An enzyme called gamma-secretase cuts other proteins in cells into smaller pieces. Like most enzymes, gamma-secretase is expected to move through several different three-dimensional shapes to perform its role, and identifying these structures could help us to understand how the enzyme works.

One of the proteins that is targeted by gamma-secretase is called amyloid precursor protein, and cutting this protein results in the formation of so-called amyloid-beta peptides. When gamma-secretase doesn't work properly, these amyloid-beta peptides can accumulate in the brain and large accumulations of these peptides have been observed in the brains of patients with Alzheimer's disease. Earlier in 2015, a group of researchers used a technique called cryo-electron microscopy (cryo-EM) to produce a three-dimensional model of gamma-secretase. This revealed that the active site of the enzyme, that is, the region that is used to cut the other proteins, is particularly flexible.

Now, Bai et al. – including many of the researchers from the earlier work – studied this flexibility in more detail. For the experiments, gamma-secretase was exposed to an inhibitor molecule that stopped it from cutting other proteins. This meant that the structure of gamma-secretase became more rigid than normal, which made it possible to collect more detailed structural information using cryo-EM. Bai et al. also developed new methods for processing images to separate the images of individual enzyme molecules based on the different shapes they had adopted at the time. These methods make it possible to view a mixture of very similar enzyme structures that differ only in a small region of the protein (in this case the active site).

In the future, it would be useful to repeat these imaging experiments using a range of different molecules that alter the activity of gamma-secretase. Furthermore, the new image processing methods developed by Bai et al. could be used to study flexibility in the shapes of other proteins.

γ-Secretase clears the anchors of type-I membrane proteins that are left behind in the membrane after shedding of their ectodomain. The substrate specificity of this intra-membrane protease is remarkably relaxed. It will cleave a wide range of substrates, as long as they form a single hydrophobic transmembrane helix, and the remaining ectodomain is not too large (Struhl and Adachi, 2000). As a manifestation of its promiscuous substrate specificity, γ-secretase also cleaves its substrate in different positions. For two of its most studied substrates, the Notch receptor and the amyloid precursor protein (APP), γ-secretase performs an initial endopeptidase-like ε-cleavage, which is followed by carboxypeptidase-like trimming, called γ-cleavage. Cleavage of APP leads to secretion of β-amyloid (Aβ) peptides into the extracellular environment. Abundant deposits of Aβ peptides in the brain, clinically known as β-amyloid plaques, are a defining characteristic of Alzheimer's disease (AD). Variability in the position of both ε– and γ-cleavages results in Aβ peptides with lengths ranging from 36 to 49 residues, with Aβ40 being the most common form. Longer peptides seem to be more prone to aggregation, and increased ratios of Aβ42/Aβ40 are thought to play a role in AD pathogenesis (Tanzi and Bertram, 2005).

Because of its central role in Aβ generation, γ-secretase is an attractive target for treatment of AD. However, a clinical trial with the γ-secretase inhibitor semagacestat had to be interrupted prematurely due to strong side effects, including skin cancer, weight loss and a faster decline of the cognitive skills of patients receiving the highest dose of the drug (De Strooper, 2014). Probably, global inhibition of the complex to reduce the formation of Aβ-peptides is undesirable, as this may also affect other pathways such as Notch signaling. Therefore, the development of modulators of γ-secretase activity that leave ε-cleavage intact but stimulate γ-cleavage has been suggested as an alternative (Wolfe, 2012). Moreover, the development of specific inhibitors of Notch cleavage may be beneficial for the treatment of cancer. However, such developments are currently hindered by a lack of quantitative insights into the mechanism of γ-secretase proteolysis.

The γ-secretase complex consists of four essential, integral membrane proteins: presenilin (PS), nicastrin, anterior pharynx defective 1 (Aph-1), and presenilin enhancer 2 (Pen-2) (De Strooper, 2003; Kimberly et al., 2003). PS provides the two essential aspartates that form the proteolytic active site where both ε and γ-cleavages occur. More than 200 missense mutations in the gene for PS have been linked to an early-onset, familial form of Alzheimer's disease (FAD) ((De Strooper et al., 2012) and http://www.alzforum.org/mutations). In humans, two genes encode two homologous forms of PS (PS1 and PS2), but most FAD-derived mutations target PS1. Because there are also two forms of Aph-1 (Aph-1a and Aph-1b), four possible complexes may form, of which nicastrin:Aph-1a:PS1:Pen-2 is the most abundant. Nicastrin has a large, heavily glycosylated ectodomain, which is thought to play a role in substrate recognition (Shah et al., 2005). Aph-1 has been proposed to play a scaffolding role (Lee et al., 2004). Pen-2 is essential for proteolytic activity of the mature complex, and facilitates auto-proteolysis of PS1 in a long cytosolic loop between its sixth and seventh transmembrane helices (TM6 and TM7) (Thinakaran et al., 1996).

We recently solved the cryo-EM structure of a nicastrin:Aph-1a:PS1:Pen-2 complex to a resolution of 3.4 Å (Bai et al., 2015a). This structure allowed building a near-complete atomic model, and revealed how Aph-1 and Pen-2 hold a remarkably flexible PS1 subunit underneath the nicastrin ectodomain. In particular, TM2, the cytoplasmic side of TM6 and the long linker between TM6 and TM7 of PS1 were largely disordered. The presence of the linker could only be inferred from fuzzy densities in 2D class averages, whereas the approximate position of TM2 could only be inferred from a 7 Å low-pass filtered map. This low-pass filtered map also showed a rod-shaped density in the cavity between TM2, TM3 and TM5, but the identity of this density could not be determined. Although the sample used for structure determination did show proteolytic activity for APP (Lu et al., 2014), the active site appeared to be in an inactive conformation as the two catalytic aspartates were too far apart to catalyze proteolysis.

In this paper, we set out to gain further insights into the proteolytic mechanism of γ-secretase by using two complementary approaches to sample the conformational landscape of its catalytic subunit. With the aim of trapping the complex in a more defined conformation, we solve a structure in complex with the non-transition-state analogue inhibitor DAPT, which is a precursor in the development of semagacestat (Dovey et al., 2001). In addition, we describe how masked cryo-EM image classification combined with subtraction of part of the signal from the experimental images allows visualizing molecular dynamics of the catalytic subunit in its apo-state at the secondary structure level. The resulting four structures represent a significant step towards understanding how this protease cleaves its many substrates.

Although many proteins employ functionally important flexibility at the level of secondary structure, few experimental techniques exist for the study of this dynamics. Nuclear magnetic resonance (NMR) is a powerful tool for the characterization of structural ensembles in dynamic complexes, but its applicability is typically limited to proteins with a molecular weight below 40– 50 kDa. For complexes larger than several hundred thousand daltons, dynamic changes in tertiary and quaternary structure have been studied by cryo-EM image classification, in particular with the recent advent of direct electron detectors and improved computer algorithms (Bai et al., 2015b; Dashti et al., 2014). However, for complexes that are too large for NMR, the characterization of molecular dynamics within individual protein domains has typically been restricted to computer simulations. The procedure for masked cryo-EM image classification combined with residual signal subtraction fills part of this gap in experimental techniques. The idea to subtract part of the signal from experimental cryo-EM images is not new. Michael Radermacher and colleagues subtracted partial projections from cryo-EM images to study symmetry mismatches in bacteriophage ϕ29 (Morais et al., 2003) and flaviviruses (Zhang et al., 2007); Steven Ludtke and colleagues used image subtraction in the e2ligandclassify.py program to separate ribosomes with and without secY channels (Park et al., 2014); Hongwei Wang and colleagues used a modified version of RELION to subtract projections of NSF rings to analyze a symmetry mismatch and structural variability in the SNAP-SNARE complex (Zhou et al., 2015); we used an iterative image subtraction method in RELION to improve the density of a flexible domain of the spliceosomal U4/U6.U5 tri-snRNP complex (Nguyen et al., 2015); and most recently, Huiskonen and colleagues also used RELION to subtract viral capsid densities in order to visualize an RNA polymerase bound inside the virus (Ilca et al., 2015). The procedure described here provides an easily accessible and generally applicable tool for signal subtraction coupled to masked refinements and/or classifications of single-particle data. Its successful application to γ-secretase demonstrates its potential for complexes that are considered to be relatively small for cryo-EM structure determination, and shows that it allows separation of protein structures that differ only in the orientation and position of a few α-helices.

Application of the masked classification approach to the data set of apo-state γ-secretase particles revealed a range of different conformations for TM6. This conformational flexibility leads to a variation in the distance between the two aspartates that form the active site. Interestingly, the class where the aspartates are closest together also shows a markedly different conformation of Pen-2 and a re-arrangement of TM3, TM4 and TM5 in PS1. Pen-2 is required for autocatalytic maturation and protease activity of γ-secretase. Binding of Pen-2 to the complex activates the active site, and binding of Pen-2 was observed to have an allosteric effect on TM6 (Takeo et al., 2012). However, in the absence of PS1 structures without Pen-2 bound, it will probably remain unclear whether the changes in Pen-2 and PS1 observed here are relevant to this allosteric activation mechanism.

Our analysis of the structure in complex with DAPT provides complementary insights into the conformational freedom of γ-secretase. Upon binding of the inhibitor, the catalytic subunit undergoes a marked rigidification. TM2 and its linkers to TM1 and TM3 become ordered, and so do the cytoplasmic half of TM6 and part of the linker between TM6 and TM7. Around the active site, the conformations of the kink in TM6 and the conformation of the long linker between TM6 and TM7 are noticeably different from the apo-state consensus structure. Maturation of the γ-secretase complex requires auto-proteolytic cleavage at Thr290 (or alternatively at Val292 or Met297). The observed kink in TM6 may expedite the U-turn that is required to position the auto-proteolytic cleavage site back into the active site. Moreover, auto-proteolytic cleavage is predicted to occur in an α-helix spanning residues 280–300 of the linker. In the DAPT-bound structure, a single helical turn starts at Glu280, but residues 285–288 form a short β-sheet with the end of the linker that connects to TM7. The auto-proteolytic site is still flexible in this structure, as the density disappears after Tyr288. Since auto-proteolysis appeared to be complete in our sample (Lu et al., 2014), it could be that residues 280–300 do form a helical structure prior to self-cleavage. Alternatively, it could be that the inhibitor specifically alters the secondary structure in the linker, for example through its observed interaction with Phe283.

TM2 of PS1 is well ordered in both the DAPT-bound structure and in class 1 of the apo-state, whereas it is invisible in the other apo-state classes. This helix only seems to be ordered when something is bound in the large cavity that is lined with FAD-derived mutations between TM2, TM3 and TM5. In the inhibitor-bound structure this cavity contains the density that we attribute to DAPT, while density for a kinked α-helix is visible in class 1 of the apo-state. Although the density for the kinked α-helix was not of sufficient quality for unambiguous identification, mass spectrometry and Western blot analyses suggest that a mixture of different proteins with a single transmembrane helix may be present in our sample. Four of the proteins that were identified by mass spectrometry had also previously been observed to co-purify with γ-secretase: TMP21/p23, p24a, Vamp-8 and Sec22B (Wakabayashi et al., 2009). TMP21 was also observed to be a component of the γ-secretase complex that acts as a negative regulator of γ-cleavage, while leaving ε-cleavage intact (Chen et al., 2006).

We hypothesize that the kinked α-helix in our structure arises from a mixture of co-purified proteins in our sample that bind to the γ-secretase complex in a manner that mimics substrate binding. This hypothesis is in good agreement with previous biochemical observations about an 'initial substrate-binding site' that is distinct from the active site (Beher et al., 2003; Das et al., 2003; Esler et al., 2002; Tian et al., 2002). Photolabeling experiments suggest that the initial substrate-binding site partially overlaps with the DAPT-binding site (Kornilova et al., 2003; Morohashi et al., 2006), and mutational analysis identified TM2 and TM6 to be involved in substrate binding (Watanabe et al., 2010). Experiments with photoaffinity probes based on α-helical substrate-like inhibitors showed that DAPT could not displace a 10-residue long helical probe, suggesting spatially separated binding sites of the substrate and the inhibitor. This however was not the case for a 13-residue long peptide, for which addition of DAPT led to a strong reduction in photolabeling. Because this peptide also prevented labeling of a transition-state mimicking photoprobe, the longer α-helical probe probably also interacts with the active site (Kornilova et al., 2005). Our hypothesis explains these data well. A superposition of the kinked α-helix on top of the DAPT structure shows that after the kink, the α-helix extends for 10 residues into the transmembrane domain, before its four C-terminal residues overlap with the DAPT-binding site and almost reach the active site (Figure 6A).

Figure 6 with 1 supplement see all

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Furthermore, the NMR structure of the Aβ42 peptide in an aqueous solution of fluorinated alcohols shows a strikingly similar 90-degree kink in its α-helical structure, which is positioned right after Lys699 (Figure 6B). A superposition of the NMR model on the kinked α-helix in our structure places its N-terminus flat on the extracellular side of the membrane, while the cleavage site that would result in the formation of Aβ42 peptides is placed in close proximity to the active site (Figure 6C). Interestingly, all the proteins that were both identified in our sample by mass spectrometry and that had also previously been observed to co-purify with γ-secretase (Wakabayashi et al., 2009) contain either an arginine or a lysine just before their predicted transmembrane helix (Figure 6—figure supplement 1). It is therefore tempting to speculate that the large, positively charged residue together with the kink in the α-helix act as an anchor to delimit the cleavage position in the substrate. This would explain why mutations in Lys699 have a marked effect on the length of the Aβ cleavage products (Kukar et al., 2011). In addition, the kink in the α-helix is positioned next to the recently mapped binding site of a phenylimidazole-like γ-secretase modulator (highlighted with spheres in Figure 6A), which could explain how this modulator affects cleavage in the distant active site through changes in the substrate conformation (Takeo et al., 2014). It could also explain why replacement of Lys699 or increasing the positive charge at Gly700 has been observed to block or attenuate the effects of γ-secretase modulators (Jung et al., 2014).

In conclusion, we show that masked cryo-EM image classification combined with the subtraction of part of the signal from the experimental images allows separation of structures that differ at the secondary structure level. Application of this approach to our previously described data set of γ-secretase in its apo-state reveals distinct conformations for TM2 and TM6 of PS1. In one of the identified structures, we observe a kinked α-helix in the cavity between TM2, TM3 and TM5 of PS1 that cannot be attributed to any of the γ-secretase components. Mass spectrometry and Western blot analyses suggest the presence of a mixture of proteins with a single transmembrane helix that co-purified with the complex, one of which is a known modulator of γ-secretase cleavage. These results are complemented with a cryo-EM structure of the complex bound to the dipeptidic inhibitor DAPT. Binding of the inhibitor leads to a marked reduction in the flexibility of the catalytic subunit. Together, our results form the basis for a hypothesis that substrate enters the transmembrane domain through the cavity formed by TM2, TM3 and TM5 of PS1. The disordered nature of TM2 in the absence of substrate or inhibitor suggests that this helix may act as a lateral gate through which the transmembrane helix of the substrate enters the cavity. The observation that the inhibitor-bound structure closely resembles the class with the kinked α-helix suggests that the inhibitor and the substrate stabilize a similar conformation from the apo-state ensemble. DAPT would then act as an inhibitor by blocking access of the substrate to the active site. An appealing route to confirm our hypothesis, and to gain further mechanistic insights, is the determination of additional cryo-EM structures in complex with different substrates, substrate analogues, inhibitors or other modulators of activity. Masked classification with signal subtraction will be a useful tool in this endeavor, as understanding how different factors shift the equilibrium of conformational states in this flexible enzyme will be key to increase our understanding of its functioning.

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Author details

1. Xiao-chen Bai

   MRC Laboratory of Molecular Biology, Cambridge, United Kingdom

   Contribution

   X-CB, Collected and processed cryo-EM data and performed atomic model refinement, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

2. Eeson Rajendra

   MRC Laboratory of Molecular Biology, Cambridge, United Kingdom

   Contribution

   ER, Performed Western blot analysis, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Guanghui Yang

   Ministry of Education Key Laboratory of Protein Science, Tsinghua-Peking Joint Center for Life Sciences, Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, China

   Contribution

   GY, Purified γ-secretase, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

4. Yigong Shi

   Ministry of Education Key Laboratory of Protein Science, Tsinghua-Peking Joint Center for Life Sciences, Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, China

   Contribution

   YS, Initiated the γ-secretase structure determination project, Drafting or revising the article

   For correspondence

   shi-lab@tsinghua.edu.cn

   Competing interests

   No competing interests declared.

5. Sjors HW Scheres

   MRC Laboratory of Molecular Biology, Cambridge, United Kingdom

   Contribution

   SHWS, Developed the new classification approach, supervised the work presented, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   scheres@mrc-lmb.cam.ac.uk

   Competing interests

   SHWS: Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-0462-6540

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