulations. The error bars were estimated according to these independent simulations.

15 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 33

Interestingly, it has been revealed by previous amide exchange experiments and computational modeling that the homodimerization of the APP TMD enhances the conformational dynamics and hydration levels at the C-terminal TMD of APP.18 The importance of APP dimerization for the catalytic process has been proposed on the basis of this finding. Our current results demonstrate that a similar outcome could also emerge from the direct interaction between the substrate and the recognition surface of PS1 upon the initial docking of the substrate. As a result, the ε-cleavage sites could be rendered more flexible and more accessible to water molecules prior to the translocation and cleavage of the substrate. Taken together, previous studies and ours suggest the vital role of the interaction of the APP substrate with other partners in facilitating its cleavage by γ-secretase.

Suppression of the flexibility of APP substrates disfavors binding between substrate and PS1’s CTF and affects positioning of ε-cleavage sites Next, we examined how the flexibility of the APP substrate affects its recognition by γsecretase. Additional simulations of substrate binding were performed employing the parameters calibrated to render the APP TMD as rigid as polyleucine, one of the most rigid TMD sequences known to date (see Methods and Figure S1b). 41 The simulation results in general reveal no obvious change in patterns of residue-residue contacts formed between the substrate TMD and the four subunits of γ-secretase (Figure S6) but the details of residue-residue contacts between the substrate and the recognition surface of PS1 are indeed affected by reduction in flexibility of the substrate TMD (Figure 9a). In particular, the interactions between the APP TMD and TMD9 of the CTF disappeared in these simulations.

16 ACS Paragon Plus Environment

Page 17 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

FIGURE 9. Probability of residue-residue contacts formed between PS1 CTF and TMDs of flexibility-deprived APP (a), G37L/G38L APP mutant (b) and notch (c).

In an effort to understand how the PS1-substrate interactions are affected by the change of the substrate flexibility, we compared binding poses of the substrate in contact with the recognition surface of PS1 before and after the flexibility of the substrate TMD was reduced. For the substrate with reduced flexibility, the majority (~70 %) of sampled binding poses are structurally not different from the representative poses of the flexible substrate by more than a RMSD of 0.35 nm. This indicates that the suppression of flexibility of the substrate results in virtually no new binding poses. Instead, the reduction in flexibility prevents the substrate from binding to PS1 CTF, rebalancing statistical weights between the binding poses where the substrate binds at the interface between the CTF and NTF of PS1 or on its NTF (Figures 10 and 11a). Especially, removal of TMD flexibility does not affect stability of those stable NTF-binding poses observed for a flexible APP. Our results suggest that the flexibility of the APP TMD is indeed able to affect the substrate recognition by modulating conformational equilibrium between individual substrate binding poses. Reducing this flexibility can shift the equilibrium in favor of APP binding to PS1 NTF.

17 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 33

Figure 10. Effect of flexibility of APP TMD on relative abundance of various substrate binding poses on PS1. Shown as blue bars are the probabilities of all the observed representative binding poses of the APP TMD obtained from clustering analysis. These binding poses are sorted by their probabilities and grouped according to whether the substrate binds to the CTF (a) or the NTF (c) of PS1, or both (b). Shown as orange bars are the probabilities of observing these representative binding poses arising also in the simulations of the binding between PS1 and a flexibility-deprived APP TMD. These probabilities were calculated by examining structural similarity between each representative binding pose and all the sampled binding poses of the flexibility-deprived APP TMD. The structural similarity is measured by calculating the RMSD based on the coordinates of Cα atoms of PS1’s TMD2/6/9 and PAL loop and those of the substrate TMD. Two poses are thought to be similar if their RMSD is smaller than 0.35 nm.

18 ACS Paragon Plus Environment

Page 19 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

FIGURE 11. Observed representative binding poses of flexibility-deprived APP substrate (a) and its G37L/G38L mutant (b) and notch (c) substrate on PS1 of γ-secretase. In each case, all substrate poses are overlaid. The poses are colored dark gray, gray and white when the substrate binds to the PS1 NTF, the PS1 CTF, and both, respectively. Nicastrin, Aph-1, Pen-2 and the CTF and NTF of PS1 are colored red, yellow, tan, blue and orange, respectively. The TMDs of the PS1 NTF and CTF are numbered. The PS1 TMD9 is highlighted in cyan.

The results discussed above imply that a bent APP substrate may be needed for its recognition by PS1’s CTF but not by its NTF. To confirm this, we conducted multiple 100 ns timescale atomic simulations starting with the most populated poses of substrate binding either to the CTF or to the NTF. These atomic simulations permit a more accurate description of interactions between the substrate and the enzyme as well as conformational variation of the bound substrate. The simulations of the CTF-binding poses showed that the substrate TMD exhibit large local bending curvatures that are up to ~10o larger than that of the unbound substrate (Figure 12). The residues with large curvatures reside mainly in the central part of the APP TMD, indicating that the bound substrate is bent in its center (Figure S7). Conversely, we did not observe any 19 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 33

significant increase in the curvature of the APP TMD for the NTF-binding poses (Figures 12 and S7). Together, these results suggest that a flexible TMD allows APP to access bent conformations that can accommodate interactions with the PS1 CTF whereas a bent TMD is not required for the substrate to bind to the NTF.

FIGURE 12. Local bending curvatures of APP TMD observed during all-atom simulations starting from representative binding poses. The curvature results in the most populated poses in which the APP TMD binds to the CTF or the NTF of PS1 are shown in dark red/red and light blue/blue, respectively. The curvatures of isolated APP is shown in black. All the results were estimated using Bendix42 as an average over the last 50 ns of the simulations. The standard errors were calculated according to three independent simulations.

We proceeded to examine if a shift in equilibrium between poses affects the positioning of the ε-cleavage sites. Our analysis revealed that the average vertical distances between the sites and the active center are approximately 6-8 Å, slightly larger than observed for a normal APP substrate (approximately 5-7 Å). This indicates that the vertical gaps separating the cleavage sites and the active center in substrate-enzyme complexes do not change significantly as a result of the loss of the flexibility of the substrate. But, when compared to a normal APP substrate, the flexibility-deprived substrate exhibits an increased chance of interacting with PS1 using its T48L49 bond (~36% vs. ~30%) but becomes less likely to interact with PS1 using its L49-V50 bond (~15% vs. ~23%). This result is consistent with our finding that the CTF tends to interact with the L49-V50 bond while the NTF tends to interact with the T48-L49 bond (Figures 7b-d). To gain further insight into the importance of the flexibility of the APP substrate, we also investigated the initial binding between γ-secretase and an APP mutant in which G37G38 was 20 ACS Paragon Plus Environment

Page 21 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

replaced with two leucine residues. This mutant has been shown previously by NMR studies to exhibit greatly reduced flexibility in its hinge region.17 Indeed, our atomic simulation revealed that the mutation leads to significant reduction of flexibility in positions 37 and 38 (Figure S1c). In accordance with our expectations, our CG binding simulations showed that the CTF binding poses disappear (Figure 11b) and the mutated substrate has much lower chance of interacting with the PS1 TMD9 than does the wild type (WT) substrate (Figures 3 and 9b). Our analysis of the positioning of the cleavage sites revealed that the L49-V50 bond in the mutant is less likely to interact with PS1 than it does in the WT APP (~17% vs. ~23%), which is in accord with the results for the flexibility-deprived APP substrate. However, unlike the flexibility-deprived APP whose T48-L49 bond has an increased chance of interacting with PS1 (~36% vs. ~30%), the same bond in the mutant is considerably less likely to interact with PS1 (~19% vs. ~30%). This result indicates that factors besides the flexibility of the substrate also participate in the substrate-enzyme interactions. For instance, it is likely that the two large hydrophobic side chains introduced could alter the relative tendencies of different faces of the substrate TMD to be involved in the binding interface. Altogether, our results suggests that the G37L/G38L mutation could affect the APP’s ability to interact with different parts of PS1 as well as the positioning of the cleavage sites at the stage of the initial substrate docking. It would be interesting to investigate how the APP mutant could bind to the CTF and NTF of PS1 through similar GST-pulldown experiments as was done previously for the WT substrate40 and whether the relative abundances of the T48-L49 and L49-V50 cleavage products could be altered by the mutations. As proposed in previous studies, the flexibly curved nature of the APP TMD may facilitate the interaction between the APP substrates and γ-secretase via APP dimerization,19 translocation of substrates to the catalytic site,17 or exposure of cleavage sites of substrates for the proteolytic process.18 In particular, based on a medium-resolution cryo-EM structure of γ-secretase,43,44 it has been suggested that a substrate with a curved TMD may be well suited to interactions with a curved lumen harboring the catalytic site within γ-secretase.17 Our simulations of substrate binding using a high-resolution structure22 revealed that the flexibility of the APP TMD could also be essential for initial substrate binding. The ability of substrates to bind to different parts of PS1 may be modulated by its flexibility, and this in turn may have an impact on the positioning 21 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 33

of the cleavage sites. This finding provides another layer of crucial detail for an understanding of the importance of substrate flexibility for the activity of γ-secretase.

Notch substrate possesses rigid TMD but still can bind to PS1’s CTF by a different mechanism Another notable γ-secretase substrate namely notch receptors29 contains a TMD which shares little sequence identify with the APP TMD, and is devoid of the glycine residues that are enriched in the N-terminus of the APP TMD, responsible for flexibility of the TMD (Scheme 1).17,18,19 Indeed, we found through atomic simulations that the notch TMD is almost as rigid as polyleucine and unlikely to bend (Figure S1d). This raises the question of whether the mechanism of the notch recognition is analogous to that of the recognition of the flexibilitydeprived APP substrate and its G37L/G38L variant.

SCHEME 1. Sequences of TMDs of APP and notch.

To this end, CG simulations were conducted to investigate the binding of the notch TMD to γsecretase, using parameters calibrated to reproduce atomic simulation results regarding the flexibility of the notch TMD. The results of these simulations show that the notch TMD is able to interact with PS1, Pen-2, Aph-1 and nicastrin. The corresponding patterns of residue-residue contacts between the notch and these subunits are very similar to those observed for the APP substrate (Figure S6), implying that the two substrates share binding sites on γ-secretase. Indeed, competitive binding experiments showed that notch-based inhibitors compete with APP for all subunits of γ-secretase.32 Unlike the flexibility-deprived APP substrate or the G37L/G38L variant, the notch substrate, though also bearing a rather rigid TMD, is still capable of interacting with the CTF of PS1, particularly with its TMD9 (Figure 9c). The clustering analysis also revealed multiple representative substrate binding poses in which the notch is in contact not only with the NTF but also with the CTF (Figure 11c). In fact, the CTF binding poses account for ~28% of all the observed binding poses. This result is in accord with the experimental finding that the PS1 NTF as well as the PS1 CTF can be co-immunoprecipitated with the notch substrate.45 We examined 22 ACS Paragon Plus Environment

Page 23 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

further the positioning of the S3-cleavage site of the notch between V1744 and L1745. This site is equivalent to the ε-cleavage sites of APP. There is a ~33% chance to observe the binding between PS1 and the S3-cleavage site. Interestingly, over 90% of the observed binding between this site and the enzyme requires the PS1 CTF to be at least part of binding interface between the notch substrate and PS1. The interaction of the notch substrate with the PS1 CTF appears essential for the positioning of the S3-cleavage site. Detailed inspection of the CTF-binding poses revealed further that in these poses, the Cterminus of the notch binds tightly to the PAL loop while its N-terminus tilts away from the CTF of PS1 (Figure 13), leading to a slight separation between the backbone of this terminus and the PS1 TMD9. Nonetheless, the large side chains of M1727 and F1734 at the N-terminal part of the notch can still form contacts with the PS1 TMD9. This structural feature was further validated through our atomic simulations (Figure S8). On the other hand, as the N-terminus of the APP substrate is rich in glycine and alanine residues, the backbone of this region must be in close proximity to the PS1 TMD9 to interact with the PS1 CTF (Figure 13). Consequently, the APP TMD needs to be curved to enable at the same time its interactions with the TMD9 and with the PAL loop. Together, these results suggest that although both the APP and notch share almost the same binding sites on PS1, the underlying binding mechanisms can vary, depending on flexibility and sequences of their TMDs.

FIGURE 13. Close-up views of binding poses of APP (right) and the notch (left) TMDs complexed with PS1 CTF. Shown as red and yellow ellipsoids are nicastrin’s TMD and Aph-1. Orange and cyan cylinders denote the PS1 CTF. Blue circles highlight the proline of the PAL motif. Pen2 is not shown for clarity. All binding poses are shown as gray helices and overlaid. In the left panel, green and pink ellipsoids represent M1727 and F1734 of notch, respectively. In the right panel, green and pink ellipsoids represent G30/38 and A31/42 of APP, respectively.

23 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 33

Conclusion In summary, the CG and atomic simulations conducted in the present study permit a first glimpse of the structural details of initial binding of substrates to γ-secretase. The simulations reveal that APP binds to an extended surface of PS1 in diverse modes whose stabilities are modulated by the flexibility of the transmembrane domains of the substrate. The initial substrate binding has an impact on the positioning of the ε-cleavage sites as well as on their flexibility and hydration levels. Moreover, our computational study reveals further differences between recognition mechanisms for the APP and notch substrates which are an outcome of distinct substrate flexibility and sequences. This might have implications in specific inhibition of the APP cleavage. In fact, there have been several notch-sparing γ-secretase modulators that selectively inhibit the activity of γ-secretase toward Aβ production.46,47 In particular, the activity of some of these modulators are known to require the presence of the APP substrate in γ-secretase.48 Although our current CG approach is unable to accurately model interactions between these modulators and the enzyme, the results from the present study provide possible structural models of substrate-enzyme complexes for further study using more sophisticated modeling methods of the mechanism of action of the modulators. It should be noted that γ-secretase also exhibits considerable conformational plasticity.8,9,22 Since the γ-secretase structure investigated here, which represents a substrate-binding mimic state, already has a complex means of substrate recognition, it would be intriguing to know how the enzyme in other conformational states would recognize its substrates. Future studies of this question will impact our understanding of the mechanism of APP cleavage by γ-secretase.

Methods Model construction A cryo-EM structure at a resolution of 4.2 Å (PDB ID: 5FN2) was used as the initial model for MD simulations of γ-secretase. This structure comprises all four subunits of γsecretase, namely Aph-1, Pen-2, PS1 and nicastrin, complexed with an active state inhibitor that was used to lock γ-secretase into a substrate-binding mimic conformational state.22 In the present study, we retained all transmembrane parts of γ-secretase. It is known that the TMDs of γ24 ACS Paragon Plus Environment

Page 25 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

secretase are responsible for substrate binding.49 The extracellular domain (residues 1-664) of nicastrin, on the other hand, was removed as it was shown to be dispensable from substrate binding and cleavage.49 In addition to γ-secretase, we created also helical constructs of two 30residue

substrates

to

represent

APP

TMD

(sequence

SNKGAIIGLMV-

GGVVIATVIVITLVMLKKK), notch TMD (PSQLHLMYVAAAAFVLLFFVGCGVLLSRKR) and a 30-residue polyleucine peptide (sequence KKKWLLLLLLLLLLLLLLLLLLLLLLLKKK) to represent rigid TMDs. All these constructs were built in straight α-helical conformations using SwissModel.50 Simulation setup In the present study, both CG and all-atom simulations were conducted using the GROMACS package.51 CG simulations were used to examine processes of substrate binding to γsecretase and to derive information about binding poses. Atomic simulations were used to provide more accurate description of the flexibly curved nature of the substrate when it is bound to the enzyme or remains isolated. All these simulations are summarized in Table S2. In the CG simulations, all the initial atomic models obtained as described above were converted into MARTINI representation using the CHARMM-GUI MARTINI Maker. 52 The initial models for these simulations were constructed as follows: for isolated substrate systems, the substrate TMD was embedded in a 50 Å × 50 Å bilayer membrane composed of about 69 CG POPC lipid molecules; for systems contains both γ-secretase and a substrate, the enzyme was oriented using the Orientation of Proteins in Membrane (OPM) program 53 and placed into a larger lipid bilayer (125 Å × 125 Å) that allow us to randomly place the substrate ~70 Å away from the enzyme. All the systems were solvated in MARTINI CG water boxes, buffered with NaCl at 150 mM concentration. The MARTINI 2.2 parameters24 were employed for the CG simulations with modifications needed to reproduce helical flexibility. These modifications will be discussed in the sections below. All the CG systems were subject to a 5000-step energy minimization followed by a 2-ns NPT pre-equilibration simulation with positions of protein beads being restrained. In CG production runs, the temperature was set to 323K using the V-rescale thermostat and the pressure was set to 1 bar using a semi-isotropic coupling method. A time step 25 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 33

of 20 fs was used, a typical value employed in MARTINI simulations. Each simulation ran for one µs, the last 600 ns of which was used for analysis. We carried out also all-atom simulations to examine the flexibility of the TMD of an isolated substrate and structural variation of the substrates complexed with the enzyme (Table S2). The CHARMM-GUI Membrane Builder54 was used to construct initial models for the all atom simulations. Atomic POPC lipid bilayers with dimensions of 50 Å × 50 Å and 105 Å × 105 Å were constructed for systems of isolated substrates and binding complexes, respectively. Again, all the systems were solvated in 150 mM NaCl solution. In all-atom simulations, proteins, lipids and ions were modeled with the CHARMM36 force field while water was modeled with the TIP3P water model.55 Nonbonded interactions were truncated beyond 12 Å. Long range electrostatic interactions were described with the Particle Mesh Ewald method (PME).56 The all-atom simulations were conducted with a 2 fs time step at 310 K using the Nose-Hoover coupling algorithm and at 1 bar using the semi-isotropic coupling method. All the all-atom systems were pre-equilibrated following the same procedure employed for the CG systems. For each system examined, three independent simulations were performed, each starting with different atomic velocities. Each simulation was simulated for 100 ns and its second half was used for analysis. Clustering analysis of binding poses of substrate In the present study, we employed two approaches to cluster binding poses of a substrate sampled from binding simulations. At a coarse level, we grouped the binding poses to examine which of the subunits in γ-secretase the substrate interacts with. For this purpose, all the sampled structures were first aligned with respect to the TMDs of the experimental structure of γsecretase. Then we calculated the probability map of the xy coordinates of the substrate centroid. This map describes where the bound substrate is likely to be located around γ-secretase. The select regions in the map, as highlighted in shade (Figure S3a), were employed to discriminate between the poses of the substrate binding to different subunits. Following the same idea, we obtained also a group of binding poses that represent the binding of the substrate to PS1 on its recognition surface that are made of its TMD2/6/9 and PAL loop (Figure S3a). To further understand how the substrate interacts with the recognition surface 26 ACS Paragon Plus Environment

Page 27 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

of PS1, we clustered the select binding poses using the program g_cluster contained in the GROMACS package. An RMSD-based algorithm proposed by Daura et al was employed.57 In the present study, the RMSD between structures was calculated based on the coordinates of Cα atoms of PS1’s TMD2/6/9 and PAL loop and those of the substrate TMD. A RMSD cutoff of 0.35 nm was used for the clustering analysis. Calculation of bending curvature of TMD The Bendix program was used to calculate local bending curvatures of helices.41 In Bendix, local helical axes of a helix are first generated based on coordinates of several consecutive residues and are then jointed by a spline. Local curvatures along the helix are interpolated according to the resulting spline. 58 In the present study, we followed the default setup of Bendix and used every four consecutive residues to determine local helical axes. Modification of MARTINI to model bending fluctuation of substrate TMDs A central task of the present study is to model bending fluctuation of TMDs of substrates. The all-atom CHARMM36 force field have proved to be competent for this task. Using this force field, previous simulation studies18,19,59 have reproduced the conformational fluctuation of the TMD of several APP segments that have been carefully measured in membrane and micelle environments by NMR chemical shift and amide exchange experiments.17,18 The APP TMD model proposed by these studies comprises two local helical segments jointed by a central hinge, composed of G37/38, which gives rise to bending fluctuation of the TMD. In addition, the local helical segment at the N-terminus undergoes more fluctuation than does the one at the Cterminus. Indeed, our CHARMM36 simulations of the APP TMD in a POPC bilayer yield a local bending curvature profile that agrees also with these proposed key structural features of the TMD (Figure S1a). In the present study, the CG MARTINI force field was used to sample extensively processes of the binding of the APP Substrate to γ-secretase, computationally a daunting task that cannot be achieved by any all atom models. In MARTINI, helical structures are maintained during simulations through a restraint potential expressed as follows: ‫ܧ‬୦ୣ୪ = ݇୦ୣ୪ ሾ1 + cosሺߠ − ߠ଴ ሻሿ, 27 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 33

where θ is a backbone helical dihedral angle, defined as the dihedral angle of four consecutive backbone CG beads that need to be confined in a helical conformation, and θ0 is set to -120o in MARTINI. The strength of the force constant khel determines effectively the extent of fluctuation around the helical conformation. By default, khel is the same for all backbone helical dihedral angles, taking a value of 400 kJ/mol/rad2. With these parameters, however, the simulation yielded a curvature profile of the TMD that does not match with that obtained using the CHARMM36 force field (Figure S1a). The RMSD between the two profiles are 0.75 deg. The major deviations come from the N-terminus and the central hinge of the TMD, the fluctuation of which is underestimated by the MARTINI simulations. In response, we assigned different force constants to the restraint forces on individual backbone helical dihedral angles. These parameters were adjusted manually to match the curvature profile obtained from the all-atom simulation. Not only can the resulting parameters (Table S1) yield a bending curvature profile with a much reduced RMSD (0.36 deg) to the profile derived from the all-atom simulation, but they can also reproduce much better the distributions of local bending curvatures throughout almost the entire TMD of APP (Figure S2a). To model a flexibility-deprived APP TMD, we also adjusted khel to make the APP TMD as rigid as the polyleucine peptide. It turned out that the bending curvature of the polyleucine from all-atom simulations can be reproduced excellently if a khel value of 800 kJ/mol/rad2 is used for all backbone helical dihedral angles (Figures S1b and S2b). Similarly, we also found that it is enough to use a khel value of 600 kJ/mol/rad2 to model correctly the bending fluctuation of the notch TMD (Figures S1d and S2d).

ASSOCIATED CONTENT Supporting Information Table S1, recalibrated MARTINI parameters; Table S2, summary of simulation systems; Figures S1 and S2, comparison of bending curvature results calculated from CG and atomistic simulations; Figure S3, probability distributions of substrates around γ-secretase; Figure S4, probability distribution of APP substrate around isolated PS1; Figure S5, representative binding 28 ACS Paragon Plus Environment

Page 29 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

modes of APP substrate; Figure S6, residue-residue contact maps between γ-secretase and all substrates investigated; Figure S7, structures of representative binding modes of APP after allatom simulations; Figure S8, bending curvature profiles of notch substrate calculated according to all-atom simulations. This material is available free of charge via the Internet at http://pubs.acs.org.” AUTHOR INFORMATION Corresponding Author [email protected] Author Contributions S.L. performed the simulations; S. L., W. Z. and W. H. analyzed the data; W. H. conceived and designed the research; W. H. wrote the paper. Notes The authors declare no competing financial interests. ACKNOWLEDGMENT We thank financial supports from the National Science Foundation of China (21673013), the Shenzhen STIC (KQTD2015032709315529, JCYJ20160330095839867) and the MOST of China (2013CB911501). Computer time was provided through Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).

29 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 33

REFERENCES (1) Haass, C., Selkoe, D. J. (1993) Cellular processing of beta-amyloid precursor protein and the genesis of amyloid beta-peptide. Cell 75, 1039-1042. (2) Hardy, J. A., Higgins, G. A. (1992) Alzheimer's disease: the amyloid cascade hypothesis. Science 256, 184. (3) De Strooper, B., Iwatsubo, T., Wolfe, M. S. (2012) Presenilins and γ-secretase: structure, function, and role in Alzheimer disease. Cold Spring Harb. Perspect Med. 2, a006304. (4) Thinakaran, G., Borchelt, D. R., Lee, M. K., Slunt, H. H., Spitzer, L., Kim, G., Ratovisky, T., Davenport, F., Nordstedt, C., Seeger, M., et al. (1996) Endoproteolysis of presenilin 1 and accumulation of processed derivatives in vivo. Neuron 17, 181-190. (5) Esler, W. P., Kimberly, W. T., Ostaszewski, B. L., Ye, W., Diehl, T. S., Selkoe, D. J., Wolfe, M. S. (2002) Activity-dependent isolation of the presenilin-γ-secretase complex reveals nicastrin and a γ substrate. Proc. Natl. Acad. Sci. U. S. A. 99, 2720-2725. (6) Kornilova, A. Y., Bihel, F., Das, C., Wolfe, M. S. (2005) The initial substrate-binding site of γ-secretase is located on presenilin near the active site. Proc. Natl. Acad. Sci. U. S. A. 102, 3230-3235. (7) Takagi-Niidome, S., Sasaki, T., Osawa, S., Sato, T., Morishima, K., Cai, T., Iwatsubo, T., Tomita, T. (2015) Cooperative roles of hydrophilic loop 1 and the C-terminus of presenilin 1 in the substrate-gating mechanism of γsecretase. J. Neurosci. 35, 2646-2656. (8) Li, Y., Lu, S. H. J., Tsai, C. J., Bohm, C., Qamar, S., Dodd, R. B., Meadows, W., Jeon, A., McLeodm, A., Chen, F., et al. (2014) Structural interactions between inhibitor and substrate docking sites give insight into mechanisms of human PS1 complexes. Structure 22, 125-135. (9) Elad, N., De Strooper, B., Lismont, S., Hagen, W., Veugelen, S., Arimon, M., Horré, K., Berezovska, S., Sachse, C., Chávez-Gutiérrez, L. (2015) The dynamic conformational landscape of γ-secretase. J. Cell Sci. 128, 589-598. (10) Bolduc, D. M., Montagna, D. R., Seghers, M. C., Wolf, M. S., Selkoe, D. J. (2016) The amyloid-beta forming tripeptide cleavage mechanism of γ-secretase. eLife 5, e17578. (11) Langosch, D., Scharmagl, C., Steiner, H., Lemberg, M. K. (2015) Understanding intramembrane proteolysis: from protein dynamics to reaction kinetics. Trends Biochem. Sci. 40, 318-327. (12) He, G., Luo, W., Li, P., Remmers, C., Netzer, W. J., Hendrick, J., Bettayeb, K., Flajolet, M., Gorelick, F., Wennogle, L. P., et al. (2010) Gamma-secretase activating protein is a therapeutic target for Alzheimer’s disease. Nature 467, 95-98. ( 13 ) Watanabe, N., Takagi, S., Tominaga, A., Tomita, T., Iwatsubo, T. (2010) Functional Analysis of the Transmembrane Domains of Presenilin 1 PARTICIPATION OF TRANSMEMBRANE DOMAINS 2 AND 6 IN THE FORMATION OF INITIAL SUBSTRATE-BINDING SITE OF γ-SECRETASE. J. Biol. Chem. 285, 1973819746. (14) Wolffs, M., Delsuc, N., Veldman, D., Anh, N. V., Williams, R. M., Meskers, S. C. J., Janssen, R. A. J., Huc, I., Schenning, A. P. H. J. (2009) Helical aromatic oligoamide foldamers as organizational scaffolds for photoinduced charge transfer. J. Am. Chem. Soc. 131, 4819-4829. (15) Sato, C., Takagi, S., Tomita, T., Iwatsubo, T. (2008) The C-terminal PAL motif and transmembrane domain 9 of presenilin 1 are involved in the formation of the catalytic pore of the γ-secretase. J. Neurosci. 28, 6264-6271. (16) Li, X., Dang, S., Yan, C., Gong, X., Wang, J., Shi, Y. (2013) Structure of a presenilin family intramembrane aspartate protease. Nature 2013, 493, 56-61. (17) Barret, P. J., Song, Y., van Horn, W. D., Hustedt, E. J., Schafer, J. M., Hadziselimovic, A., Beel, A. J., Sanders, C. R. (2012) The amyloid precursor protein has a flexible transmembrane domain and binds cholesterol. Science 336, 1168-1171. (18) Pester, O., Barret, P. J., Hornburg, D., Hornburg, P., Pröbstle, R., Widmaier, S., Kutzner, C., Dürrbaum, M., Kapurniotu, A., Sanders, C. R., et al. (2013) The backbone dynamics of the amyloid precursor protein transmembrane helix provides a rationale for the sequential cleavage mechanism of γ-secretase. J. Am. Chem. Soc. 135, 1317-1329. (19) Dominguez, L., Meredith, S. C., Straub, J. E., Thirumalai, D. (2014) Transmembrane fragment structures of amyloid precursor protein depend on membrane surface curvature. J. Am. Chem. Soc. 136, 854-857. (20) Scharnagl, C., Pester, O., Hornburg, P., Hornburg, D., Götz, A., Langosch, D. (2014) Side-chain to main-chain hydrogen bonding controls the intrinsic backbone dynamics of the amyloid precursor protein transmembrane helix. Biophys. J. 106, 1318-1326.

30 ACS Paragon Plus Environment

Page 31 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

(21) Miyashita, N., Straub, J. E., Thirumalai, D. (2009) Structures of β-Amyloid Peptide 1− 40, 1− 42, and 1− 55 the 672− 726 Fragment of APP in a Membrane Environment with Implications for Interactions with γ-Secretase. J. Am. Chem. Soc. 131, 17843-17852. (22) Bai, X. C., Rajendra, E., Yang, G., Shi, Y., Scheres, S. H. W. (2015) Sampling the conformational space of the catalytic subunit of human γ-secretase. eLife 4, e11182. (23) Bai, X. C., Yan, C., Yang, G., Lu, P., Ma, D., Sun, L., Zhou, R., Scheres, S. H. W., Shi, Y. (2015) An atomic structure of human γ-secretase. Nature 525, 212-217. (24) Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P., de Vries, A. H. (2007) The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B 111, 7812-7824. (25) de Jong, D. H., Singh, G., Bennett, W. F. D., Arnaresz, C., Wassenaar, T. A., Schäfer, L. V., Periole, X., Tieleman, D. P., Marrink, S. J. (2012) Improved parameters for the martini coarse-grained protein force field. J. Chem. Theory Comput. 9, 687-697. (26) Dominguez, L., Foster, L., Straub, J. E., Thirumalai, D. (2016) Impact of membrane lipid composition on the structure and stability of the transmembrane domain of amyloid precursor protein. Proc. Natl. Acad. Sci. U. S. A. 113, E5281-E5287. (27) Reddy, T., Rainey, J. K. (2012) Multifaceted substrate capture scheme of a rhomboid protease. J. Phys. Chem. B 116, 8942-8954. (28) Mumm, J. S., Kopan, R. (2000) Notch signaling: from the outside in. Dev. Biol. 228, 151-165. ( 29 ) Kopan, R., IIagan, M. X. G. (2009) The canonical Notch signaling pathway: unfolding the activation mechanism. Cell 137, 216-233. (30) Raman, E. P., Guvench, O., MacKerell, A. D. (2010) CHARMM additive all-atom force field for glycosidic linkages in carbohydrates involving furanoses. J. Phys. Chem. B 114, 12981-12994. (31) Huang, J., MacKerell, A. D. (2013) CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 34, 2135-2145. (32) Fukumori, A., Steiner, H. (2016) Substrate recruitment of γ-secretase and mechanism of clinical presenilin mutations revealed by photoaffinity mapping. EMBO J. e201694151. (33) Chen, A. C., Guo, L. Y., Ostaszewski, B. L., Selkoe, D. J., LaVoie, M. J. (2010) Aph-1 associates directly with full-length and C-terminal fragments of γ-secretase substrates. J. Biol. Chem. 285, 11378-11391. (34) Tomita, T., Watabiki, T., Takikawa, R., Morohashi, Y., Takasugi, N., Kopan, R., De Strooper, B., Iwatsubo, T. (2001) The first proline of PALP motif at the C terminus of presenilins is obligatory for stabilization, complex formation, and γ-secretase activities of presenilins. J. Biol. Chem. 276, 33273-33281. (35) Wang, J., Beher, D., Nyborg, A. C., Shearman, M. S., Golde, T. E., Goate, A. (2006) C-terminal PAL motif of presenilin and presenilin homologues required for normal active site conformation. J. Neurochem. 96, 218-227. (36) Kukar, T. L., Ladd, T. B., Robertson, P., Pintchovski, S. A., Moore, B., Bann, M. A., Ren, Z., Jansen-West, K., Malphrus, K., Eggert, S., et al. (2011) Lysine 624 of the Amyloid Precursor Protein (APP) Is a Critical Determinant of Amyloid β Peptide Length SUPPORT FOR A SEQUENTIAL MODEL OF γ-SECRETASE INTRAMEMBRANE PROTEOLYSIS AND REGULATION BY THE AMYLOID β PRECURSOR PROTEIN (APP) JUXTAMEMBRANE REGION. J. Biol. Chem. 286, 39804-39812. (37) Song, Y., Mittendorf, K. F., Lu, Z., Sanders, C. R. (2014) Impact of bilayer lipid composition on the structure and topology of the transmembrane amyloid precursor C99 protein. J. Am. Chem. Soc. 136, 4093-4096. (38) Dimitrov, M., Alattia, J. R., Lemmin, T., Lehal, R., Fligier, A., Houacine, J., Hussain, I., Radtke, F., Peraro, M, D., Beher, D., et al. (2013) Alzheimer's disease mutations in APP but not γ-secretase modulators affect epsiloncleavage-dependent AICD production. Nat. Commun. 4, 2246. (39) Fernandez, M. A., Biette, K. M., Dolios, G., Seth, D., Wang, R., Wolf, M. S. (2016) Transmembrane substrate Determinants for γ-Secretase processing of APP CTFβ. Biochem. 55, 5675-5688. (40) Annaert, W. G., Esselens, C., Baert, V., Boeve, C., Snellings, G., Cupers, P., Creassaerts, K., De Strooper, B. (2001) Interaction with telencephalin and the amyloid precursor protein predicts a ring structure for presenilins. Neuron 32, 579-589. ( 41 ) Bhagatji, P., Leventis, R., Rich, R., Line, C., Silvius, J. R. (2010) Residue-specific side-chain packing determines the backbone dynamics of transmembrane model helices. Biophys. J. 99, 2541-2549. (42) Dahl, A. C. E.; Chavent, M.; Sansom, M. S. P. (2012) Bendix: intuitive helix geometry analysis and abstraction. Bioinformatics 28, 2193-2194. (43) Osenkowski, P., Li, H., Ye, W., Li, D., Aeschbach, L., Fraering, P. C., Wolfe, M. S., Selkoe, D. J., Li, H. (2009) Cryoelectron microscopy structure of purified γ-secretase at 12 Å resolution. J. Mol Biol 385, 642-652.

31 ACS Paragon Plus Environment

ACS Chemical Neuroscience

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 33

(44) Renzi, F.; Zhang, X.; Rice, W. J.; Torres-Arancivia, C.; Gomez-Llorente, Y., Diaz, R., Ahn, K., Yu, C. J., Li, Y. M., Sisodia, S. S., Ubarretxena-Belandia, I. (2011) Structure of γ-secretase and its trimeric pre-activation intermediate by single-particle electron microscopy. J. Biol. Chem. 286, 21440-21449. (45) Ray, W. J., Yao, M., Nowotny, P., Mumm, J., Zhang, W., Wu, J. Y., Kopan, R., Goate, A. M. (1999) Evidence for a physical interaction between presenilin and Notch. Proc. Natl. Acad. Sci. U. S. A. 96, 3263-3268. (46) Beher, D., Clarke, E. E., Wrigley, J. D., Martin, A. C., Nadin, A., Churcher, I., Shearman, M. S. (2004) Selected Non-steroidal Anti-inflammatory Drugs and Their Derivatives Target γ-Secretase at a Novel Site EVIDENCE FOR AN ALLOSTERIC MECHANISM. J. Biol. Chem. 279, 43419-43426. (47) Kukar, T. L., Ladd, T. B., Bann, M. A., Fraering, P. C., Narlawar, R., Maharvi, G. M., Healy, B., Chapman, R., Welzel, A. T., Price, R. W., et al. (2008) Substrate-targeting & γ-secretase modulators. Nature 453, 925-929. (48) Sagi, S. A., Lessard, C. B., Winden, K. D., Maruyama, H., Koo, J. C., Weggen, S., Kukar, T. L., Koo, E. H. (2011) Substrate sequence influences γ-secretase modulator activity, role of the transmembrane domain of the amyloid precursor protein. J. Biol. Chem. 286, 39794-39803. (49) Bolduc, D. M., Montagna, D. R., Gu Y., Selkoe, D. J., Wolfe, M. S. (2016) Nicastrin functions to sterically hinder γ-secretase–substrate interactions driven by substrate transmembrane domain. Proc. Natl. Acad. Sci. U. S. A. 22, E509-E518. (50) Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Cassarino, T. G., Bertoni, M., Bordoli, L., Schwede, T. (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252. (51) Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., Lindahl, E. (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX s1-2, 19-25. (52) Qi, Y., Ingόlgsson, H. I., Cheng, X., Lee, J., Marrink, S. J., Im, W. (2015) CHARMM-GUI martini maker for coarse-grained simulations with the martini force field. J. Chem. Theory Comput. 11, 4486-4494. (53) Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I., Lomize, A. L. (2012). OPM database and PPM web server: resources for positioning of proteins in membranes. Nuclei Acids Res. 40, D370-D376. (54) Lee X., Cheng X., Swails, J. M., Yeom, M. S., Eastman, P. K., Lemkul, J. A., Wei, S., Buckner, J., Jeong, J. C., Qi, Y., Jo, S., Pande, V. S., Case, D. A., Brooks, C. L., MacKerell, A. D.., Klauda, J. B., Im, J. (2016) CHARMMGUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405-413. (55) Mark, P., Nilsson, L. (2011) Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J. Phys. Chem. A 105, 9954-9960. (56) Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., Pedersen, L. G. (1995) A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577-8593. (57) Daura, X., Gademann, K., Jaun, B., Seebach, D., van Gunsteren, W. F., Mark, A. E. (1999) Peptide folding: when simulation meets experiment. Angew Chem. Int. Ed. 38, 236-240. (58) Dalton, J. A R., Michalopoulos, I., Westhead, D. R. (2003) Calculation of helix packing angles in protein structures. Bioinformatics 19, 1298. ( 59 ) Panahi, A., Bandara, A., Pantelopulos, G. A., Dominguez, L., Straub, J. E. (2016) Specific Binding of Cholesterol to C99 Domain of Amyloid Precursor Protein Depends Critically on Charge State of Protein. J. Phys. Chem. Lett. 7, 3535-3541.

32 ACS Paragon Plus Environment

Page 33 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Chemical Neuroscience

Table of Content Graphics

33 ACS Paragon Plus Environment