{"paper_id":"1136fd4e-a506-4690-8703-62157fa08a02","body_text":"Capture of Amyloid Precursor Protein Fragments by an Engineered Water-Soluble γ-Secretase Variant | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Analysis Capture of Amyloid Precursor Protein Fragments by an Engineered Water-Soluble γ-Secretase Variant Alper Karagöl, Taner Karagöl This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8320072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Amyloid Precursor Protein (APP) fragments involve plaque formation when generated by γ-secretase. Mechanistic interrogation of γ-secretase substrate selection has been historically constrained by the hydrophobic and detergent-sensitive nature of its transmembrane core. Here, we generate the first fully water-soluble QTY-engineered analogue of the γ-secretase transmembrane scaffold and evaluate its ability to bind the APP transmembrane helix across 250ns molecular dynamics simulations. The QTY-code engineered protein preserved helical topology, orientation, and packing with 1.7 angstrom precision. MM-GBSA analyses yielded a mean binding free energy of -173.1±24.9 kcal/mol, supported by residue-level hotspot contributions and APP positions Arg61, Lys65, and Phe78. We further uncovered a dense cluster of co-evolving residues at the interface between the Presenilin-1 substrate-binding pocket and the flexible loop region of APP. These findings demonstrate that substrate recognition is preserved outside a lipid environment, establishing QTY-solubilized γ-secretase as a powerful platform for mechanistic dissection, mutational analysis, and biosensor development. Biological sciences/Chemical biology/Proteins Biological sciences/Drug discovery Protein Engineering Molecular Dynamics Simulation Evolutionary Couplings Water-Soluble Membrane Proteins Figures Figure 1 Figure 2 Figure 3 Introduction The γ-secretase complex, a multi-subunit, intramembrane aspartyl protease, is central to the regulated proteolysis of over 90 substrates, including amyloid precursor protein (APP) [1,2,3,4,5]. Sequential cleavage of APP’s C-terminal fragment by γ-secretase generates amyloid-β (Aβ) peptides, whose aggregation into oligomeric and fibrillar assemblies constitutes a defining characteristic of Alzheimer’s disease (AD) [6,7]. Despite extensive structural studies, the molecular determinants governing substrate recognition, helix-helix packing, and conformational transitions leading to catalysis remain incompletely understood [1,2,3]. This is partly due to the native lipid environment imposing significant experimental barriers that hinder high-fidelity modelling [8,9,10]. Membrane proteins constitute nearly one-third of all encoded gene products yet remain chronically underrepresented in structural and biophysical databases due to their intrinsic hydrophobicity and dependence on lipid bilayer environments [8,9,10,11]. Their α-helical transmembrane (TM) segments exhibit strong anisotropic solvation forces that severely complicate the recombinant expression, purification, and high-resolution characterization of these proteins [8,9,10,11]. Detergent micelles, amphipols, and nanodisc reconstitution strategies only partially alleviate these constraints and frequently distort the native thermodynamics, obscure functionally critical interfaces, or destabilize labile multiprotein assemblies [8,9,10,11]. The QTY code (developed by Zhang and colleagues) provides a powerful generalizable strategy to overcome these challenges [10]. By substituting hydrophobic TM residues (L, I, V, F) with their hydrophilic yet sterically compatible counterparts Q, T, and Y, QTY variants preserve secondary structure, helix geometry, and overall supramolecular topology while becoming fully water-soluble [10]. Multiple studies have demonstrated that QTY-designed receptors maintain native-like fold stability and ligand-binding behavior, despite extensive sequence alterations [10,12,13,14,15,16]. The QTY-code is previously utilized to generate water-soluble analogs of neurological transporters and receptors [13,14,15,16]. Crucially, these variants can be produced without detergents, enabling rapid structural sampling, large-scale mutagenesis, and biophysical assays that are inaccessible to native membrane proteins. Here, we applied the QTY-design methodology to generate, for the first time, a fully water-soluble variant of the γ-secretase TM scaffold to investigate its interactions with APP-derived helices. Using 250ns full-atom molecular dynamics (MD) simulations, trajectory analyses, and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) free-energy calculations, we systematically evaluated the capacity of the γ-secretase QTY variant to recognize and bind the APP transmembrane fragment. Our study demonstrates that it is possible to decouple functional interactions from membrane mechanics in one of the most complex protease systems associated with neurodegeneration. This work provides mechanistic insights into APP-γ-secretase recognition beyond the constraints of the membrane, offering a tractable model system for variant analysis, biosensor development, and future engineering of Alzheimer’s disease-relevant interactions. By revealing the structural determinants that remain robust under QTY solubilization, our study also highlights the broader applicability of water-soluble TM mimetics for dissecting the molecular evolution, dynamics, and specificity of membrane protein complexes. Results QTY-code Preserves Structural Integrity of the γ-Secretase Transmembrane Scaffold In this study, we employed the amyloid precursor protein (APP) sequence as resolved in the the human γ-secretase-APP complex reported by Zhou et al. (2019), which provides a structurally validated model of substrate engagement within the catalytic pore of γ-secretase [2]. To investigate how hydrophobic-to-hydrophilic substitutions reshape the physicochemical profile of APP while preserving critical interaction determinants, we systematically applied the QTY rational design rules across the gamma secretase regions. Importantly, residues identified as direct contact points with APP based on structural analyses were deliberately exempted from QTY substitutions. This exclusion ensured that side-chain features contributing to substrate positioning, catalytic alignment, and helix unwinding within the γ-secretase active site were preserved without perturbation. The introduction of QTY substitutions yielded a fully water-soluble analogue of the γ-secretase transmembrane core while preserving its native architecture with remarkable fidelity. The engineered complex exhibited a mean Cα root-mean-square deviation of 1.79Å relative to the membrane-embedded native structure (Figure 1). Helical curvature and overall supramolecular packing remained similar, deviating by less than 3Å from their native counterparts. Pairwise sequence alignment showed 77.31% identity, with substitutions confined primarily to predicted transmembrane helices while catalytic residues were retained. Biochemical property analysis demonstrated that PS1QTY exhibits nearly identical molecular weight and an unchanged isoelectric point (pI = 5.60) relative to the native protein, reflecting the fact that QTY substitutions alter hydrophobicity without introducing additional charged residues. These observations confirm that QTY solubilization does not collapse or distort the transmembrane topology but instead maintains a conformational ensemble that is close to the functional membrane state (Figure 2). APP Transmembrane Helix Binds to the Soluble γ-Secretase Variant The APP transmembrane helix consistently recognized and docked into the substrate-entry groove of the soluble γ-secretase variant. This dynamic optimization was quantitatively captured by MM-GBSA trajectory analysis, which revealed a progressive strengthening of the interaction: the binding energy deepened from approximately -117 kcal/mol in the initial docking stages to nearly -200 kcal/mol in the first 50ns (Mean ΔGbind =−173.1±24.9 kcal/mol). Energetic decomposition of the driving forces indicates a binding mechanism that is fundamentally distinct from the purely hydrophobic effect typically seen in lipid bilayers. In this solubilized system, substrate affinity is powered by a robust combination of specific electrostatic attraction (ΔE elec=−329.6±122.8 kcal/mol) and precise shape complementarity evidenced by favorable Van der Waals packing (ΔE vdw=−280.8±21.1 kcal/mol). These strong attractive terms are sufficient to overcome the substantial desolvation penalty (ΔG solv ≈+437 kcal/mol) associated with burying the polar QTY residues. This data confirms that the QTY-code re-engineers the recognition interface, replacing non-specific hydrophobic burial with high-affinity salt-bridge networks and hydrogen bonds while preserving the native docking topology. Energetic Analyses Reveal Conservation of Substrate-Recognition Hotspots Generalized Born Surface Area (MM-GBSA) calculations revealed that the stability of the APP-γ-secretase complex is driven by a robust network of electrostatic and polar solvation interactions (Figure 3, Supplementary Figure S1). Decomposition of the total energy identified hotspot residues that contribute significantly to the complex's stability. In the γ-secretase scaffold, residues Asp307, and Glu195 emerged as the primary destabilizers, while Phe98, Tyr162 and Met155 contributed to binding. On the ligand side, Arg61, Lys65, and Phe68 exerted the strongest stabilizing effects (Supplementary Figure S2). Interestingly, Glu72 and Glu80 were strong destabilizers. These results indicate that the \"QTY code\" effectively replaces the microenergetic landscape of hydrophobic exclusion with one defined by precise charge complementarity and solvation dynamics. Persistent interfacial contacts across the QTY-engineered complex were maintained through the electrostatic hotspots identified in the decomposition analysis. These pairs formed a stable electrostatic clamp that anchored the helix within the binding groove. Additional stabilization was provided by QTY-derived polar residues, including Gln198 and Gln95 on the receptor, which facilitated hydrogen bonding networks that substituted for the native van der Waals contacts. These findings demonstrate that while the chemical nature of the interface has shifted the structural specificity and shape complementarity required for substrate recognition are rigorously preserved. Evolutionary Couplings Reveal a Co-evolved Recognition Interface Between Presenilin-1 and the APP Substrate To investigate the structural constraints governing the interaction between the gamma-secretase catalytic subunit, Presenilin-1 (PS1), and its substrate, the Amyloid Precursor Protein (APP), we mapped high-scoring evolutionary couplings (ECs) onto the structural complex (Figure 1). Molecular evolution of transmembrane proteins is non-linear and complex [17,18,19] and involves co-evolutionary dependencies [18,19,20,21]. The analysis focused specifically on the interface between the transmembrane domains of PS1 and the C-terminal fragment of APP. The structural mapping identifies a significant cluster of co-evolving residues at the binding interface. Most notably, we observed a distinct enrichment of evolutionary couplings (Figure 3) surrounding the flexible loop region of APP. This region is critical for substrate positioning prior to cleavage. The high density of significant ECs at this specific junction indicates that the PS1 residues comprising the substrate-binding pocket have co-evolved with the APP loop region. Conversely, regions of the APP transmembrane helix distal to the recognition loop show fewer coupling constraints, suggesting that the primary evolutionary pressure is exerted on the loop-recognition interface to ensure the fidelity of amyloidogenic cleavage. Discussion The solubility and structural stability of the engineered complex facilitate analyses that are difficult or impossible in membrane contexts. Beyond mechanistic insights, the water-soluble model provides an immediately usable platform for designing biosensors capable of reporting helix-docking events via fluorescence, FRET, or other biophysical readouts. Because the QTY architecture preserves helix-helix recognition rules and substrate-specific interactions, the system also enables comparative and evolutionary analyses to explore how sequence variation influences transmembrane interaction landscapes. The water-soluble γ-secretase model provides several advantages for both basic research and translational science. First, it offers a tractable platform for analyzing the molecular consequences of familial Alzheimer’s disease mutations in APP and PSEN1/2, enabling rapid computational or experimental evaluation without the need for detergents, nanodiscs, or proteoliposomes. Second, the soluble scaffold is inherently compatible with high-sensitivity biophysical techniques (such as NMR, single-molecule fluorescence microscopy, and FRET-based biosensors) that are typically incompatible with native γ-secretase due to its membrane dependence. Third, the system allows systematic application of the user’s own computational frameworks, to quantify how mutations perturb local and global interaction landscapes. Perhaps most importantly, this work establishes a methodological precedent. By showing that a multisubunit intramembrane protease can be rendered soluble while maintaining native-like function, we open the possibility of applying QTY engineering to membrane complexes traditionally considered experimentally intractable. The ability to decouple functional interactions from membrane mechanics is likely to have profound implications for studying transporter evolution, receptor oligomerization, ion-channel activation, and membrane-associated signaling networks. As QTY-engineered systems become more widely adopted, they may ultimately redefine how membrane-protein biology is conceptualized, enabling the field to interrogate mechanistic questions previously restricted by lipid dependency. Methods AlphaFold predictions Isoform structures were predicted using the AlphaFold3 server [22]. We utilized the full-length amyloid precursor protein (APP) transmembrane sequence derived from the high-resolution cryo-EM structure of the human γ-secretase-APP complex [2]. To preserve the structural interface, residues identified via Chimera X [23] as being within 4 Å of APP and making direct contact with presenilin-1 were explicitly excluded from QTY substitutions. The reliability of the predicted models was assessed through structural superposition with experimental structures using PyMOL (https://pymol.org/2/), with similarity quantified by root mean square deviation (RMSD). Although AlphaFold output conformations can vary, no deviations that would hinder direct comparison were observed. Molecular dynamics simulations Molecular dynamics simulations were executed on AlphaFold-predicted membrane structures using GROMACS 2024.3 [24]. Computational analyses utilized a cluster of three Google Colab instances, equipped with an NVIDIA L4 GPU, 126 GB of VRAM, 318 GB of RAM, and Intel® Xeon® CPUs [25]. To maximize efficiency, the software was recompiled with CUDA support, enabling parallelization across multiple cores [25]. All-atom systems were constructed using the CHARMM-GUI Builder [15,16,17,18,26,27]. System neutrality was established by adding K⁺ and Cl⁻ ions to an ionic strength of 0.15 M, determined through 2,000 steps of Monte Carlo simulation using a primitive ion model. The CHARMM36m all-atom force field was applied throughout [27]. Following the solvation and ionization of the system, the potential energy was minimized to remove steric clashes and relax the system geometry using the steepest descent algorithm. The minimization convergence criterion was set to a maximum force (Fmax) of less than 1000.0 kJ/mol, with a maximum of 5000 steps allowed for the process. During this phase, harmonic position restraints were applied to the protein backbone and side chains with force constants of 400.0 kJ/mol. Following minimization, the system underwent equilibration in the canonical (NVT) ensemble to stabilize the temperature. This phase consisted of a 125ps run (125,000 steps with a time step of 1fs). The temperature was maintained at 303.15 K using the V-rescale thermostat (a modified Berendsen thermostat with a stochastic term), applied separately to the solute and solvent groups with a coupling time constant (τt) of 1.0ps. Harmonic position restraints were maintained on the heavy atoms during this equilibration phase. Velocities were generated from a Maxwell-Boltzmann distribution at 303.15 K. Production MD simulations were carried out for 250ns with frames saved every 0.5ns, consistent with our earlier studies [16]. The production phase was conducted in the isothermal-isobaric (NPT) ensemble for a total duration of 250ns. The equations of motion were integrated using the leap-frog algorithm with a time step of 2fs. The temperature was maintained at 303.15 K using the V-rescale thermostat (τ t=1.0 ps). Pressure control was achieved using the C-rescale barostat, employing isotropic coupling to a reference pressure of 1.0 bar. Bond lengths involving hydrogen atoms were constrained using the LINCS (Linear Constraint Solver) algorithm, permitting the use of the 2fs time step. Long-range electrostatic interactions were computed using the Particle Mesh Ewald (PME) method with a real-space cutoff of 1.2nm. Short-range van der Waals interactions were calculated using a Lennard-Jones potential with a cutoff of 1.2nm, employing a force-switch modifier starting at 1.0 nm to smoothly decay the forces to zero. Periodic boundary conditions were applied in all three directions, and center-of-mass motion was removed linearly every 100 steps. We evaluated system stability and molecular dynamics using several metrics. Trajectory analysis began by calculating the Root Mean Square Deviation (RMSD) of the protein backbone and the radius of gyration (Rg) with the GROMACS rms and gyrate modules, respectively, to assess overall stability and compactness. Residue flexibility was quantified via the Root Mean Square Fluctuation (RMSF). We also determined the Solvent Accessible Surface Area (SASA) for protein side chains using the gmx sasa tool, applying a standard probe radius of 1.4 Angstroom [28]. Finally, the equilibrated trajectories were analyzed to estimate the binding free energy. Binding Free Energy Calculations Binding affinities were estimated using the Molecular Mechanics Genralized-Boltzmann Surface Area (MMGBSA) approach tailored for solvent systems, implemented via gmx_MMPBSA [29,30]. We assigned dielectric constants of 7.0, 4.0, and 80.0 to the membrane, solute, and solvent, respectively. Electrostatic interactions were computed using the particle-particle particle-mesh (P3M) algorithm. To isolate specific contributions, we applied per-residue decomposition (idecomp = 2) for both electrostatic and van der Waals components. Final binding energy values reflect averages over the trajectory, with the standard error of the mean (SEM) determined through uncertainty propagation. Interfacial contacts within a 4 Å cutoff were mapped using gmx_MMPBSA_ana and visualized in UCSF ChimeraX [23], while quantitative plots were produced using Grace (https://plasma-gate.weizmann.ac.il/Grace/). Co-evolutionary Profilling We computed Evolutionary Couplings (ECs) using the EVcouplings server, employing a maximum entropy model constrained by multiple sequence alignment (MSA) statistics [31]. To ensure robust comparisons independent of database size or sequence length, we utilized length-normalized bitscores. Alignment reliability was evaluated using the ratio of effective sequences to protein length (Neff /L), where a value exceeding 1.0 signifies a high-quality run. For this analysis, the Neff/L ratio was calculated at 1.84. Declarations Ethics Approval Ethics approval was not required for this computational study as it did not involve animal subjects, human participants, and identifiable data. Consent to participate Not applicable. This computational study did not involve human participants. Consent for publication Not applicable. This computational study did not involve human participants. Competing financial interests None. Funding The author(s) received no specific funding for this work. References Guo X, Li H, Yan C, Lei J, Zhou R, Shi Y (2024) Molecular mechanism of substrate recognition and cleavage by human γ-secretase. Science (New York, N.Y.), 384(6700), 1091-1095. https://doi.org/10.1126/science.adn5820 Zhou R, Yang G, Guo X, Zhou Q, Lei J, Shi Y (2019) Recognition of the amyloid precursor protein by human γ-secretase. Science (New York, N.Y.), 363(6428), eaaw0930. https://doi.org/10.1126/science.aaw0930 Hogl S, Kuhn PH, Colombo A, Lichtenthaler SF (2011) Determination of the proteolytic cleavage sites of the amyloid precursor-like protein 2 by the proteases ADAM10, BACE1 and γ-secretase. PloS one, 6(6), e21337. https://doi.org/10.1371/journal.pone.0021337 Hitzenberger M, Zacharias M (2019) γ-Secretase Studied by Atomistic Molecular Dynamics Simulations: Global Dynamics, Enzyme Activation, Water Distribution and Lipid Binding. Frontiers in chemistry, 6, 640. https://doi.org/10.3389/fchem.2018.00640 Bhattarai A, Devkota S, Bhattarai S, Wolfe MS, Miao Y (2020) Mechanisms of γ-Secretase Activation and Substrate Processing. ACS Cent Sci. 6(6):969-983. https://doi.org/10.1021/acscentsci.0c00296 Xu TH, Yan Y, Kang Y, Jiang Y, Melcher K, Xu HE (2016) Alzheimer's disease-associated mutations increase amyloid precursor protein resistance to γ-secretase cleavage and the Aβ42/Aβ40 ratio. Cell discovery, 2, 16026. https://doi.org/10.1038/celldisc.2016.26 Zhang, YW, Thompson R, Zhang H, Xu H (2011) APP processing in Alzheimer's disease. Molecular brain, 4, 3. https://doi.org/10.1186/1756-6606-4-3 Thoma, J, Burmann BM (2020) Fake It 'Till You Make It-The Pursuit of Suitable Membrane Mimetics for Membrane Protein Biophysics. International journal of molecular sciences, 22(1), 50. https://doi.org/10.3390/ijms22010050 Hoi KK, Bada Juarez, JF, Judge PJ, Yen HY, Wu D, Vinals J, Taylor GF, Watts A, Robinson CV (2021) Detergent-free Lipodisq Nanoparticles Facilitate High-Resolution Mass Spectrometry of Folded Integral Membrane Proteins. Nano letters, 21(7), 2824-2831. https://doi.org/10.1021/acs.nanolett.0c04911 Zhang S, Tao F, Qing R, Tang H, Skuhersky M et al (2018) QTY code enables design of detergent-free chemokine receptors that retain ligand-binding activities. Proceedings of the National Academy of Sciences of the United States of America, 115(37), E8652-E8659. https://doi.org/10.1073/pnas.1811031115 Carlson ML, Young JW, Zhao Z, Fabre L, Jun D, Li J, Li J, Dhupar HS, Wason I, Mills AT, Beatty JT, Klassen JS, Rouiller I, Duong F (2018) The Peptidisc, a simple method for stabilizing membrane proteins in detergent-free solution. eLife, 7, e34085. https://doi.org/10.7554/eLife.34085 Sajeev-Sheeja A, Karagöl, A, Karagöl T, Zhang S (2025) Molecular dynamics simulations and structural bioinformatics of bacterial integral alpha-helical membrane enzymes and their AlphaFold2-predicted water-soluble QTY analogues. Molecular Simulation , vol.51, no.15, 984-998. http://doi.org/10.1080/08927022.2025.2562932 Karagöl A, Karagöl, T, Smorodina, E, Zhang S (2024) Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F. PloS one, 19(4), e0289644. https://doi.org/10.1371/journal.pone.0289644 Karagöl T, Karagöl A, Zhang S (2024) Structural bioinformatics studies of serotonin, dopamine and norepinephrine transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F. PloS one, 19(3), e0300340. https://doi.org/10.1371/journal.pone.0300340 Karagöl A, Karagöl, T, Zhang S (2024) Molecular Dynamic Simulations Reveal that Water-Soluble QTY-Variants of Glutamate Transporters EAA1, EAA2 and EAA3 Retain the Conformational Characteristics of Native Transporters. Pharmaceutical research, 41(10), 1965-1977. https://doi.org/10.1007/s11095-024-03769-0 Johnsson F, Karagöl T, Karagöl A, Zhang S (2024) Structural bioinformatic study of six human olfactory receptors and their AlphaFold3 predicted water-soluble QTY variants and OR1A2 with an odorant octanoate and TAAR9 with spermidine. QRB discovery, 6, e2. https://doi.org/10.1017/qrd.2024.18 Karagöl T, Karagöl A (2025) pH-Dependent Membrane Binding Specificity of Synaptogyrins 1-3 Provides Mechanistic Insights into Synaptic Vesicle Regulation and Neurological Disease. bioRxiv. https://doi.org/10.1101/2025.03.03.641025 Karagöl T, Karagöl A, Zhang S (2025) Co-evolution of alpha-helical transmembrane protein residues: large-scale variant profiling and complete mutational landscape of 2277 known PDB entries representing 504 unique human protein sequences. Journal of Molecular Evolution, 1-19. https://doi.org/10.1007/s00239-025-10262-8 Karagöl, A., & Karagöl, T. (2025). Adaptation to Solvent Environment in Toll-like Receptor 5: A Comparative Evolutionary Analysis of Membrane-bound and Soluble Forms in Epinephelus coioides. bioRxiv, 2025-02. https://doi.org/10.1101/2025.02.28.640895 Fuchs A, Martin-Galiano AJ, Kalman M, Fleishman S, Ben-Tal N, Frishman D (2007) Co-evolving residues in membrane proteins. Bioinformatics, 23(24), 3312-3319. https://doi.org/10.1093/bioinformatics/btm515 Zeng B, Hönigschmid P, Frishman D (2019) Residue co-evolution helps predict interaction sites in α-helical membrane proteins. Journal of Structural Biology, 206(2), 156-169. https://doi.org/10.1016/j.jsb.2019.02.009 Jumper J, Evans, R, Pritzel A, Green T et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2 Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, Ferrin TE (2023). UCSF ChimeraX: Tools for structure building and analysis. Protein Science, 32(11), p.e4792. https://doi.org/10.1002/pro.4792 Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX , 1 , pp.19-25. https://doi.org/10.1016/j.softx.2015.06.001 Karagöl T, Karagöl A (2024) Benchmarking GROMACS on Optimized Colab Processors and the Flexibility of Cloud Computing for Molecular Dynamics. bioRxiv , pp.2024-11. https://doi.org/10.1101/2024.11.14.623563 Jo S, Kim T, Iyer VG, Im W (2008) CHARMM‐GUI: a web‐based graphical user interface for CHARMM. Journal of computational chemistry , 29 (11), pp.1859-1865. https://doi.org/10.1002/jcc.20945 Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL, Grubmüller H, MacKerell Jr AD, (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nature methods , 14 (1), pp.71-73. https://doi.org/10.1038/nmeth.4067 Eisenhaber F, Lijnzaad P, Argos P, Sander C, Scharf M (1995) The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. Journal of computational chemistry , 16 (3), pp.273-284. https://doi.org/10.1002/jcc.540160303 Bill R, Miller T, Dwight McGee, Jason M Swails, Nadine Homeyer, Holger Gohlke, Adrian E Roitberg (2012) MMPBSA. py: An Efficient Program for End-State Free Energy Calculations. Journal of Chemical Theory and Computation, 2012 8 (9), 3314-3321. https://doi.org/10.1021/ct300418h Valdés-Tresanco MS, Valdés-Tresanco ME, Valiente PA, Moreno E (2021) gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. Journal of Chemical Theory and Computation, 17 (10), 6281-6291. https://doi.org/10.1021/acs.jctc.1c00645 Thomas A Hopf, Anna G Green, Benjamin Schubert, Sophia Mersmann, Charlotta P I Schärfe, John B Ingraham, Agnes Toth-Petroczy, Kelly Brock, Adam J Riesselman, Perry Palmedo, Chan Kang, Robert Sheridan, Eli J Draizen, Christian Dallago, Chris Sander, Debora S Marks (2019) The EVcouplings Python framework for coevolutionary sequence analysis, Bioinformatics, Volume 35, Issue 9, 1 May 2019, Pages 1582-1584, https://doi.org/10.1093/bioinformatics/bty862 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFile.pdf Supplementary File Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8320072\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Analysis\",\"associatedPublications\":[],\"authors\":[{\"id\":558101196,\"identity\":\"4a74c5de-0d1c-47d8-bb22-245d866fdd01\",\"order_by\":0,\"name\":\"Alper Karagöl\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACdjCZAOE8qAASzMwN+LUwI2tJOAMSYSRFS2IbiCSghb+Z9+DjCoY0efP24xcfJM6rjeZvB2r5UbENpxaJw3zJhmcYcgznnMkpNkjcdjx3xmHGBsaeM7dxW3OYx0yygaGCcQZDTppE4rZjuQ1ALcyMbbi1yB/mMf8J1GI/g/8NUMucY7nzCWkxANoCdHtO4gyJ9GMSiQ01uRsIaTEE+kWywSAteYbEG2aDhGMHcjcCtRzE5xe5470HPzZUJNvO4E9/+OBDTV3uvPOHDz74UYHH+ww8IOeBGSDyMFjsAB71UC1gwP4ASNThVzwKRsEoGAUjEgAAzYBaV812TBgAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0009-0001-7864-0732\",\"institution\":\"Istanbul University, Istanbul Medical Faculty\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Alper\",\"middleName\":\"\",\"lastName\":\"Karagöl\",\"suffix\":\"\"},{\"id\":558101197,\"identity\":\"718e5a62-9cc0-4e56-83f4-954475a31467\",\"order_by\":1,\"name\":\"Taner Karagöl\",\"email\":\"\",\"orcid\":\"https://orcid.org/0009-0005-1011-7661\",\"institution\":\"Istanbul University, Istanbul Medical Faculty\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Taner\",\"middleName\":\"\",\"lastName\":\"Karagöl\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-12-09 16:57:02\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8320072/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8320072/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":98044727,\"identity\":\"27014103-1e54-4c66-96c1-549312289f62\",\"added_by\":\"auto\",\"created_at\":\"2025-12-12 08:01:47\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":214368,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eComparative bioinformatics analysis of the QTY variant of presenilin-1\\u003c/strong\\u003e. a) Superposed structures of the QTY-variant (cyan) and presenilin-1 (green). Sequence alignment (b) with variations highlighted, and their sequence characteristics (c).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8320072/v1/a92a4943a7f77c51dd39330f.png\"},{\"id\":98044723,\"identity\":\"7a4dd7de-8303-43b3-9551-8d192a81d163\",\"added_by\":\"auto\",\"created_at\":\"2025-12-12 08:01:46\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":658559,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe water-soluble QTY-variant of presenilin-1 (cyan) complexed with APP fragment (red).\\u003c/strong\\u003e The equilibrated complex is in solution with Monte-Carlo placed K+ CL− ions (neutralizing, concentration = 0.15 M). In both the front view (a) and the 90° rotated side view (b), PS1 (cyan) maintains its characteristic multi-pass helical architecture, while APP (red) inserts as an extended helix-like segment that traverses the catalytic core region of PS1 in a membrane-mimetic solvent environment. Water molecules and ions (gray spheres) surround the complex, confirming that the system remains fully solvated throughout the simulation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8320072/v1/945d7256fac2a20dca8920ab.png\"},{\"id\":98044725,\"identity\":\"44be238a-3235-426f-bcf1-202dd97e3eeb\",\"added_by\":\"auto\",\"created_at\":\"2025-12-12 08:01:47\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":219371,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBinding interface of the APP fragment and QTY-variant complex.\\u003c/strong\\u003e a) The QTY-variant (cyan) and the APP (red) with the contributing residues mapped. b) MMGBSA binding energy calculations of the complex through 50ns equilibrated simulation. c) the APP substrate fragment is shown in red. Purple spheres indicate residues involved in the top-ranking evolutionary couplings (co-evolved residue pairs). The visualization highlights a dense network of co-evolving residues at the interface where PS1 recognizes the flexible loop region of APP, indicating a highly conserved recognition motif essential for substrate processing.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8320072/v1/9df4d039c7ddbb68cf2f138c.png\"},{\"id\":98777106,\"identity\":\"f8fb7e8a-1f47-4eb5-8e39-11fd522807a2\",\"added_by\":\"auto\",\"created_at\":\"2025-12-22 12:25:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1545121,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8320072/v1/2afd91a7-a07d-45da-bed2-ddf7365add91.pdf\"},{\"id\":98044724,\"identity\":\"db180f2c-b6d3-4c69-b41d-55cc2448cb1c\",\"added_by\":\"auto\",\"created_at\":\"2025-12-12 08:01:47\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1586305,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary File\",\"description\":\"\",\"filename\":\"SupplementaryFile.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8320072/v1/d3053ff9dfeefed640fd0696.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Capture of Amyloid Precursor Protein Fragments by an Engineered Water-Soluble γ-Secretase Variant\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe γ-secretase complex, a multi-subunit, intramembrane aspartyl protease, is central to the regulated proteolysis of over 90 substrates, including amyloid precursor protein (APP) [1,2,3,4,5]. Sequential\\u0026nbsp;cleavage\\u0026nbsp;of APP’s C-terminal fragment by γ-secretase\\u0026nbsp;generates\\u0026nbsp;amyloid-β (Aβ) peptides, whose aggregation into oligomeric and fibrillar assemblies constitutes a defining characteristic of Alzheimer’s disease (AD) [6,7]. Despite extensive structural\\u0026nbsp;studies, the molecular determinants governing substrate recognition, helix-helix packing, and conformational transitions leading to catalysis remain incompletely understood [1,2,3]. This is partly due to the native lipid environment imposing significant experimental barriers\\u0026nbsp;that\\u0026nbsp;hinder high-fidelity modelling [8,9,10].\\u003c/p\\u003e\\n\\u003cp\\u003eMembrane proteins constitute nearly one-third of all encoded gene products yet remain chronically underrepresented in structural and biophysical databases due to their intrinsic hydrophobicity and dependence on lipid bilayer environments [8,9,10,11]. Their α-helical transmembrane (TM) segments exhibit strong anisotropic solvation forces that severely complicate the recombinant expression, purification, and high-resolution characterization of these proteins [8,9,10,11]. Detergent micelles, amphipols, and nanodisc reconstitution strategies only partially alleviate these constraints and frequently distort the native thermodynamics, obscure functionally critical interfaces, or destabilize labile multiprotein assemblies [8,9,10,11]. The QTY code (developed by Zhang and colleagues) provides a powerful generalizable strategy to overcome these challenges [10]. By substituting hydrophobic TM residues (L, I, V, F) with their hydrophilic yet sterically compatible counterparts Q, T, and Y, QTY variants preserve secondary structure, helix geometry, and overall supramolecular topology while becoming fully water-soluble [10]. Multiple studies have demonstrated that QTY-designed receptors maintain native-like fold stability and ligand-binding behavior, despite extensive sequence alterations [10,12,13,14,15,16]. The QTY-code is previously utilized \\u0026nbsp;to generate water-soluble analogs of neurological transporters and receptors [13,14,15,16]. Crucially, these variants can be produced without detergents, enabling rapid structural sampling, large-scale mutagenesis, and biophysical assays that are inaccessible to native membrane proteins.\\u003c/p\\u003e\\n\\u003cp\\u003eHere, we applied the QTY-design methodology to generate, for the first time, a fully water-soluble variant of the γ-secretase TM scaffold to investigate its interactions with APP-derived helices. Using 250ns full-atom molecular dynamics (MD) simulations, trajectory analyses, and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) free-energy calculations, we systematically evaluated the capacity of the γ-secretase QTY variant to recognize and bind the APP transmembrane fragment. Our study demonstrates that it is possible to decouple functional interactions from membrane mechanics in one of the most complex protease systems associated with neurodegeneration.\\u003c/p\\u003e\\n\\u003cp\\u003eThis work provides mechanistic insights into APP-γ-secretase recognition beyond the constraints of the membrane, offering a tractable model system for variant analysis, biosensor development, and future engineering of Alzheimer’s disease-relevant interactions. By revealing the structural determinants that remain robust under QTY solubilization, our study also highlights the broader applicability of water-soluble TM mimetics for dissecting the molecular evolution, dynamics, and specificity of membrane protein complexes.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eQTY-code Preserves Structural Integrity of the γ-Secretase Transmembrane Scaffold\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we employed the amyloid precursor protein (APP) sequence as resolved in the the human γ-secretase-APP complex reported by Zhou et al. (2019), which provides a structurally validated model of substrate engagement within the catalytic pore of γ-secretase [2]. To investigate how hydrophobic-to-hydrophilic substitutions reshape the physicochemical profile of APP while preserving critical interaction determinants, we systematically applied the QTY rational design rules across the gamma secretase regions. Importantly, residues identified as direct contact points with APP based on structural analyses were deliberately exempted from QTY substitutions. This exclusion ensured that side-chain features contributing to substrate positioning, catalytic alignment, and helix unwinding within the γ-secretase active site were preserved without perturbation.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe introduction of QTY substitutions yielded a fully water-soluble analogue of the γ-secretase transmembrane core while preserving its native architecture with remarkable fidelity. The engineered complex exhibited a mean Cα root-mean-square deviation of 1.79Å relative to the membrane-embedded native structure (Figure 1). Helical curvature and overall supramolecular packing remained similar, deviating by less than 3Å from their native counterparts. Pairwise sequence alignment showed 77.31% identity, with substitutions confined primarily to predicted transmembrane helices while catalytic residues were retained. Biochemical property analysis demonstrated that PS1QTY exhibits nearly identical molecular weight and an unchanged isoelectric point (pI = 5.60) relative to the native protein, reflecting the fact that QTY substitutions alter hydrophobicity without introducing additional charged residues. These observations confirm that QTY solubilization does not collapse or distort the transmembrane topology but instead maintains a conformational ensemble that is close to the functional membrane state (Figure 2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAPP Transmembrane Helix Binds to the Soluble γ-Secretase Variant\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe APP transmembrane helix consistently recognized and docked into the substrate-entry groove of the soluble γ-secretase variant. This dynamic optimization was quantitatively captured by MM-GBSA trajectory analysis, which revealed a progressive strengthening of the interaction: the binding energy deepened from approximately -117 kcal/mol in the initial docking stages to nearly -200 kcal/mol in the first 50ns (Mean ΔGbind =−173.1±24.9 kcal/mol). Energetic decomposition of the driving forces indicates a binding mechanism that is fundamentally distinct from the purely hydrophobic effect typically seen in lipid bilayers. In this solubilized system, substrate affinity is powered by a robust combination of specific electrostatic attraction (ΔE elec=−329.6±122.8 kcal/mol) and precise shape complementarity evidenced by favorable Van der Waals packing (ΔE vdw=−280.8±21.1 kcal/mol). These strong attractive terms are sufficient to overcome the substantial desolvation penalty (ΔG solv ≈+437 kcal/mol) associated with burying the polar QTY residues. This data confirms that the QTY-code re-engineers the recognition interface, replacing non-specific hydrophobic burial with high-affinity salt-bridge networks and hydrogen bonds while preserving the native docking topology.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEnergetic Analyses Reveal Conservation of Substrate-Recognition Hotspots\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGeneralized Born Surface Area (MM-GBSA) calculations revealed that the stability of the APP-γ-secretase complex is driven by a robust network of electrostatic and polar solvation interactions (Figure 3, Supplementary Figure S1). Decomposition of the total energy identified hotspot residues that contribute significantly to the complex's stability. In the γ-secretase scaffold, residues Asp307, and Glu195 emerged as the primary destabilizers, while Phe98, Tyr162 and Met155 contributed to binding. On the ligand side, Arg61, Lys65, and Phe68 exerted the strongest stabilizing effects (Supplementary Figure S2). Interestingly, Glu72 and Glu80 were strong destabilizers. These results indicate that the \\\"QTY code\\\" effectively replaces the microenergetic landscape of hydrophobic exclusion with one defined by precise charge complementarity and solvation dynamics.\\u003c/p\\u003e\\n\\u003cp\\u003ePersistent interfacial contacts across the QTY-engineered complex were maintained through the electrostatic hotspots identified in the decomposition analysis. These pairs formed a stable electrostatic clamp that anchored the helix within the binding groove. Additional stabilization was provided by QTY-derived polar residues, including Gln198 and Gln95 on the receptor, which facilitated hydrogen bonding networks that substituted for the native van der Waals contacts. These findings demonstrate that while the chemical nature of the interface has shifted the structural specificity and shape complementarity required for substrate recognition are rigorously preserved.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEvolutionary Couplings Reveal a Co-evolved Recognition Interface Between Presenilin-1 and the APP Substrate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate the structural constraints governing the interaction between the gamma-secretase catalytic subunit, Presenilin-1 (PS1), and its substrate, the Amyloid Precursor Protein (APP), we mapped high-scoring evolutionary couplings (ECs) onto the structural complex (Figure 1).\\u0026nbsp;Molecular evolution of transmembrane proteins is non-linear and complex [17,18,19] and involves co-evolutionary dependencies [18,19,20,21]. The analysis focused specifically on the interface between the transmembrane domains of PS1 and the C-terminal fragment of APP.\\u003c/p\\u003e\\n\\u003cp\\u003eThe structural mapping identifies a significant cluster of co-evolving residues at the binding interface. Most notably, we observed a distinct enrichment of evolutionary couplings (Figure 3) surrounding the flexible loop region of APP. This region is critical for substrate positioning prior to cleavage. The high density of significant ECs at this specific junction indicates that the PS1 residues comprising the substrate-binding pocket have co-evolved with the APP loop region. Conversely, regions of the APP transmembrane helix distal to the recognition loop show fewer coupling constraints, suggesting that the primary evolutionary pressure is exerted on the loop-recognition interface to ensure the fidelity of amyloidogenic cleavage.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe solubility and structural stability of the engineered complex facilitate analyses that are difficult or impossible in membrane contexts. Beyond mechanistic insights, the water-soluble model provides an immediately usable platform for designing biosensors capable of reporting helix-docking events via fluorescence, FRET, or other biophysical readouts. Because the QTY architecture preserves helix-helix recognition rules and substrate-specific interactions, the system also enables comparative and evolutionary analyses to explore how sequence variation influences transmembrane interaction landscapes.\\u003c/p\\u003e\\n\\u003cp\\u003eThe water-soluble γ-secretase model provides several advantages for both basic research and translational science. First, it offers a tractable platform for analyzing the molecular consequences of familial Alzheimer’s disease mutations in APP and PSEN1/2, enabling rapid computational or experimental evaluation without the need for detergents, nanodiscs, or proteoliposomes. Second, the soluble scaffold is inherently compatible with high-sensitivity biophysical techniques (such as NMR, single-molecule fluorescence microscopy, and FRET-based biosensors) that are typically incompatible with native γ-secretase due to its membrane dependence. Third, the system allows systematic application of the user’s own computational frameworks, to quantify how mutations perturb local and global interaction landscapes.\\u003c/p\\u003e\\n\\u003cp\\u003ePerhaps most importantly, this work establishes a methodological precedent. By showing that a multisubunit intramembrane protease can be rendered soluble while maintaining native-like function, we open the possibility of applying QTY engineering to membrane complexes traditionally considered experimentally intractable. The ability to decouple functional interactions from membrane mechanics is likely to have profound implications for studying transporter evolution, receptor oligomerization, ion-channel activation, and membrane-associated signaling networks. As QTY-engineered systems become more widely adopted, they may ultimately redefine how membrane-protein biology is conceptualized, enabling the field to interrogate mechanistic questions previously restricted by lipid dependency.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAlphaFold predictions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIsoform structures were predicted using the AlphaFold3 server [22]. We utilized the full-length amyloid precursor protein (APP) transmembrane sequence derived from the high-resolution cryo-EM structure of the human \\u0026gamma;-secretase-APP complex [2]. To preserve the structural interface, residues identified via Chimera X [23] as being within 4 \\u0026Aring; of APP and making direct contact with presenilin-1 were explicitly excluded from QTY substitutions. The reliability of the predicted models was assessed through structural superposition with experimental structures using PyMOL (https://pymol.org/2/), with similarity quantified by root mean square deviation (RMSD). Although AlphaFold output conformations can vary, no deviations that would hinder direct comparison were observed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMolecular dynamics simulations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMolecular dynamics simulations were executed on AlphaFold-predicted membrane structures using GROMACS 2024.3 [24]. Computational analyses utilized a cluster of three Google Colab instances, equipped with an NVIDIA L4 GPU, 126 GB of VRAM, 318 GB of RAM, and Intel\\u0026reg; Xeon\\u0026reg; CPUs [25]. To maximize efficiency, the software was recompiled with CUDA support, enabling parallelization across multiple cores [25]. All-atom systems were constructed using the CHARMM-GUI Builder [15,16,17,18,26,27]. System neutrality was established by adding K⁺ and Cl⁻ ions to an ionic strength of 0.15 M, determined through 2,000 steps of Monte Carlo simulation using a primitive ion model. The CHARMM36m all-atom force field was applied throughout [27].\\u003c/p\\u003e\\n\\u003cp\\u003eFollowing the solvation and ionization of the system, the potential energy was minimized to remove steric clashes and relax the system geometry using the steepest descent algorithm. The minimization convergence criterion was set to a maximum force (Fmax) of less than 1000.0 kJ/mol, with a maximum of 5000 steps allowed for the process. During this phase, harmonic position restraints were applied to the protein backbone and side chains with force constants of 400.0 kJ/mol. Following minimization, the system underwent equilibration in the canonical (NVT) ensemble to stabilize the temperature. This phase consisted of a 125ps run (125,000 steps with a time step of 1fs). The temperature was maintained at 303.15 K using the V-rescale thermostat (a modified Berendsen thermostat with a stochastic term), applied separately to the solute and solvent groups with a coupling time constant (\\u0026tau;t) of 1.0ps. Harmonic position restraints were maintained on the heavy atoms during this equilibration phase. Velocities were generated from a Maxwell-Boltzmann distribution at 303.15 K. Production MD simulations were carried out for 250ns with frames saved every 0.5ns, consistent with our earlier studies [16]. The production phase was conducted in the isothermal-isobaric (NPT) ensemble for a total duration of 250ns. The equations of motion were integrated using the leap-frog algorithm with a time step of 2fs. The temperature was maintained at 303.15 K using the V-rescale thermostat (\\u0026tau; t=1.0 ps). Pressure control was achieved using the C-rescale barostat, employing isotropic coupling to a reference pressure of 1.0 bar. Bond lengths involving hydrogen atoms were constrained using the LINCS (Linear Constraint Solver) algorithm, permitting the use of the 2fs time step. Long-range electrostatic interactions were computed using the Particle Mesh Ewald (PME) method with a real-space cutoff of 1.2nm. Short-range van der Waals interactions were calculated using a Lennard-Jones potential with a cutoff of 1.2nm, employing a force-switch modifier starting at 1.0 nm to smoothly decay the forces to zero. Periodic boundary conditions were applied in all three directions, and center-of-mass motion was removed linearly every 100 steps.\\u003c/p\\u003e\\n\\u003cp\\u003eWe evaluated system stability and molecular dynamics using several metrics. Trajectory analysis began by calculating the Root Mean Square Deviation (RMSD) of the protein backbone and the radius of gyration (Rg) with the GROMACS rms and gyrate modules, respectively, to assess overall stability and compactness. Residue flexibility was quantified via the Root Mean Square Fluctuation (RMSF). We also determined the Solvent Accessible Surface Area (SASA) for protein side chains using the gmx sasa tool, applying a standard probe radius of 1.4 Angstroom [28]. Finally, the equilibrated trajectories were analyzed to estimate the binding free energy.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBinding Free Energy Calculations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBinding affinities were estimated using the Molecular Mechanics Genralized-Boltzmann Surface Area (MMGBSA) approach tailored for solvent systems, implemented via gmx_MMPBSA [29,30]. We assigned dielectric constants of 7.0, 4.0, and 80.0 to the membrane, solute, and solvent, respectively. Electrostatic interactions were computed using the particle-particle particle-mesh (P3M) algorithm. To isolate specific contributions, we applied per-residue decomposition (idecomp = 2) for both electrostatic and van der Waals components. Final binding energy values reflect averages over the trajectory, with the standard error of the mean (SEM) determined through uncertainty propagation. Interfacial contacts within a 4 \\u0026Aring; cutoff were mapped using gmx_MMPBSA_ana and visualized in UCSF ChimeraX [23], while quantitative plots were produced using Grace (https://plasma-gate.weizmann.ac.il/Grace/).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCo-evolutionary Profilling\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe computed Evolutionary Couplings (ECs) using the EVcouplings server, employing a maximum entropy model constrained by multiple sequence alignment (MSA) statistics [31]. To ensure robust comparisons independent of database size or sequence length, we utilized length-normalized bitscores. Alignment reliability was evaluated using the ratio of effective sequences to protein length (Neff /L), where a value exceeding 1.0 signifies a high-quality run. For this analysis, the Neff/L ratio was calculated at 1.84.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthics approval was not required for this computational study as it did not involve animal subjects, human participants, and identifiable data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. This computational study did not involve human participants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. This computational study did not involve human participants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting financial interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNone.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author(s) received no specific funding for this work.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eGuo X, Li H, Yan C, Lei J, Zhou R, Shi Y (2024) Molecular mechanism of substrate recognition and cleavage by human \\u0026gamma;-secretase. Science (New York, N.Y.), 384(6700), 1091-1095. https://doi.org/10.1126/science.adn5820\\u003c/li\\u003e\\n \\u003cli\\u003eZhou R, Yang G, Guo X, Zhou Q, Lei J, Shi Y (2019) Recognition of the amyloid precursor protein by human \\u0026gamma;-secretase. Science (New York, N.Y.), 363(6428), eaaw0930. https://doi.org/10.1126/science.aaw0930\\u003c/li\\u003e\\n \\u003cli\\u003eHogl S, Kuhn PH, Colombo A, Lichtenthaler SF (2011) Determination of the proteolytic cleavage sites of the amyloid precursor-like protein 2 by the proteases ADAM10, BACE1 and \\u0026gamma;-secretase. PloS one, 6(6), e21337. https://doi.org/10.1371/journal.pone.0021337\\u003c/li\\u003e\\n \\u003cli\\u003eHitzenberger M, Zacharias M (2019) \\u0026gamma;-Secretase Studied by Atomistic Molecular Dynamics Simulations: Global Dynamics, Enzyme Activation, Water Distribution and Lipid Binding. Frontiers in chemistry, 6, 640. https://doi.org/10.3389/fchem.2018.00640\\u003c/li\\u003e\\n \\u003cli\\u003eBhattarai A, Devkota S, Bhattarai S, Wolfe MS, Miao Y (2020) Mechanisms of \\u0026gamma;-Secretase Activation and Substrate Processing. ACS Cent Sci. 6(6):969-983. https://doi.org/10.1021/acscentsci.0c00296\\u003c/li\\u003e\\n \\u003cli\\u003eXu TH, Yan Y, Kang Y, Jiang Y, Melcher K, Xu HE (2016) Alzheimer\\u0026apos;s disease-associated mutations increase amyloid precursor protein resistance to \\u0026gamma;-secretase cleavage and the A\\u0026beta;42/A\\u0026beta;40 ratio. Cell discovery, 2, 16026. https://doi.org/10.1038/celldisc.2016.26\\u003c/li\\u003e\\n \\u003cli\\u003eZhang, YW, Thompson R, Zhang H, Xu H (2011) APP processing in Alzheimer\\u0026apos;s disease. Molecular brain, 4, 3. https://doi.org/10.1186/1756-6606-4-3\\u003c/li\\u003e\\n \\u003cli\\u003eThoma, J, Burmann BM (2020) Fake It \\u0026apos;Till You Make It-The Pursuit of Suitable Membrane Mimetics for Membrane Protein Biophysics. International journal of molecular sciences, 22(1), 50. https://doi.org/10.3390/ijms22010050\\u003c/li\\u003e\\n \\u003cli\\u003eHoi KK, Bada Juarez, JF, Judge PJ, Yen HY, Wu D, Vinals J, Taylor GF, Watts A, Robinson CV (2021) Detergent-free Lipodisq Nanoparticles Facilitate High-Resolution Mass Spectrometry of Folded Integral Membrane Proteins. Nano letters, 21(7), 2824-2831. https://doi.org/10.1021/acs.nanolett.0c04911\\u003c/li\\u003e\\n \\u003cli\\u003eZhang S, Tao F, Qing R, Tang H, Skuhersky M et al (2018) QTY code enables design of detergent-free chemokine receptors that retain ligand-binding activities. Proceedings of the National Academy of Sciences of the United States of America, 115(37), E8652-E8659. https://doi.org/10.1073/pnas.1811031115\\u003c/li\\u003e\\n \\u003cli\\u003eCarlson ML, Young JW, Zhao Z, Fabre L, Jun D, Li J, Li J, Dhupar HS, Wason I, Mills AT, Beatty JT, Klassen JS, Rouiller I, Duong F (2018) The Peptidisc, a simple method for stabilizing membrane proteins in detergent-free solution. eLife, 7, e34085. https://doi.org/10.7554/eLife.34085\\u003c/li\\u003e\\n \\u003cli\\u003eSajeev-Sheeja A, Karag\\u0026ouml;l, A, Karag\\u0026ouml;l T, Zhang S (2025) Molecular dynamics simulations and structural bioinformatics of bacterial integral alpha-helical membrane enzymes and their AlphaFold2-predicted water-soluble QTY analogues. Molecular Simulation , vol.51, no.15, 984-998. http://doi.org/10.1080/08927022.2025.2562932\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l A, Karag\\u0026ouml;l, T, Smorodina, E, Zhang S (2024) Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L-\\u0026gt;Q, I-\\u0026gt;T, F-\\u0026gt;Y and Q-\\u0026gt;L, T-\\u0026gt;I and Y-\\u0026gt;F. PloS one, 19(4), e0289644. https://doi.org/10.1371/journal.pone.0289644\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l T, Karag\\u0026ouml;l A, Zhang S (2024) Structural bioinformatics studies of serotonin, dopamine and norepinephrine transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L-\\u0026gt;Q, I-\\u0026gt;T, F-\\u0026gt;Y and Q-\\u0026gt;L, T-\\u0026gt;I and Y-\\u0026gt;F. PloS one, 19(3), e0300340. https://doi.org/10.1371/journal.pone.0300340\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l A, Karag\\u0026ouml;l, T, Zhang S (2024) Molecular Dynamic Simulations Reveal that Water-Soluble QTY-Variants of Glutamate Transporters EAA1, EAA2 and EAA3 Retain the Conformational Characteristics of Native Transporters. Pharmaceutical research, 41(10), 1965-1977. https://doi.org/10.1007/s11095-024-03769-0\\u003c/li\\u003e\\n \\u003cli\\u003eJohnsson F, Karag\\u0026ouml;l T, Karag\\u0026ouml;l A, Zhang S (2024) Structural bioinformatic study of six human olfactory receptors and their AlphaFold3 predicted water-soluble QTY variants and OR1A2 with an odorant octanoate and TAAR9 with spermidine. QRB discovery, 6, e2. https://doi.org/10.1017/qrd.2024.18\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l T, Karag\\u0026ouml;l A (2025) pH-Dependent Membrane Binding Specificity of Synaptogyrins 1-3 Provides Mechanistic Insights into Synaptic Vesicle Regulation and Neurological Disease. bioRxiv. https://doi.org/10.1101/2025.03.03.641025\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l T, Karag\\u0026ouml;l A, Zhang S (2025) Co-evolution of alpha-helical transmembrane protein residues: large-scale variant profiling and complete mutational landscape of 2277 known PDB entries representing 504 unique human protein sequences. Journal of Molecular Evolution, 1-19. https://doi.org/10.1007/s00239-025-10262-8\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l, A., \\u0026amp; Karag\\u0026ouml;l, T. (2025). Adaptation to Solvent Environment in Toll-like Receptor 5: A Comparative Evolutionary Analysis of Membrane-bound and Soluble Forms in Epinephelus coioides. bioRxiv, 2025-02. https://doi.org/10.1101/2025.02.28.640895\\u003c/li\\u003e\\n \\u003cli\\u003eFuchs A, Martin-Galiano AJ, Kalman M, Fleishman S, Ben-Tal N, Frishman D (2007) Co-evolving residues in membrane proteins. Bioinformatics, 23(24), 3312-3319. https://doi.org/10.1093/bioinformatics/btm515\\u003c/li\\u003e\\n \\u003cli\\u003eZeng B, H\\u0026ouml;nigschmid P, Frishman D (2019) Residue co-evolution helps predict interaction sites in \\u0026alpha;-helical membrane proteins. Journal of Structural Biology, 206(2), 156-169. https://doi.org/10.1016/j.jsb.2019.02.009\\u003c/li\\u003e\\n \\u003cli\\u003eJumper J, Evans, R, Pritzel A, Green T et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2\\u003c/li\\u003e\\n \\u003cli\\u003eMeng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, Ferrin TE (2023). UCSF ChimeraX: Tools for structure building and analysis. Protein Science, 32(11), p.e4792. https://doi.org/10.1002/pro.4792\\u003c/li\\u003e\\n \\u003cli\\u003eAbraham MJ, Murtola T, Schulz R, P\\u0026aacute;ll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. \\u003cem\\u003eSoftwareX\\u003c/em\\u003e, \\u003cem\\u003e1\\u003c/em\\u003e, pp.19-25. https://doi.org/10.1016/j.softx.2015.06.001\\u003c/li\\u003e\\n \\u003cli\\u003eKarag\\u0026ouml;l T, Karag\\u0026ouml;l A (2024) Benchmarking GROMACS on Optimized Colab Processors and the Flexibility of Cloud Computing for Molecular Dynamics. \\u003cem\\u003ebioRxiv\\u003c/em\\u003e, pp.2024-11. https://doi.org/10.1101/2024.11.14.623563\\u003c/li\\u003e\\n \\u003cli\\u003eJo S, Kim T, Iyer VG, Im W (2008) CHARMM‐GUI: a web‐based graphical user interface for CHARMM. \\u003cem\\u003eJournal of computational chemistry\\u003c/em\\u003e, \\u003cem\\u003e29\\u003c/em\\u003e(11), pp.1859-1865. https://doi.org/10.1002/jcc.20945\\u003c/li\\u003e\\n \\u003cli\\u003eHuang J, Rauscher S, Nawrocki G, Ran T, Feig M, De Groot BL, Grubm\\u0026uuml;ller H, MacKerell Jr AD, (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. \\u003cem\\u003eNature methods\\u003c/em\\u003e, \\u003cem\\u003e14\\u003c/em\\u003e(1), pp.71-73. https://doi.org/10.1038/nmeth.4067\\u003c/li\\u003e\\n \\u003cli\\u003eEisenhaber F, Lijnzaad P, Argos P, Sander C, Scharf M (1995) The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. \\u003cem\\u003eJournal of computational chemistry\\u003c/em\\u003e, \\u003cem\\u003e16\\u003c/em\\u003e(3), pp.273-284. https://doi.org/10.1002/jcc.540160303\\u003c/li\\u003e\\n \\u003cli\\u003eBill R, Miller T, Dwight McGee, Jason M Swails, Nadine Homeyer, Holger Gohlke, Adrian E Roitberg (2012) MMPBSA. py: An Efficient Program for End-State Free Energy Calculations. Journal of Chemical Theory and Computation, 2012 8 (9), 3314-3321. https://doi.org/10.1021/ct300418h\\u003c/li\\u003e\\n \\u003cli\\u003eVald\\u0026eacute;s-Tresanco MS, Vald\\u0026eacute;s-Tresanco ME, Valiente PA, Moreno E (2021) gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. Journal of Chemical Theory and Computation, 17 (10), 6281-6291. https://doi.org/10.1021/acs.jctc.1c00645\\u003c/li\\u003e\\n \\u003cli\\u003eThomas A Hopf, Anna G Green, Benjamin Schubert, Sophia Mersmann, Charlotta P I Sch\\u0026auml;rfe, John B Ingraham, Agnes Toth-Petroczy, Kelly Brock, Adam J Riesselman, Perry Palmedo, Chan Kang, Robert Sheridan, Eli J Draizen, Christian Dallago, Chris Sander, Debora S Marks (2019) The EVcouplings Python framework for coevolutionary sequence analysis, Bioinformatics, Volume 35, Issue 9, 1 May 2019, Pages 1582-1584, https://doi.org/10.1093/bioinformatics/bty862\\u003cstrong\\u003e\\u003cbr\\u003e\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Protein Engineering, Molecular Dynamics Simulation, Evolutionary Couplings, Water-Soluble Membrane Proteins\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8320072/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8320072/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"The Amyloid Precursor Protein (APP) fragments involve plaque formation when generated by γ-secretase. Mechanistic interrogation of γ-secretase substrate selection has been historically constrained by the hydrophobic and detergent-sensitive nature of its transmembrane core. Here, we generate the first fully water-soluble QTY-engineered analogue of the γ-secretase transmembrane scaffold and evaluate its ability to bind the APP transmembrane helix across 250ns molecular dynamics simulations. The QTY-code engineered protein preserved helical topology, orientation, and packing with 1.7 angstrom precision. MM-GBSA analyses yielded a mean binding free energy of -173.1±24.9 kcal/mol, supported by residue-level hotspot contributions and APP positions Arg61, Lys65, and Phe78. We further uncovered a dense cluster of co-evolving residues at the interface between the Presenilin-1 substrate-binding pocket and the flexible loop region of APP. These findings demonstrate that substrate recognition is preserved outside a lipid environment, establishing QTY-solubilized γ-secretase as a powerful platform for mechanistic dissection, mutational analysis, and biosensor development.\",\"manuscriptTitle\":\"Capture of Amyloid Precursor Protein Fragments by an Engineered Water-Soluble γ-Secretase Variant\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-12 08:01:42\",\"doi\":\"10.21203/rs.3.rs-8320072/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0beb105d-5579-425b-a492-37c7b519234d\",\"owner\":[],\"postedDate\":\"December 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":59405803,\"name\":\"Biological sciences/Chemical biology/Proteins\"},{\"id\":59405804,\"name\":\"Biological sciences/Drug discovery\"}],\"tags\":[],\"updatedAt\":\"2025-12-22T03:16:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-12 08:01:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8320072\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8320072\",\"identity\":\"rs-8320072\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}