The amyloid cascade hypothesis: an updated critical review.

Results from recent clinical trials of antibodies that target amyloid-ß (Aß) for Alzheimer's disease have created excitement and have been heralded as corroboration of the amyloid cascade hypothesis. However, while Aß may contribute to disease, genetic, clinical, imaging and biochemical data suggest a more complex aetiology. Here we review the history and weaknesses of the amyloid cascade hypothesis in view of the new evidence obtained from clinical trials of anti-amyloid antibodies. These trials indicate that the treatments have either no or uncertain clinical effect on cognition. Despite the importance of amyloid in the definition of Alzheimer's disease, we argue that the data point to Aß playing a minor aetiological role. We also discuss data suggesting that the concerted activity of many pathogenic factors contribute to Alzheimer's disease and propose that evolving multi-factor disease models will better underpin the search for more effective strategies to treat the disease.

Excess mortality in Denmark, Finland, Norway and Sweden during the COVID-19 pandemic 2020-2022.

BACKGROUND: The Nordic countries represent a unique case study for the COVID-19 pandemic due to socioeconomic and cultural similarities, high-quality comparable administrative register data and notable differences in mitigation policies during the pandemic. We aimed to compare weekly excess mortality in the Nordic countries across the three full pandemic years 2020-2022. METHODS: Using data on weekly all-cause mortality from official administrative registers in Denmark, Finland, Norway and Sweden, we employed time series regression models to assess mortality developments within each pandemic year, with the period 2010-2019 used as reference period. We then compared excess mortality across the countries in 2020-2022, taking differences in population size and age- and sex-distribution into account. Results were age- and sex-standardized to the Danish population of 2020. Robustness was examined with a variety of sensitivity analyses. RESULTS: While Sweden experienced excess mortality in 2020 [75 excess deaths per 100 000 population (95% prediction interval 29-122)], Denmark, Finland and Norway experienced excess mortality in 2022 [52 (14-90), 130 (83-177) and 88 (48-128), respectively]. Weekly death data reveal how mortality started to increase in mid-2021 in Denmark, Finland and Norway, and continued above the expected level through 2022. CONCLUSION: Although the Nordic countries experienced relatively low pandemic excess mortality, the impact and timing of excess mortality differed substantially. These estimates-arguably the most accurate available for any region in capturing pandemic-related excess deaths-may inform future research and policy regarding the complex mortality dynamics in times of a health crisis such as the COVID-19 pandemic.

Stability and expression of SARS-CoV-2 spike-protein mutations.

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

Survival of the cheapest: how proteome cost minimization drives evolution.

Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, quantitative proteomics revealed that selection pressures not relating to optimal function play much larger roles than previously thought, acting perhaps most importantly via protein expression levels. This paper first summarizes recent progress in the 21st century toward recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying 'non-function' selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases.

Data set and fitting dependencies when estimating protein mutant stability: Toward simple, balanced, and interpretable models.

Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias BM . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the Ssym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case.

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged "ensemble" estimates for groups of residues when multiple structures are available.

Assessment of AlphaFold2 for Human Proteins via Residue Solvent Exposure.

As only 35% of human proteins feature (often partial) PDB structures, the protein structure prediction tool AlphaFold2 (AF2) could have massive impact on human biology and medicine fields, making independent benchmarks of interest. We studied AF2's ability to describe the backbone solvent exposure as a functionally important and easily interpretable "natural coordinate" of protein conformation, using human proteins as test case. After screening for appropriate comparative sets, we matched 1818 human proteins predicted by AF2 against 7585 unique experimental PDBs, and after curation for sequence overlap, we assessed 1264 comparative pairs comprising 115 unique AF2 structures and 652 unique experimental structures. AF2 performed markedly worse for multimers, whereas ligands, cofactors, and experimental resolution were interestingly not very important for performance. AF2 performed excellently for monomer proteins. Challenges relating to specific groups of residues and multimers were analyzed. We identified larger deviations for lower-confidence scores (pLDDT), and exposed residues and polar residues (e.g., Asp, Glu, Asn) being less accurately described than hydrophobic residues. Proline conformations were the hardest to predict, probably due to a common location in dynamic solvent-accessible parts. In summary, using solvent exposure as a metric, we quantified the performance of AF2 for human proteins and provided estimates of the expected agreement as a function of ligand presence, multimer/monomer status, local residue solvent exposure, pLDDT, and amino acid type. Overall performance was found to be excellent.

A base measure of precision for protein stability predictors: structural sensitivity.

BACKGROUND: Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. RESULTS: We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. CONCLUSIONS: The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.

Membrane dynamics of γ-secretase with the anterior pharynx-defective 1B subunit.

The four-subunit protease complex γ-secretase cleaves many single-pass transmembrane (TM) substrates, including Notch and ß-amyloid precursor protein to generate amyloid-ß (Aß), central to Alzheimer's disease. Two of the subunits anterior pharynx-defective 1 (APH-1) and presenilin (PS) exist in two homologous forms APH1-A and APH1-B, and PS1 and PS2. The consequences of these variations are poorly understood and could affect Aß production and γ-secretase medicine. Here, we developed the first complete structural model of the APH-1B subunit using the published cryo-electron microscopy (cryo-EM) structures of APH1-A (Protein Data Bank: 5FN2, 5A63, and 6IYC). We then performed all-atom molecular dynamics simulations at 303 K in a realistic bilayer system to understand both APH-1B alone and in γ-secretase without and with substrate C83-bound. We show that APH-1B adopts a 7TM topology with a water channel topology similar to APH-1A. We demonstrate direct transport of water through this channel, mainly via Glu84, Arg87, His170, and His196. The apo and holo states closely resemble the experimental cryo-EM structures with APH-1A, however with subtle differences: The substrate-bound APH-1B γ-secretase was quite stable, but some TM helices of PS1 and APH-1B rearranged in the membrane consistent with the disorder seen in the cryo-EM data. This produces different accessibility of water molecules for the catalytic aspartates of PS1, critical for Aß production. In particular, we find that the typical distance between the catalytic aspartates of PS1 and the C83 cleavage sites are shorter in APH-1B, that is, it represents a more closed state, due to interactions with the C-terminal fragment of PS1. Our structural-dynamic model of APH-1B alone and in γ-secretase suggests generally similar topology but some notable differences in water accessibility which may be relevant to the protein's existence in two forms and their specific function and location.

Three Simple Properties Explain Protein Stability Change upon Mutation.

Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by single-point amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.

Precision Biotransformation of Emerging Pollutants by Human Cytochrome P450 Using Computational-Experimental Synergy: A Case Study of Tris(1,3-dichloro-2-propyl) Phosphate.

Precision biotransformation is an envisioned strategy offering detailed insights into biotransformation pathways in real environmental settings using experimentally guided high-accuracy quantum chemistry. Emerging pollutants, whose metabolites are easily overlooked but may cause idiosyncratic toxicity, are important targets of such a strategy. We demonstrate here that complex metabolic reactions of tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) catalyzed by human CYP450 enzymes can be mapped via a three-step synergy strategy: (i) screening the possible metabolites via high-throughout (moderate-accuracy) computations; (ii) analyzing the proposed metabolites in vitro by human liver microsomes and recombinant human CYP450 enzymes; and (iii) rationalizing the experimental data via precise mechanisms using high-level targeted computations. Through the bilateral dialogues from qualitative to semi-quantitative to quantitative levels, we show how TDCIPP metabolism especially by CYP3A4 generates bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) as an O-dealkylation metabolite and bis(1,3-dichloro-2-propyl) 3-chloro-1-hydroxy-2-propyl phosphate (alcoholß-dehalogen) as a dehalogenation/reduction metabolite via the initial rate-determining H-abstraction from αC- and ßC-positions. The relative yield ratio [dehalogenation/reduction]/[O-dealkylation] is derived from the relative barriers of H-abstraction at the ßC- and αC-positions by CYP3A4, estimated as 0.002 to 0.23, viz., an in vitro measured ratio of 0.04. Importantly, alcoholß-dehalogen formation points to a new mechanism involving successive oxidation and reduction functions of CYP450, with its precursor aldehydeß-dehalogen being a key intermediate detected by trapping assays and rationalized by computations. We conclude that the proposed three-step synergy strategy may meet the increasing challenge of elucidating biotransformation mechanisms of substantial synthesized organic compounds in the future.

A computer-simulated mechanism of familial Alzheimer's disease: Mutations enhance thermal dynamics and favor looser substrate-binding to γ-secretase.

The 4-subunit intramembrane protease complex γ-secretase cleaves many substrates including fragments of the ß-amyloid precursor protein (APP), leading to formation of Aß peptides, and Notch. Mutations in APP and the catalytic subunit of γ-secretase, presenilin, cause familial Alzheimer's disease (fAD). Mutations are assumed to change the substrate-binding and cleavage and thereby the Aß formed. Whereas a wild-type structure of substrate-bound γ-secretase became recently available from cryogenic electron microscopy (6IYC), the structure and dynamics of mutant proteins remain obscure. Here, we studied five prominent mutants of substrate-bound γ-secretase by explicit all-atom molecular dynamics in a phospholipid membrane model at physiological temperature using the experimental structure as template: The presenilin 1 mutants E280A, G384A, A434C, and L435F and the V717I mutant of APP. Our structures and dynamics provide the first atomic detail into how fAD-causing mutations affect substrate binding to γ-secretase. The pathogenic mutations tend to increase the space and variability in the substrate binding site, as seen e.g. from the distance from catalytic aspartate to substrate cleavage sites. We suggest that we have identified the molecular cause of the "imprecise cleavage" that leads to two trimming pathways in γ-secretase, consistent with the FIST model, which may rationalize the experimental Aß42/Aß40 ratios as a molecular basis for fAD.

Common mechanism of thermostability in small α- and ß-proteins studied by molecular dynamics.

Protein thermostability is important to evolution, diseases, and industrial applications. Proteins use diverse molecular strategies to achieve stability at high temperature, yet reducing the entropy of unfolding seems required. We investigated five small α-proteins and five ß-proteins with known, distinct structures and thermostability (Tm ) using multi-seed molecular dynamics simulations at 300, 350, and 400 K. The proteins displayed diverse changes in hydrogen bonding, solvent exposure, and secondary structure with no simple relationship to Tm . Our dynamics were in good agreement with experimental B-factors at 300 K and insensitive to force-field choice. Despite the very distinct structures, the native-state (300 + 350 K) free-energy landscapes (FELs) were significantly broader for the two most thermostable proteins and smallest for the three least stable proteins in both the α- and ß-group and with both force fields studied independently (tailed t-test, 95% confidence level). Our results suggest that entropic ensembles stabilize proteins at high temperature due to reduced entropy of unfolding, viz., ΔG = ΔH - TΔS. Supporting this mechanism, the most thermostable proteins were also the least kinetically stable, consistent with broader FELs, typified by villin headpiece and confirmed by specific comparison to a mesophilic ortholog of Thermus thermophilus apo-pyrophosphate phosphohydrolase. We propose that molecular strategies of protein thermostabilization, although diverse, tend to converge toward highest possible entropy in the native state consistent with the functional requirements. We speculate that this tendency may explain why many proteins are not optimally structured and why molten-globule states resemble native proteins so much.

Chemical Causes of Metal Nobleness.

Humans have appreciated the "noble" metals for millennia, yet modern chemistry still struggles with different definitions. Herein, metal nobleness is analyzed using thermochemical cycles including the different bulk, gas, and solution states implied by these definitions. The analysis suggests that metal nobleness mainly reflects inability to fulfil the electron demands of electronegative oxygen. Accordingly, gold is the most noble metal in existence, not because of d-band properties of the solid state, but because gold's electronegativity is closest to that of oxygen, producing weaker polar covalent bonding. The high electronegativity arises from the effective nuclear charge due to diffuse d-states, enforced by relativistic effects. This explanation accounts for the activity series, corrosion tendency, and trends in oxygen chemisorption, which other models do not. While gold is the most noble metal, the ranking of Ag, Pt, and Pd depends on the thermochemistry as discussed in detail.

Dioxygen Binding to all 3d, 4d, and 5d Transition Metals from Coupled-Cluster Theory.

Understanding how transition metals bind and activate dioxygen (O2 ) is limited by experimental and theoretical uncertainties, making accurate quantum mechanical descriptors of interest. Here we report coupled-cluster CCSD(T) energies with large basis sets and vibrational and relativistic corrections for 160 3d, 4d, and 5d metal-O2 systems. We define four reaction energies (120 in total for the 30 metals) that quantify O-O activation and reveal linear relationships between metal-oxygen and O-O binding energies. The CCSD(T) data can be combined with thermochemical cycles to estimate chemisorption and physisorption energies for each metal from metal oxide embedding energies, in good correlation with atomization enthalpies (R2 =0.75). Spin-geometry variations can break the linearities, of interest to circumventing the Sabatier principle. Pt, Pd, Co, and Fe form a distinct group with the weakest O2 binding. R2 up to 0.84 between surface adsorption energies and our energies for MO2 systems indicate relevance also to real catalytic systems.

Systematic Investigation of the Data Set Dependency of Protein Stability Predictors.

Prediction of protein stability changes caused by mutation is of major importance to protein engineering and for understanding protein misfolding diseases and protein evolution. The major limitation to these applications is the fact that different prediction methods vary substantially in terms of performance for specific proteins; i.e., performance is not transferable from one type of mutation or protein to another. In this study, we investigated the performance and transferability of eight widely used methods. We first constructed a new data set composed of 2647 mutations using strict selection criteria for the experimental data and then defined a variety of subdata sets that are unbiased with respect to various aspects such as mutation type, stabilization extent, structure type, and solvent exposure. Benchmarking the methods against these subdata sets enabled us to systematically investigate how data set biases affect predictor performance. In particular, we use a reduced amino acid alphabet to quantify the bias toward mutation type, which we identify as the major bias in current approaches. Our results show that all prediction methods exhibit large biases, stemming not from failures of the models applied but mostly from the selection biases of experimental data used for training or parametrization. Our identification of these biases and the construction of new mutation-type-balanced data should lead to the development of more balanced and transferable prediction methods in the future.

Computational Biotransformation Profile of Emerging Phenolic Pollutants by Cytochromes P450: Phenol-Coupling Mechanism.

Phenols are ubiquitous environmental pollutants, whose biotransformation involving phenol coupling catalyzed by cytochromes P450 may produce more lipophilic and toxic metabolites. Density functional theory (DFT) computations were performed to explore the debated phenol-coupling mechanisms, taking triclosan as a model substrate. We find that a diradical pathway facilitated by compound I and protonated compound II of P450 is favored vs alternative radical addition or electron-transfer mechanisms. The identified diradical coupling resembles a "two-state reactivity" from compound I characterized by significantly high rebound barriers of the phenoxy radicals, which can be formulated into three equations for calculating the ratio [coupling]/[hydroxylation]. A higher barrier for rebound than for H-abstraction in high-spin triclosan can facilitate the phenoxy radical dissociation and thus enable phenol coupling, while H-abstraction/radical rebound causing phenol hydroxylation via minor rebound barriers mostly occurs via the low-spin state. Therefore, oxidation of triclosan by P450 fits the first equation with a ratio [coupling]/[hydroxylation] of 1:4, consistent with experimental data indicating different extents of triclosan coupling (6-40%). The high rebound barrier of phenoxy radicals, as a key for the mechanistic identification of phenol coupling vs hydroxylation, originates from their weak electron donor ability due to spin aromatic delocalization. We envision that the revealed mechanism can be extended to the cross-coupling reactions between different phenolic pollutants, and the coupling reactions of several other aromatic pollutants, to infer unknown metabolites.

Cryo-temperature effects on membrane protein structure and dynamics.

Innovations in cryogenic electron microscopy (Cryo-EM) have led to high-quality structures of important proteins such as the ribosome and γ-secretase, the membrane protease that produces Aß involved in Alzheimer's disease. However, freezing may change protein structure and dynamics relative to the physiologically relevant "hot" state. To explore this, we studied substrate-bound γ-secretase (6IYC) by molecular dynamics as a hot, cold, and quickly cooled state in both membrane and water systems. We show that the experimental structure resembles the simulated cooled state, structurally between the hot and cold states and membrane and water systems, but with cold dynamics. We observe "cryo-contraction" in the membrane from 303 to 85 K, reducing radius of gyration (Rg) by 1% from 4.01 to 3.97 nm (6IYC = 3.95 nm). The hot state features an unwound C83-substrate with 10-14 α-helix residues (6IYC: 11) in equilibrium with an intact state with 16 helix residues not previously reported. The ß-sheet is weakened with temperature. Multiple hot conformations probably control the Aß42/Aß40 ratio. We thus propose that MD simulation protocols of hot, cold, and cooled states as applied here can correct cryo-EM coordinates. However, important frozen-out fast modes require specific supplementary hot simulations or experiments.

Using electronegativity and hardness to test density functionals.

Density functional theory (DFT) is used in thousands of papers each year, yet lack of universality reduces DFT's predictive capacity, and functionals may produce energy-density imbalances. The absolute electronegativity (χ) and hardness (η) directly reflect the energy-density relationship via the chemical potential ∂E/∂N and we thus hypothesized that they probe universality. We studied χ and η for atoms Z = 1-36 using 50 diverse functionals covering all major classes. Very few functionals describe both χ and η well. η benefits from error cancellation, whereas χ is marred by error propagation from IP and EA; thus, almost all standard GGA and hybrid functionals display a plateau in the MAE at ∼0.2 eV-0.3 eV for η. In contrast, variable performance for χ indicates problems in describing the chemical potential by DFT. The accuracy and precision of a functional is far from linearly related, yet for a universal functional, we expect linearity. Popular functionals such as B3LYP, PBE, and revPBE perform poorly for both properties. Density sensitivity calculations indicate large density-derived errors as occupation of degenerate p- and d-orbitals causes "non-universality" and large dependency on exact exchange. Thus, we argue that performance for χ for the same systems is a hallmark of an important aspect of universality by probing ∂E/∂N. With this metric, B98, B97-1, PW6B95D3, MN-15, rev-TPSS, HSE06, and APFD are the most "universal" among the tested functionals. B98 and B97-1 are accurate for very diverse metal-ligand bonds, supporting that a balanced description of ∂E/∂N and ∂E2/∂N2, via χ and η, is probably a first simple probe of universality.

Molecular dynamics of C99-bound γ-secretase reveal two binding modes with distinct compactness, stability, and active-site retention: implications for Aß production.

The membrane protease γ-secretase cleaves the C99 fragment of the amyloid precursor protein, thus producing the Aß peptides central to Alzheimer's disease. Cryo-electron microscopy has provided the topology but misses the membrane and loop parts that contribute to substrate binding. We report here an essentially complete atomic model of C99 within wild-type γ-secretase that respects all the experimental constraints and additionally describes loop, helix, and C99 substrate dynamics in a realistic all-atom membrane. Our model represents the matured auto-cleaved state required for catalysis. From two independent 500-ns molecular dynamic simulations, we identify two conformation states of C99 in equilibrium, a compact and a loose state. Our simulations provide a basis for C99 processing and Aß formation and explain the production of longer and shorter Aß, as the compact state retains C99 for longer and thus probably trims to shorter Aß peptides. We expect pathogenic presenilin mutations to stabilize the loose over the compact state. The simulations detail the role of the Lys53-Lys54-Lys55 anchor for C99 binding, a loss of helicity of bound C99, and positioning of Thr48 and Leu49 leading to alternative trimming pathways on opposite sides of the C99 helix in three amino acid steps. The C99 binding topology resembles that of C83-bound γ-secretase without membrane but lacks a presenilin 1-C99 ß-sheet, which could be induced by C83's stronger binding. The loose state should be selectively disfavored by γ-secretase modulators to increase C99 trimming and reduce the formation of longer Aß, a strategy that is currently much explored but has lacked a structural basis.