Pharmacophore-Guided Computational Modeling of Marine-Derived γ-Secretase Modulators for Amyloid-Beta Reduction in Alzheimer’s Disease

Pharmacophore-Guided Computational Modeling of Marine-Derived γ-Secretase Modulators for Amyloid-Beta Reduction in Alzheimer’s Disease | 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 Article Pharmacophore-Guided Computational Modeling of Marine-Derived γ-Secretase Modulators for Amyloid-Beta Reduction in Alzheimer’s Disease Md. Sakhawat Hossain, Md. Al Amin, Md. Saruar Alam Sakib, Akhi Akter, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5369025/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 Alzheimer’s disease is primarily caused by the accumulation of amyloid-beta (Aβ) proteins, with γ-secretase playing a key role in the formation of Aβ (1–42). This study aimed to identify novel γ-secretase modulators from marine resources that selectively lower Aβ production. Using BMS 299897 and ELN 318463 as reference drugs, we developed a Shared Feature Pharmacophore (SFP) map featuring 1 hydrogen bond donor, 3 hydrogen bond acceptors, 4 hydrophobic regions, 2 aromatic bonds, and 2 halogen bond donors. Screening a library of 47,451 marine-derived compounds through this map identified six promising hits. Synthetic γ-secretase modulators were designed using fragment-based drug design by integrating bioactive fragments from these hits with the essential 4-chlorobenzenesulfonamide ring of the reference drugs. Molecular docking and pharmacokinetic analyses highlighted three compounds (Molecule 6, Molecule 24, and Molecule 28) with stronger binding affinities than BMS 299897 and favorable blood-brain barrier permeability. Additionally, 100 ns molecular dynamics simulations demonstrated stable conformational dynamics and robust interactions for Molecule 24. While these findings are promising, further experimental validation is necessary to confirm the effectiveness and safety of these compounds as potential Alzheimer’s treatments. Biological sciences/Drug discovery/Drug screening/Virtual screening Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimers disease Biological sciences/Computational biology and bioinformatics γ-Secretase Modulators Amyloid-Beta (Aβ) Marine-Derived Compounds Pharmacophore Modeling and Alzheimer’s disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights 1. Marine-derived γ-secretase modulators were identified using pharmacophore-based virtual screening, targeting amyloid-beta reduction for Alzheimer’s treatment. 2. Fragment-based drug design integrated bioactive elements from top-screened marine compounds with a key structural feature (4-chlorobenzenesulfonamide ring) for novel inhibitor development. 3. The newly designed modulators demonstrated strong binding affinities and favorable pharmacokinetics, including the ability to cross the blood-brain barrier. 1. Introduction Alzheimer's disease (AD) represents a multifaceted neurodegenerative condition distinguished by a gradual deterioration of cognitive functions, memory impairment, and ultimately, a decline in independence [ 1 ]. As the most common variant of dementia, it impacts over 50 million people globally, with estimations indicating that this figure may surpass 152 million by the year 2050 [ 2 ]. The pathophysiological mechanisms underlying AD entail the buildup of amyloid-beta plaques, tau neurofibrillary tangles, neuroinflammatory responses, and synaptic dysfunction, culminating in neurodegeneration and cognitive deficits [ 3 ]. Amyloid-beta plaques are extracellular deposits primarily composed of aggregated Aβ42 peptides [ 4 ]. These plaques accumulate in the brain and disrupt neuronal communication, leading to synaptic dysfunction, neuroinflammation, and eventually neuronal death [ 5 ]. The overproduction and aggregation of Aβ42 are driven, in part, by dysregulation of gamma-secretase activity [ 6 ]. Gamma-secretase constitutes a multiprotein complex that is integral to the proteolytic cleavage of various type I transmembrane proteins, notably including the amyloid precursor protein (APP). This cleavage process yields amyloid-beta (Aβ42) peptides, which subsequently aggregate to form plaques [ 7 ]. The principal constituents of the gamma-secretase complex comprise presenilin (PSEN), nicastrin, anterior pharynx-defective 1 (APH-1), and presenilin enhancer 2 (PEN-2) [ 8 ]. Among these elements, presenilin 1 (PSEN1) is of particular importance, given that changes in the PSEN1 gene are related with familial variants of AD, resulting in an elevated production of the deleterious Aβ42 peptide [ 9 ]. The importance of gamma-secretase in AD pathogenesis has made it a target for therapeutic intervention. However, gamma-secretase also cleaves several other essential substrates, such as Notch receptors, which are involved in cell signaling and development [ 10 ]. Complete inhibition of gamma-secretase could therefore result in serious side effects such as impairing notch signaling pathway [ 11 ]. As a result, current research is focused on identifying gamma-secretase modulators (GSMs) that selectively reduce Aβ42 production without affecting other substrates, offering a more targeted approach to reducing amyloid plaque formation. A few modulators have been identified that can affect gamma-secretase functionality, either reducing Aβ42 production or avoid inhibition of notch signaling pathway [ 12 ]. However, their failure in clinical trials underscores the need for more precise and effective therapeutic strategies. Therefore, in this study, we aimed to identify novel GSMs with improved specificity and efficacy in treating AD. We utilized computational methods in this study for the virtual screening of an extensive library of marine-derived compounds. Marine bioactive compounds, extracted from a diverse array of marine life including algae, sponges, and corals, have attracted scholarly interest for their prospective therapeutic implications in AD [ 13 ]. These compounds display a range of biological activities, encompassing antioxidant, anti-inflammatory, and neuroprotective properties, positioning them as promising candidates for the formulation of AD therapeutics [ 14 ]. For example, polyphenols derived from marine sources have demonstrated efficacy in diminishing amyloid-beta aggregation and modulating tau phosphorylation, thereby addressing pivotal pathological features of the disorder [ 15 ]. Additionally, the distinctive chemical configurations of these marine compounds present avenues for the exploration of novel mechanisms of action that could enhance pre-existing therapies or unveil new treatment pathways. To guide our design process, we focused on BMS 299897 which has been well-documented for its ability to reduce Aβ production [ 16 ]. However, its interference with Notch signaling and other pathways raises concerns about potential side effects [ 17 ]. Conversely, in cellular experiments, the APP-selective gamma-secretase inhibitor ELN318463 has a 75–120-fold preference for blocking Aβ synthesis above its impact on Notch signaling. It works as a traditional gamma-secretase inhibitor [ 18 ]. Both the inhibitors showed to interact with the PSEN1 subunit of gamma-secretase [ 19 ]. Hence, by utilizing these compounds as reference models, we aimed to identify novel molecules that selectively target PSEN1 without disrupting Notch signaling. This strategic approach enhances the therapeutic potential of our synthesized modulators, potentially leading to safer and more effective treatments for Alzheimer’s disease. In the present investigation, we established a pharmacophore model employing BMS 299897 and ELN 318463 for screening marine compounds library. Following the identification of prospective hit compounds, we employed fragment-based drug design methodologies to synthesize modulators by amalgamating bioactive fragments with a critical structural element derived from the reference drugs. The newly synthesized modulators underwent further scrutiny via molecular docking to evaluate their binding affinities and pharmacokinetic characteristics. Ultimately, we performed a thorough comparison of the synthesized modulators with the original γ-secretase modulators to ascertain their potential efficacy as viable treatments for Alzheimer's disease. 2. Result 2.1. Generation and Analysis of Individual Pharmacophore Maps for γ-Secretase Inhibitors The pharmacophore map of the two γ-secretase Inhibitors, BMS 299897 and ELN 318463, was generated individually. Table 1 presents a detailed breakdown of the key pharmacophoric features identified for each compound, along with the features of the Shared Feature Pharmacophore (SFP) model. The individual pharmacophore maps of BMS 299897 and ELN 318463 revealed several common features, with hydrophobic regions (HPho), aromatic moieties (Ar), and halogen bond donors (XBD) being consistent across both inhibitors. However, they differ slightly regarding hydrogen bond donors (HBD) and acceptors (HBA), with BMS 299897 lacking any hydrogen bond donors, while ELN 318463 has one. The Shared Feature Pharmacophore (SFP) model was generated by aligning the individual maps of both compounds, leading to a consensus model that retained the key features from each inhibitor. The final SFP model reflects the features common between the two, including 1 hydrogen bond donor, 3 hydrogen bond acceptors, 4 hydrophobic regions, 2 aromatic bonds, and 2 halogen bond donors, as shown in Table 1 . These features were aligned using the pharmacophore alignment algorithms, resulting in the final SFP model that retains the key interacting regions of both inhibitors (Fig. 1 a). The alignment of BMS 299897 and ELN 318463 was conducted, accompanied by their corresponding pharmacophore maps. The 2D and 3D visualizations of these pharmacophore maps are presented in Fig. 1 b and Fig. 1 c, respectively. By overlapping these features, a unified SFP model was generated. This model retained the critical pharmacophoric elements from both drugs, which are essential for their interaction with the γ-secretase enzyme. The resulting pharmacophore map can now serve as the basis for further virtual screening and identification of novel γ-secretase inhibitors. 2.2. Ligand Library Screening and Identification of Potential Hits A ligand library consisting of 43,212 compounds from marine sources was generated from the Comprehensive Marine Natural Products Database (CMNPD). The entire library was converted into .ldb format and prepared for the first round of screening using the SFP model developed earlier. After running the virtual screening, six compounds were identified as potential hits, fitting well with the SFP model based on their pharmacophore features. Table 2 summarizes the top five hit compounds along with their pharmacophore fit scores. These scores indicate how well each compound's pharmacophoric features match the key interaction points within the SFP model. The higher the score, the better the fit with the model. The pharmacophore fit score measures how well each compound's features match the key elements of the SFP model. For example, CMNPD10454 achieved the highest score of 110.3849, indicating a nearly perfect match with the pharmacophoric features, such as HBA, HPho, and Ar, making it a strong candidate for γ-secretase inhibition (Fig. 2 a). Similarly in Fig. 2 b, the alignment of the SFP model with CMNPD10455 is shown and Fig. 2 c and 2 d shows the fit of the model with CMNPD10456 and CMNPD10457 respectively. As the six compounds are highly specific to the SFP map, their structural complexity makes them unsuitable as direct drug candidates. To address this, we created synthetic models of γ-secretase modulators by blending the bioactive portions of these hit compounds with the 4-chlorobenzenesulfonamide ring from BMS 299897 and ELN 318463, as explained in Method Section 4.3 . This approach helps retain the essential pharmacophoric features while simplifying the structure to make the compounds more viable as potential drugs. 2.3. Conjugation of Screened Compounds with the 4-Chlorobenzenesulfonamide Ring and Chemical Scaffold Analysis For this study, the γ-secretase inhibitors BMS 299897 and ELN 318463 were chosen as test sets. Using the criteria from Section 4.3 , the Alvabuilder software was utilized to generate 50 new molecular models. During this process, the 4-chlorobenzenesulfonamide ring (SMILES: ClC1 = CC = C(C = C1)S(= O)(= O)N) was kept fixed, and new fragments from the CMNPD-screened compounds were added. The full list of these compounds and their respective scores can be found in Table S1 , with the top five compounds displayed in Table 3 . These top compounds were evaluated for their binding characteristics and molecular structure. The similarity score between the new models and the original γ-secretase inhibitors exceeded 50%, with about half of the similarity attributed to the 4-chlorobenzenesulfonamide ring. Many of the new compounds incorporated pyrrole rings or fused with aliphatic segments. To avoid halogen overloading, we limited the halogens to a maximum of four, considering the presence of chlorine in the original ring. The SAScore, a measure of synthetic feasibility, was kept below 5 for most models, with many below 4.15, indicating strong practical viability for synthesis. All 50 synthetic models were selected for further molecular docking analysis to identify potential drug candidates. The SMARTS.plus tool was used to compare three newly designed compounds with the chemical scaffold of BMS 299897 (Fig. 3 ). As shown in Figs. 8 a– 8 c, the 4-chlorobenzenesulfonamide ring was the common feature between the predicted molecules and the control drug, while the rest of the fragments were newly added. Using Alvabuilder, we maintained the crucial 4-chlorobenzenesulfonamide ring and integrated new bioactive fragments, aiming to enhance the effectiveness of γ-secretase inhibitors for potential drug development. 2.5 Virtual Screening and Molecular Interaction Analysis of Selected Compounds In the second round of virtual screening, we tested the 50 newly designed γ-secretase modulator models against the 5A63 protein using PyRx. We performed docking selectively with the PSEN1 subunit of the gamma secretase enzyme. The results, summarized in Table S2 , showed that nearly all compounds had better docking scores than the control drug BMS 299897, which had a docking score of -8.9 kcal/mol. The top performers were Molecule 6, Molecule 24, and Molecule 28, all of which not only demonstrated strong docking scores (ranging from − 10.8 to -9.6 kcal/mol) (Table 4 ) but also passed additional ADMET analysis (Table S3 ), suggesting they can cross the Blood-Brain Barrier (BBB) (Table 6 ). In Table 4 & Fig. 4 , the docking energy and interaction details for the top compounds are compared with the control drug BMS 299897. The control drug forms hydrogen bonds with amino acids ASN55, ASN142, GLY144, and ASP336, while also establishing hydrophobic interactions with HIS58, ALA56, and VAL138. These interactions play a significant role in its binding efficiency to the 5A63 protein. For Molecule 6, although no hydrogen bonds were observed, the compound exhibited strong hydrophobic interactions with several key residues such as PHE411, VAL94, LEU35, ALA39, and ILE408. The extensive hydrophobic interactions strengthen the overall binding, contributing to its superior docking score of -10.8 kcal/mol. Similarly, Molecule 24 formed no hydrogen bonds but showed significant hydrophobic interactions with residues like ILE690, LEU169, PHE173, and TYR119, contributing to its strong binding and docking score of -9.7 kcal/mol. Molecule 28 displayed similar interaction patterns to Molecule 24, forming hydrophobic contacts with ILE690, LEU169, PHE173, ALA694, and PHE682, resulting in a docking score of -9.6 kcal/mol. These interactions highlight the compound’s strong binding potential to the 5A63 protein. In summary, the top compounds identified through virtual screening showed stronger binding affinities than the control drug due to their extensive hydrophobic interactions with the 5A63 protein. This interaction is crucial for stabilizing the ligand within the active site, making these compounds strong candidates for further investigation as γ-secretase modulators. 2.6. Molecular Dynamics Simulation Outcomes 2.6.1. RMSD Analysis The root mean square deviation (RMSD) metrics for the γ-secretase-molecule complexes were computed to ascertain the stability of these complexes over time. The observations indicated that the γ-secretase-Molecule-6, γ-secretase-Molecule-24, γ-secretase-Molecule-28, and γ-secretase-BMS-299897 complexes exhibited average RMSD values of 5.19Å, 4.03Å, 5.65Å, and 4.64Å, respectively, throughout the entire simulation duration (Fig. 5 a). The most pronounced deviation was recorded for the γ-secretase-Molecule-6 complex at 49 ns, with a peak RMSD value of 7.05Å; nevertheless, a trend of decreasing structural deviation was noted post 52 ns. A comparable RMSD trend was also observed for the γ-secretase-Molecule-28 complex as well as the reference complex. Among the four complexes analyzed, the γ-secretase-Molecule-24 complex exhibited the least deviation, maintaining a relatively stable profile throughout the simulation in comparison to the control. 2.6.2. RMSF Analysis The root-mean-square fluctuation (RMSF) values were derived to evaluate the degree of flexibility within the complexes during the simulation timeframe. In contrast to RMSD, which monitors positional variances across entire structures over time, RMSF specifically quantifies the fluctuations of individual amino acid residues throughout the simulation process. This analysis offers valuable insights into the dynamic alterations occurring within the amino acid residues of the protein chain amid protein-ligand interactions. From the findings, it can be deduced that both the control and the predicted complexes exhibited a comparable degree of flexibility within the protein architecture throughout the 100 ns simulation. The majority of residues demonstrated minimal variability in RMSF values, indicating the overall stability of the complexes. The control complex exhibited greater deviations of 9.706 Å, 10.839 Å, 9.714 Å, and 9.703 Å at the ASN204, GLY205, SER206, and VAL_261 residues, respectively. Among the tested molecules, Molecule-6 displayed the highest deviations of 9.4 Å, 9.202 Å, and 9.607 Å at GLY205, SER206, and SER242, respectively. The average RMSF values recorded were 2.07 Å for γ-secretase-Molecule-6, 1.76 Å for γ-secretase-Molecule-24, 1.81 Å for γ-secretase-Molecule-28, and 2.17 Å for the γ-secretase-Control complex (Fig. 5 b). 2.6.3. Radius of Gyration Evaluation An analysis of the Radius of Gyration (Rg) was performed to assess the compactness and rigidity of the drug-protein complexes. Within the framework of interactions between proteins and small molecules, the atomic configuration along the axis is evaluated through the examination of the radius of gyration (Rg). Rg serves as a paramount predictive model, facilitating the calculation and conceptualization of the overall compactness of the complex throughout the simulation duration. In terms of compactness and rigidity, the γ-secretase-Molecule-24 complex exhibited a lower Rg value compared to the other three complexes, with all molecular complexes reflecting lower values than the control complex. The determined Rg values for Molecule 6, Molecule 24, Molecule 28, and the control complex in relation to the target protein were (36.75–38.81), (37.41–38.68), (36.87–39.11), and (37.91–39.50) Å, respectively, indicating that the interactions with ligands were compact, resulting in minimal structural alterations in the protein binding sites in contrast to the control (Fig. 5 c). 2.6.4. SASA Evaluation We executed an analysis of the Solvent Accessible Surface Area (SASA) of the complexes to examine the expansion of the surface area during the simulation. The γ-secretase-Molecule-6 complex presented an average surface area of 51345.42 Ų, while the γ-secretase-Molecule-24, γ-secretase-Molecule-28, and γ-secretase-BMS 299897 exhibited surface areas of 51666.00 Ų, 50394.39 Ų, and 50707.09 Ų, respectively. Notably, the surface area of the γ-secretase-Molecule-28 complex demonstrated a decrease after 13 ns, continuing to diminish over time. Conversely, the surface area of the γ-secretase-control complex exhibited fluctuations alongside a declining trajectory after 84 ns until the simulation's conclusion. Initially, the surface area of γ-secretase-Molecule-6 was less than that of the γ-secretase-Molecule-24 complex; however, after 24 ns, both displayed a comparable decreasing trend over the designated time frame (Fig. 5 d). 2.6.5 Intermolecular Interactions The intermolecular interactions within the protein-ligand complexes were analyzed through a 100 ns simulation. Hydrogen bonds, hydrophobic interactions, ionic bonds, and water bridges are illustrated in Fig. 6 . In the γ-secretase-control complex (Fig. 6 a), transient hydrogen bonds were noted at active site residues THR90 and THR10, while a more persistent hydrogen bond interaction was observed at ILE135. Beyond hydrogen bonds, hydrophobic interactions were significant among the amino acids at the active site. For the γ-secretase-Molecule-6 complex (Fig. 6 b), hydrogen bonds were detected at THR407 and ILE127, alongside prominent hydrophobic interactions at PHE411 and ILE127. In the γ-secretase-Molecule-24 complex (Fig. 6 c), a hydrogen bond was identified at CYS4, with ionic interactions noted at LEU243 and LEU244. For the γ-secretase-Molecule-28 complex (Fig. 6 d), a hydrogen bond was formed at LEU244, with considerable hydrophobic interactions also observed. 3. Discussion Selective inhibition of γ-secretase complexes is critical for treating Alzheimer’s disease, as this enzyme is responsible for the production of amyloid-beta (Aβ) peptides, which accumulate and form plaques in the brains of affected individuals [ 20 ]. The active site of γ-secretase resides within its PSEN1 subunit, making it a key target for drug development [ 21 ]. While previous efforts have led to the creation of γ-secretase inhibitors and modulators aimed at PSEN1, these drugs have faced significant challenges, particularly due to off-target effects. Notably, many of these compounds inadvertently affect Notch signaling pathways, which can lead to adverse effects in the gastrointestinal system and other tissues [ 22 ]. In this study, we explore the potential of marine-derived metabolites in the search for more selective and effective PSEN1-targeted γ-secretase modulators. Marine natural products are known for their structural diversity, offering unique features not commonly found in terrestrial organisms [ 23 ]. This natural chemical diversity provides an opportunity to discover novel compounds that might exhibit greater specificity for PSEN1, reducing off-target interactions and associated side effects. By leveraging the distinct characteristics of marine metabolites, we aim to identify compounds that can selectively inhibit γ-secretase activity without affecting other critical pathways, such as Notch signaling, thus offering a promising new direction in Alzheimer’s disease drug discovery. By focusing on this therapeutic target, the research leveraged the known modulators BMS 299897 and ELN 318463 to develop a Shared Features Pharmacophore (SFP) model, which was used as a reference for the discovery of novel modulators. The ligand-based pharmacophore modeling approach allowed for the identification of key pharmacophoric features such as hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), hydrophobic regions (HPho), aromatic moieties (Ar), and halogen bond donors (XBD). The alignment of these features in BMS 299897 and ELN 318463 (Fig. 1 c) resulted in a robust SFP model, reflecting the critical interaction points necessary for γ-secretase modulation. This model was instrumental in the virtual screening of over 43,000 marine-derived compounds from the Comprehensive Marine Natural Products Database (CMNPD), leading to the identification of six compounds with promising pharmacophoric fit scores (Table 2 ). These compounds may be ineffective against Alzheimer’s disease due to their structural complexity and poor permeability across the blood-brain barrier, which limits the0ir ability to reach the brain and exert therapeutic effects. One of the key findings from this study was the identification of the 4-chlorobenzenesulfonamide ring as a central structural feature in both BMS 299897 and ELN 318463. This group contributed significantly to hydrophobic interactions and was retained in the design of new synthetic γ-secretase modulators. Using fragment-based drug design and QSAR modeling, 50 new synthetic models were generated, incorporating bioactive portions of the identified marine compounds. The top-performing models showed not only structural similarity to known modulators but also favorable drug-like properties as per Lipinski’s Rule of Five, synthetic feasibility (SAScore ≤ 5), and pharmacokinetic parameters (Table 3 ). Among the 50 modeled compounds, we identified 3 that were predicted to be BBB (blood-brain barrier) permeable. The identification of BBB-permeable compounds is particularly important since many promising drug candidates fail due to their inability to cross the BBB [ 24 ]. The examination of chemical scaffolds and the comparative analysis of structures among the newly designed compounds revealed similarity in their chemical motifs and scaffolds of the anticipated pharmaceuticals with the control (Fig. 3 ). Molecular docking analysis revealed several synthetic compounds with stronger binding affinities than the control drug BMS 299897, further validating the efficacy of the newly designed molecules. Molecule 6, in particular, exhibited a docking energy of -10.8 kcal/mol, outperforming the control with extensive hydrophobic interactions with residues such as PHE411, VAL94, and ILE408 (Table 4 ). Notably, we employed precision docking targeting the specific region of γ-secretase containing its active site. The interaction within the PSEN1 subunit's active site suggests that the designed molecules have the potential to modulate γ-secretase activity effectively. Interestingly, none of the top-performing compounds formed hydrogen bonds with the active site residues, suggesting that hydrophobic interactions may be the dominant factor in their inhibitory action. Pharmacokinetic analysis further strengthened the potential of these compounds as viable drug candidates. Molecules 6, 24, and 28 demonstrated favorable drug-like properties, including high gastrointestinal absorption and the ability to cross the blood-brain barrier (BBB), which is crucial for CNS-targeted therapies (Table 5 ). Additionally, these compounds met key drug-likeness criteria, such as Lipinski's, Ghose, and Veber rules, indicating a high likelihood of success in preclinical trials (Table S3 ). The molecular dynamics simulations of γ-secretase bound to Molecule-6, Molecule-24, Molecule-28, and the control compound BMS-299897 provide detailed insights into the stability, flexibility, compactness, and surface behavior of these complexes over time. By evaluating the results of RMSD, RMSF, Rg, and SASA analyses, we can assess the potential effectiveness and stability of these molecules in binding to the γ-secretase enzyme. The RMSD values of the γ-secretase-ligand complexes reveal the structural stability of the complexes throughout the simulation. A lower RMSD value generally indicates greater structural stability. Among the complexes, γ-secretase-Molecule-24 exhibited the lowest average RMSD (4.03 Å), indicating that it induced the least structural deviation in the protein, suggesting a more stable interaction (Fig. 5 ). RMSF analysis is also crucial for assessing the flexibility of individual residues during the simulation. The γ-secretase-Control complex displayed higher fluctuations at residues ASN204, GLY205, SER206, and VAL261, indicating that these regions are more flexible, potentially leading to instability. Molecule-6 exhibited the greatest fluctuations at GLY205, SER206, and SER242, reflecting some degree of local flexibility. Molecule-24 showed lowest RMSF values (1.76 Å), implying better overall stability across the protein chain. This lower fluctuation suggests a stronger and more stable interaction, which aligns with its favorable RMSD profile (Fig. 5 b). Rg values provide information about the compactness and rigidity of the complexes. The Rg values indicated that Molecule-24 (37.41–38.68 Å) resulted in a more compact complex compared to Molecule-6 (36.75–38.81 Å), Molecule-28 (36.87–39.11 Å), and the control (37.91–39.50 Å). A lower Rg value is generally favorable, as it suggests a more compact complex, implying that the ligand has induced less structural change in the protein. Molecule-24 had the highest average SASA value (51666.00 Ų), followed by Molecule-6 (51345.42 Ų), indicating that these complexes maintained relatively consistent exposure to solvent throughout the simulation (Fig. 5 c). Molecule-28 showed a decreasing trend after 13 ns, suggesting some level of contraction or reduced exposure over time. The control complex displayed fluctuations and a declining trend after 84 ns, possibly indicating some instability. In case of intermolecular bonds, hydrogen bonds and hydrophobic interactions were key contributors to the stability of these complexes (Fig. 6 ). Molecule-6 formed hydrogen bonds with THR407 and ILE127, along with significant hydrophobic interactions with PHE411 and ILE127. Molecule-24 formed hydrogen bonds at CYS4, with ionic interactions at LEU243 and LEU244. In comparison, Molecule-28 formed a single hydrogen bond at LEU244, with prominent hydrophobic interactions, while the control complex showed less consistent hydrogen bonding. Based on the analysis of RMSD, RMSF, Rg, SASA, and intermolecular bonds, Molecule-24 appears to be the best candidate for binding γ-secretase, producing better results than the control complex. It demonstrated the most stable RMSD profile, the lowest RMSF values, maintained a compact structure throughout the simulation (low Rg), and had consistent solvent exposure (SASA). The strong intermolecular interactions, particularly hydrogen bonds and ionic bonds, further support its efficacy as a ligand. 4. Methods The methodology of the study to identify novel γ-secretase modulators for Alzheimer's disease have been illustrated in Fig. 7 . 4.1. Retrieval of γ-secretase inhibitors for Ligand-based Pharmacophore Modeling In this study, ligand-based pharmacophore modeling [ 25 ] was employed to develop a pharmacophore map targeting the γ-secretase inhibitors BMS 299897 [ 26 ] and ELN318463 [ 27 ] ( Table 6 ). The software Ligandscout v4.4 [ 28 ] was used to generate a Shared Features Pharmacophore (SFP) model ( https://docs.inteligand.com/ligandscout/ ) [ 29 ]. Initially, individual pharmacophore maps for BMS 299897 and ELN318463 were created. This process involved identifying and mapping key chemical features, such as hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic regions (HyPho), aromatic moieties (Ar), and halogen bond donors (XBD), which are important for their interaction with γ-secretase [ 30 ]. The pharmacophore maps were generated by analyzing the Inhibitors' three-dimensional structures and detecting the spatial arrangement of these features. Using Ligandscout’s feature alignment algorithms, the individual maps were then aligned to highlight common features between the two inhibitors. This alignment step allowed us to superimpose and combine the most critical pharmacophoric elements from both ligands. This model was further used for virtual screening to identify novel γ-secretase modulators based on the identified features. 4.2. Ligand Library Preparation and Pharmacophore-based Virtual Screening For the virtual screening process, a ligand library was prepared using the Comprehensive Marine Natural Products Database (CMNPD) ( https://www.cmnpd.org/ ) [ 31 ], which contains about 47,451 compounds derived from diverse marine resources. The entire dataset of compounds was initially converted to the MOL2 format using OpenBabel v2.4.1 ( https://openbabel.org/ ), ensuring compatibility with standard molecular modeling tools. Since Ligandscout requires a specific library format, the MOL2-formatted dataset was further converted to .ldb format within Ligandscout [ 28 ]. The prepared .ldb library of 43,212 compounds after removing duplicates was then employed for virtual screening against the previously generated SFP model. The screening process aimed to identify compounds that fit the pharmacophoric features of the γ-secretase inhibitors BMS 299897 and ELN318463. This initial round of screening provided a subset of compounds that exhibited high potential for interaction with γ-secretase based on the SFP model features. 4.3. De Novo Molecular Descriptors Design of Synthetic γ-secretase modulators After the initial virtual screening, we identified several promising hits that strongly interacted with the Shared Features Pharmacophore (SFP) map. However, since these hits were directly obtained from raw data, their complex structures raised concerns about their practical effectiveness as γ-secretase modulators. Despite this, their inclusion as potential hits indicated that certain features could be valuable in designing new synthetic modulators. Both BMS 299897 and ELN318463 share a common structural element, the 4-chlorobenzenesulfonamide ring [ 32 ] (Fig. 8 ), which plays a key role in hydrophobic interactions. Using this shared feature, we aimed to generate synthetic models of γ-secretase modulators. We employed Alvabuilder [ 33 ], a toolkit from Alvascience [ 34 ], which uses genetic algorithms for de novo molecular design. The process included fragment-based design, QSAR modeling, drug energy minimization, stability analysis, and descriptor calculation for newly designed compounds. Two compounds (BMS 299897 and ELN318463) were used as the training set, while the hit compounds served as the test set. During the molecular design process, 50 new models were generated by keeping the 4-chlorobenzenesulfonamide ring fixed in all designs. Other important conditions, such as Lipinski's Rule of Five (molecular weight ≤ 500 g/mol, hydrogen bond acceptors ≤ 5, hydrogen bond donors ≤ 10, lipophilicity < 5, and TPSA 20 to 130 Ų) [ 35 ], [ 36 ], were applied. We also ensured that the SAScore was ≤ 5, halogen ≤ 4 [ 37 ] and ESOL was ≤-10 [ 38 ] to prioritize compounds that are both theoretically effective and synthetically feasible. Additionally, the tool was instructed to avoid adding aromatic rings of less than three and to include fragment crossover with 4-chlorobenzene sulfonamide rings, ensuring that functional groups were compatible and capable of forming stable covalent or non-covalent interactions. The key metric in this design process was the SAScore, which evaluates the synthetic accessibility of a compound. A lower SAScore (range 1 to 5) indicates that a compound is easier to synthesize [ 39 ], with our analysis setting the SAScore to ≤ 5 for optimal synthetic feasibility. In total, 50 synthetic γ-secretase inhibitor models were designed, all retaining the fixed 4-chlorobenzenesulfonamide ring. These models were subsequently screened to identify potential candidates for further analysis. 4.4. Molecular Docking Analysis 4.4.1. Preparation of γ-Secretase Protein and Active Site Identification We retrieved the crystal structure of human γ-secretase (PDB ID: 5A63, 3.4 Å resolution) from the Protein Data Bank ( https://www.rcsb.org/ ) [ 40 ]. All heteroatoms, water molecules, metal ions, and cofactors were removed from the structure using PyMOL [ 41 ]. To optimize the protein, we performed energy minimization using the Swiss-PDB Viewer tool [ 42 ] ( http://www.expasy.org/spdbv/ ). The CASTp web server was used to identify the protein's active site. ( http://sts.bioe.uic.edu/castp/ ) [ 43 ] with a default probe radius of 1.4 Å, which helped us calculate the area, volume, and sequence ID of the binding pocket. Additionally, BIOVIA Discovery Studio, a visualizer was used to further confirm the protein's binding sites. 4.4.2. Structure-based Virtual Screening To explore the interactions between our designed compounds and the target protein, we employed molecular docking using PyRx [ 44 ] ( https://pyrx.sourceforge.io/ ) with AutoDock Vina [ 45 ] as the docking engine. The drug molecules were prepared as ligands, and the protein was prepared as a macromolecule. Both were converted into pdbqt format in PyRx. The docking grid was centered at X: 125.101, Y: 138.470, Z: 131.2866, with dimensions of X: 80.615, Y: 131.1684, Z: 81.7397 Å. After docking, the compound with the highest binding affinity (measured in kcal/mol) was identified and visualized using BIOVIA Discovery Studio Visualizer for further analysis. During the de novo molecular generation, we applied Lipinski’s Rule of Five as a key criterion. To further ensure the compounds could cross the Blood-Brain Barrier (BBB), we also performed additional pharmacokinetic analysis using the Swiss-ADME server ( http://www.swissadme.ch/ ) [ 46 ]. 4.5. Drug Compatibility Test and Scaffold Analysis After the final screening, we identified several potential synthetic γ-secretase inhibitor conformers. The next step was to assess the compatibility of these new compounds by comparing them with BMS 299897 as the control drug. We utilized a chemo-informatics approach to analyze and describe the chemical patterns and scaffolds of the predicted drugs in relation to the control. This analysis was conducted using the SMARTS.plus tool [ 47 ] ( https://smarts.plus/ ) from the ZBH – Center for Bioinformatics ( https://www.zbh.uni-hamburg.de/en.html ), which applies a chemical pattern language called SMARTS (SMiles ARbitrary Target Specification). This language helps define specific chemical substructures and interactions within compounds. The SMARTS.plus software allows for the identification and visualization of these patterns, providing a textual and graphical representation of the chemical features and scaffold present in the newly predicted drugs. The scaffold analysis focused on identifying shared and unique chemical frameworks between the newly designed modulators and BMS 299897. 4.6. Molecular Dynamics (MD) Simulation of the Selected Protein-ligand Complexes Molecular dynamics (MD) simulation is conventionally employed to assess the stability of complexes formed between potential pharmaceutical compounds and their target proteins [ 48 ], [ 49 ], [ 50 ]. In our study, MD simulations were executed over a duration of 100 nanoseconds utilizing the Desmond software suite developed by Schrödinger LLC. Prior to the initiation of simulations, the protein-ligand complexes underwent a series of preprocessing steps, encompassing optimization and minimization, facilitated by the Protein Preparation Wizard within the Maestro software. The assembly of the system was accomplished via the System Builder tool. To replicate authentic environmental conditions, we employed the TIP3P solvent model incorporated within an orthorhombic simulation box [ 51 ]. The simulations were conducted using the OPLS_2005 force field, with counter ions introduced as necessary to achieve model neutrality [ 50 ]. In order to simulate physiological conditions, a 0.15 M NaCl solution was incorporated. Throughout the simulation process, equilibrium was maintained through the application of NVT and NPT ensembles, ensuring the conservation of the number of moles (N), pressure (P), and temperature (T) at 310 K and 1 atm, respectively. The models were subjected to pre-simulation relaxing techniques. To assess the stability of the simulations, we computed several metrics, including the radius of gyration (RG), solvent-accessible surface area (SASA), root mean square deviation (RMSD) [ 52 ], root mean square fluctuations (RMSF), and intermolecular binding interactions for both the control and the three foremost selected complexes. In this context, the Root Mean Square Deviation (RMSD) serves as a metric for quantifying the average displacement variation of a selection of atoms for a specific frame relative to a reference frame. It is computed for all frames within the trajectory. The RMSD for a given frame x is expressed as: RMSD x = \(\:\sqrt{(\frac{1}{N}}\) \(\:{\sum\:}_{i=1}^{N}(\text{r}{\prime\:}\text{i}\) (t x )) - r i (t ref )) 2 ) where N signifies the total number of atoms in the selection; t ref denotes the reference time (typically, the first frame is utilized as the reference, designated as time t = 0); and r' represents the coordinates of the selected atoms in frame x after superimposing onto the reference frame, where frame x is recorded at time tx. This calculation is reiterated for each frame throughout the simulation trajectory. The Root Mean Square Fluctuation (RMSF) is instrumental in characterizing localized alterations along the protein chain. The RMSF for residue i is defined as: RMSF i = \(\:\sqrt{(\frac{1}{T}}\) \(\:{\sum\:}_{t=1}^{T}) where T represents the trajectory time interval over which the RMSF is computed, tref indicates the reference time, ri denotes the coordinates of residue i; r' signifies the coordinates of atoms within residue i following superposition on the reference, and the angle brackets denote that the average of the squared distance is computed across the selection of atoms within the residue. Declarations Authors’ Contributions Md. Sakhawat Hossain, Md. Alamin and Md Masuder Rahman: Conceptualization. Md. Sakhawat Hossain and Md. Alamin: Data acquisition; Formal analysis; Writing- original draft. Md. Saruar Alam Sakib, Akhi Akter and Liton Chandra Das: Data acquisition; Writing- original draft. Md. Sakhawat Hossain, Md. Alamin and Md. Nurul Islam: Molecular Dynamics and simulation analysis. Md Masuder Rahman: Writing-review & editing; Final approval. Funding This study did not receive any financial support or funding from external sources. Data Availability All data and resources utilized in this study are available through publicly accessible repositories and platforms. The ligand library used for virtual screening was sourced from the Comprehensive Marine Natural Products Database (CMNPD) and is available at [https://www.cmnpd.org/]. Molecular docking simulations were performed using PyRx (version 0.8) and AutoDock Vina (version 1.1.2), both accessible at [https://pyrx.sourceforge.io/] and [https://autodock.scripps.edu/], respectively. The crystal structure of the γ-secretase enzyme (PDB ID: 5A63) was retrieved from the Protein Data Bank (PDB) at [https://www.rcsb.org/]. Conflicts of Interest/Competing Interests There are no relevant financial or non-financial interests to disclose. References R. 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SL Name SMILES formula Pharmacophore fit score 01 CMNPD10454 Brc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O) (OCC)c3c[nH]c4c3ccc(Br)c4)C(=O)OC 110.3849 02 CMNPD10455 Brc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O)(OC)c3c [nH]c4c3ccc(Br)c4)C(=O)OC 87.093346 03 CMNPD10456 Brc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O)(O)c3c [nH]c4c3ccc(Br)c4)C(=O)OC 86.5644 04 CMNPD10457 Brc4cc3n(cc(S(=O)(=O)c1c[nH]c2c1ccc (Br)c2)c3cc4)C(=O)O 86.39001 05 CMNPD15725 Clc1c(OC)ccc(c1)[C@H]3[C@H](O)[C@@H](O) [C@@](O)(c2cc(Cl)c(O)cc2)C3=O 86.31028 06 CMNPD15122 Brc2cc1n(cc(c1cc2)[C@H](S(=O)(=O)O)c3c [nH]c4c3ccc(Br)c4)C(=O)OC 86.07951 Table 3: Top five compounds from the generated 50 synthetic models with their average similarity score and SAScore. Table 4: Binding affinity results of reported compounds and their interacted residues with amino acids. Compounds name Docking energy (kcal/mol) with 5A63 protein SAScore Hydrogen bond interaction-AA Hydrophobic bond interaction-AA BMS 299897 (control) -8.9 3.856 ASN55, ASN142, GLY144, ASP336 HIS58, ALA56, VAL138 Molecule 6 -10.8 4.034 N/A PHE411, VAL94, VAL412, LEU35, VAL36, ALA39, VAL97, ILE408, PHE411 Molecule 24 -9.7 4.459 N/A ILE690, LEU169, PHE173, ILE679, PRO16, LEU20, VAL176, ALA228, PHE682, TYR119 Molecule 28 -9.6 4.44 N/A ILE690, LEU169, PHE173, TYR119, ILE690, ALA694, PHE682, PHE698, TYR119 Table 5: Physiochemical and pharmacokinetics properties of targeted compounds. Compound MW (g/mol) TPSA (Ų) and Molar Refractivity (MR) H. Bond Lipophilicity (Consensus Log Po/w) Water Solubility (Log S (ESOL)) Pharmacokinetics Drug likeness Molecule 6 349.83 TPSA: 71.62 Acceptor: 4 2.92 -3.88 GI absorption: High Lipinski: Yes Ghose: Yes Veber: Yes Egan: Yes Muegge: Yes MR:89.15 Donor: 1 BBB permeant: Yes Molecule 24 428.73 TPSA:71.62 Acceptor: 4 3.33 -4.92 GI absorption: High Lipinski: Yes Ghose: Yes Veber: Yes Egan: Yes Muegge: Yes MR: 97.02 Donor: 1 BBB permeant: Yes Molecule 28 428.73 TPSA: 71.62 Acceptor: 4 3.32 -4.92 GI absorption: High Lipinski: Yes Ghose: Yes Veber: Yes Egan: Yes Muegge: Yes MR:97.02 Donor: 1 BBB permeant: Yes BMS 299897 (control) 511.94 TPSA: 83.06 Acceptor: 7 5.65 -6.32 GI absorption: High Lipinski: Yes Ghose: Yes Veber: Yes Egan: Yes Muegge: Yes MR: 123.79 Donor: 1 BBB permeant: Yes Table 6: Description of γ-secretase Inhibitors used for initial pharmacophore map generation. SL no. γ-secretase Inhibitor name PubChem CID IUPAC name Ligand Formula 1. BMS 299897 11249248 4-[2-[(1R)-1-(N-(4-chlorophenyl)sulfonyl-2,5-difluoroanilino)ethyl]-5-fluorophenyl]butanoic acid C 24 H 21 ClF 3 NO 4 S 2. ELN 318463 46883899 N-[(4-bromophenyl)methyl]-4-chloro-N-[(3R)-hexahydro-2-oxo-1H-azepin-3-yl]-Benzenesulfonamide C 19 H 20 BrClN 2 O 3 S Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx 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. 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Sakhawat Hossain","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Sakhawat","lastName":"Hossain","suffix":""},{"id":390426626,"identity":"8443ed6d-b8d5-443c-ac3a-013b69be979c","order_by":1,"name":"Md. Al Amin","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Al","lastName":"Amin","suffix":""},{"id":390426627,"identity":"d97036be-a326-442b-ba70-f66939753933","order_by":2,"name":"Md. Saruar Alam Sakib","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Saruar Alam","lastName":"Sakib","suffix":""},{"id":390426628,"identity":"3213cae3-5b60-4616-b5cf-c89ede8f6603","order_by":3,"name":"Akhi Akter","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Akhi","middleName":"","lastName":"Akter","suffix":""},{"id":390426629,"identity":"a2e7964a-da73-4d14-b6d6-aa9660f222c6","order_by":4,"name":"Liton Chandra Das","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Liton","middleName":"Chandra","lastName":"Das","suffix":""},{"id":390426630,"identity":"3e66e628-829d-4f43-99f9-5232f666328a","order_by":5,"name":"Md Nurul Islam","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Nurul","lastName":"Islam","suffix":""},{"id":390426631,"identity":"88fb8db8-07b9-41d4-af25-1165c91a59b9","order_by":6,"name":"Md Masuder Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYLACHoYDQJIZREjIEK+Fh4EtAaSFhxQtPAYQNiGg2378mcTbnDv29hI5n1/dqLHgYWA/fHQDPi1mZ3LMJOdue5bYI5G7zTrnGNBhPGlpN/BqOZDDJs277XACD1CLcQ4bUIsEjxl+LeefPwNpseeRyHlmnPOPGC03EsxAWhh7JHKYH+e2EaXljbEl2C9nnpkx5/ZJ8LAR9Mv59Ic33m67Y8/envz4c863Ojl+9sPH8GpBBmwSYJJY5SDA/IEU1aNgFIyCUTByAAA5r0jWrxRmhQAAAABJRU5ErkJggg==","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Masuder","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2024-10-31 20:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5369025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5369025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71526506,"identity":"b6de6594-5d6e-44a3-9c72-7db060e4784b","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2555511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneration of shared Feature Pharmacophore (SFP) Map and screening based on that map.\u003c/strong\u003e (a) Top left panel shows the alignment of BMS 299897 and ELN 318463 γ-secretase inhibitors, middle panel shows the generated Shared Feature Pharmacophore (SFP) map, and then aligned marine-derived compounds overlaid with the SFP map. (b, c) represents the top marine-derived compounds fitting within the generated pharmacophore map where (b) illustrates the 2D structures of the pharmacophore maps generated for BMS 299897 and ELN 318463, respectively. Each color represents a specific pharmacophore feature: yellow represents HPho, red indicates HBD, green indicates HBA, blue rings signify Ar, pink arrows represent XBD. (c) represents three-dimensional visualization of the top marine-derived compounds, showing their spatial fit within the generated pharmacophore model, highlighting the interactions with key binding regions.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/57c581cad4378a701c5b26cd.png"},{"id":71528074,"identity":"65490102-5a4e-4f5f-9efe-ebd34115ba6d","added_by":"auto","created_at":"2024-12-16 12:40:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2159682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3D visualization of marine-derived hit compounds fitting within the generated SFP map. \u003c/strong\u003e(a-d)\u003cstrong\u003e \u003c/strong\u003evisualizes the top four hit compounds and their interactions with the SFP model, highlighting how marine compounds align with the key features of the pharmacophore map. (a) CMNPD10454, (b) CMNPD10455, (c) CMNPD10456, (d) CMNPD10457.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/6d52033bcec1c45d1d6ab074.png"},{"id":71526509,"identity":"91e5902c-de97-4f22-8493-4e46f7cba95f","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1382258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChemical scaffold analysis and structure comparison of newly predicted compounds.\u003c/strong\u003e a) Molecule 1 compared to BMS 299897, b) Molecule 2 compared to BMS 299897, c) Molecule 3 compared to BMS 299897.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/421bba3c0e0ad7b214a67b3b.png"},{"id":71526515,"identity":"f33e511f-7353-4afa-a5bb-e878647355e1","added_by":"auto","created_at":"2024-12-16 12:32:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":612606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D visualization of the interaction between 5A63 protein and the selected compounds.\u003c/strong\u003e a) Molecule 6, b) Molecule 24, c) Molecule 28, d) BMS 299897 (control).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/e10c840b42186039112d8b5e.png"},{"id":71528075,"identity":"aed88287-d2e8-419a-877e-8428b286e9af","added_by":"auto","created_at":"2024-12-16 12:40:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2399907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation of three molecules along with BMS 299897 as a control for four protein-ligand complexes\u003c/strong\u003e. The analysis includes: (a) Root Mean Squared Deviation (RMSD); (b) Root Mean Squared Fluctuation (RMSF); (c) Radius of Gyration (Rg); and (d) Solvent Accessible Surface Area (SASA).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/f951f4828d36867cb6536411.png"},{"id":71526514,"identity":"29ef09b0-61bf-4186-8714-c8997ddafc39","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":201386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe stacked bar charts illustrate the protein-ligand interactions via different types of bonds over a 100 ns simulation period.\u003c/strong\u003e The analyzed compounds, including the reference (Control, BMS 299897), along with Molecule 6, Molecule 24, and Molecule 28, all complexed with the target protein, are represented as a, b, c, and d, respectively. The Y-axis (in every figure) shows the total number of specific contacts the protein makes with the ligand over the course of the trajectory. The X-axis (in every figure) shows which residues interact with the ligand in each trajectory frame. Some residues make more than one specific contact with the ligand, which is represented by a darker shade of orange, according to the scale to the right of the plot.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/f6ce52449bcb316935396166.png"},{"id":71526511,"identity":"43f7171c-d829-477b-b9cb-838549cdca2f","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":940492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow for Identifying γ-Secretase Modulators for Alzheimer’s Disease Treatment. \u003c/strong\u003eA pharmacophore model was constructed based on known γ-secretase modulators, guiding the virtual screening of marine-derived compound libraries to identify potential hits. Selected compounds underwent fragment-based drug design to optimize interactions within the γ-secretase binding site, followed by molecular docking to assess binding affinities. Final candidates were evaluated through in silico ADMET profiling to predict drug-likeness and therapeutic potential, supporting the discovery of novel modulators for Alzheimer’s disease treatment.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/cd34251dc7ef97bb330cd38e.png"},{"id":71528073,"identity":"7905b6ca-7e21-495e-ab90-5f45abb2ec38","added_by":"auto","created_at":"2024-12-16 12:40:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":300802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular conformers of BMS 299897 and ELN 318463, highlighting the common 4-chlorobenzenesulfonamide ring.\u003c/strong\u003e Structural comparison of γ-secretase modulators (a) BMS 299897 and (b) ELN 318463, focusing on the shared 4-chlorobenzenesulfonamide ring. Each compound’s structure was deconstructed to highlight key pharmacophore features, with the 4-chlorobenzenesulfonamide ring identified as a crucial component for pharmacological activity. Alongside this common ring, each compound also contains unique structural features: BMS 299897 includes a fluorophenyl group and a butanoic acid chain, while ELN 318463 features a bromophenyl group and a piperazine ring. These additional groups contribute to each compound’s distinct properties and binding interactions, but the 4-chlorobenzenesulfonamide ring remains a primary focus for designing new γ-secretase modulators.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/ba1ed70676c68dbdc77de722.png"},{"id":71529495,"identity":"41ccd46c-33c0-46fc-bda2-c795d41e5270","added_by":"auto","created_at":"2024-12-16 12:57:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11311112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/f128254b-38d8-427c-bed1-1d7841692125.pdf"},{"id":71526513,"identity":"6bf3192b-f93c-4aa4-9360-7d7ccdee72ba","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":223203,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/3ed02d57cc79c8c9b654f6a4.xlsx"},{"id":71526508,"identity":"6d8874c1-59b9-46e3-b2f0-fc22b80f993b","added_by":"auto","created_at":"2024-12-16 12:32:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11738,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/ca80c1760df4d338c9fbe45b.xlsx"},{"id":71528033,"identity":"0ed9d442-9089-4042-87ec-00a1b996f846","added_by":"auto","created_at":"2024-12-16 12:40:52","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25164,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5369025/v1/3306bec1a6b33c04fa742e60.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pharmacophore-Guided Computational Modeling of Marine-Derived γ-Secretase Modulators for Amyloid-Beta Reduction in Alzheimer’s Disease","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. Marine-derived γ-secretase modulators were identified using pharmacophore-based virtual screening, targeting amyloid-beta reduction for Alzheimer\u0026rsquo;s treatment.\u003c/p\u003e\u003cp\u003e2. Fragment-based drug design integrated bioactive elements from top-screened marine compounds with a key structural feature (4-chlorobenzenesulfonamide ring) for novel inhibitor development.\u003c/p\u003e\u003cp\u003e3. The newly designed modulators demonstrated strong binding affinities and favorable pharmacokinetics, including the ability to cross the blood-brain barrier.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) represents a multifaceted neurodegenerative condition distinguished by a gradual deterioration of cognitive functions, memory impairment, and ultimately, a decline in independence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the most common variant of dementia, it impacts over 50\u0026nbsp;million people globally, with estimations indicating that this figure may surpass 152\u0026nbsp;million by the year 2050 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pathophysiological mechanisms underlying AD entail the buildup of amyloid-beta plaques, tau neurofibrillary tangles, neuroinflammatory responses, and synaptic dysfunction, culminating in neurodegeneration and cognitive deficits [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmyloid-beta plaques are extracellular deposits primarily composed of aggregated Aβ42 peptides [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These plaques accumulate in the brain and disrupt neuronal communication, leading to synaptic dysfunction, neuroinflammation, and eventually neuronal death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The overproduction and aggregation of Aβ42 are driven, in part, by dysregulation of gamma-secretase activity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Gamma-secretase constitutes a multiprotein complex that is integral to the proteolytic cleavage of various type I transmembrane proteins, notably including the amyloid precursor protein (APP). This cleavage process yields amyloid-beta (Aβ42) peptides, which subsequently aggregate to form plaques [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The principal constituents of the gamma-secretase complex comprise presenilin (PSEN), nicastrin, anterior pharynx-defective 1 (APH-1), and presenilin enhancer 2 (PEN-2) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these elements, presenilin 1 (PSEN1) is of particular importance, given that changes in the PSEN1 gene are related with familial variants of AD, resulting in an elevated production of the deleterious Aβ42 peptide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The importance of gamma-secretase in AD pathogenesis has made it a target for therapeutic intervention. However, gamma-secretase also cleaves several other essential substrates, such as Notch receptors, which are involved in cell signaling and development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Complete inhibition of gamma-secretase could therefore result in serious side effects such as impairing notch signaling pathway [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, current research is focused on identifying gamma-secretase modulators (GSMs) that selectively reduce Aβ42 production without affecting other substrates, offering a more targeted approach to reducing amyloid plaque formation. A few modulators have been identified that can affect gamma-secretase functionality, either reducing Aβ42 production or avoid inhibition of notch signaling pathway [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, their failure in clinical trials underscores the need for more precise and effective therapeutic strategies. Therefore, in this study, we aimed to identify novel GSMs with improved specificity and efficacy in treating AD.\u003c/p\u003e \u003cp\u003eWe utilized computational methods in this study for the virtual screening of an extensive library of marine-derived compounds. Marine bioactive compounds, extracted from a diverse array of marine life including algae, sponges, and corals, have attracted scholarly interest for their prospective therapeutic implications in AD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These compounds display a range of biological activities, encompassing antioxidant, anti-inflammatory, and neuroprotective properties, positioning them as promising candidates for the formulation of AD therapeutics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For example, polyphenols derived from marine sources have demonstrated efficacy in diminishing amyloid-beta aggregation and modulating tau phosphorylation, thereby addressing pivotal pathological features of the disorder [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the distinctive chemical configurations of these marine compounds present avenues for the exploration of novel mechanisms of action that could enhance pre-existing therapies or unveil new treatment pathways.\u003c/p\u003e \u003cp\u003eTo guide our design process, we focused on BMS 299897 which has been well-documented for its ability to reduce Aβ production [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, its interference with Notch signaling and other pathways raises concerns about potential side effects [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, in cellular experiments, the APP-selective gamma-secretase inhibitor ELN318463 has a 75\u0026ndash;120-fold preference for blocking Aβ synthesis above its impact on Notch signaling. It works as a traditional gamma-secretase inhibitor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Both the inhibitors showed to interact with the PSEN1 subunit of gamma-secretase [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence, by utilizing these compounds as reference models, we aimed to identify novel molecules that selectively target PSEN1 without disrupting Notch signaling. This strategic approach enhances the therapeutic potential of our synthesized modulators, potentially leading to safer and more effective treatments for Alzheimer\u0026rsquo;s disease. In the present investigation, we established a pharmacophore model employing BMS 299897 and ELN 318463 for screening marine compounds library. Following the identification of prospective hit compounds, we employed fragment-based drug design methodologies to synthesize modulators by amalgamating bioactive fragments with a critical structural element derived from the reference drugs. The newly synthesized modulators underwent further scrutiny via molecular docking to evaluate their binding affinities and pharmacokinetic characteristics. Ultimately, we performed a thorough comparison of the synthesized modulators with the original γ-secretase modulators to ascertain their potential efficacy as viable treatments for Alzheimer's disease.\u003c/p\u003e"},{"header":"2. Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Generation and Analysis of Individual Pharmacophore Maps for \u0026gamma;-Secretase Inhibitors\u003c/h2\u003e\n \u003cp\u003eThe pharmacophore map of the two \u0026gamma;-secretase Inhibitors, BMS 299897 and ELN 318463, was generated individually. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents a detailed breakdown of the key pharmacophoric features identified for each compound, along with the features of the Shared Feature Pharmacophore (SFP) model.\u003c/p\u003e\n \u003cp\u003eThe individual pharmacophore maps of BMS 299897 and ELN 318463 revealed several common features, with hydrophobic regions (HPho), aromatic moieties (Ar), and halogen bond donors (XBD) being consistent across both inhibitors. However, they differ slightly regarding hydrogen bond donors (HBD) and acceptors (HBA), with BMS 299897 lacking any hydrogen bond donors, while ELN 318463 has one.\u003c/p\u003e\n \u003cp\u003eThe Shared Feature Pharmacophore (SFP) model was generated by aligning the individual maps of both compounds, leading to a consensus model that retained the key features from each inhibitor. The final SFP model reflects the features common between the two, including 1 hydrogen bond donor, 3 hydrogen bond acceptors, 4 hydrophobic regions, 2 aromatic bonds, and 2 halogen bond donors, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThese features were aligned using the pharmacophore alignment algorithms, resulting in the final SFP model that retains the key interacting regions of both inhibitors (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). The alignment of BMS 299897 and ELN 318463 was conducted, accompanied by their corresponding pharmacophore maps. The 2D and 3D visualizations of these pharmacophore maps are presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, respectively. By overlapping these features, a unified SFP model was generated. This model retained the critical pharmacophoric elements from both drugs, which are essential for their interaction with the \u0026gamma;-secretase enzyme. The resulting pharmacophore map can now serve as the basis for further virtual screening and identification of novel \u0026gamma;-secretase inhibitors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Ligand Library Screening and Identification of Potential Hits\u003c/h2\u003e\n \u003cp\u003eA ligand library consisting of 43,212 compounds from marine sources was generated from the Comprehensive Marine Natural Products Database (CMNPD). The entire library was converted into .ldb format and prepared for the first round of screening using the SFP model developed earlier. After running the virtual screening, six compounds were identified as potential hits, fitting well with the SFP model based on their pharmacophore features. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the top five hit compounds along with their pharmacophore fit scores. These scores indicate how well each compound\u0026apos;s pharmacophoric features match the key interaction points within the SFP model. The higher the score, the better the fit with the model. The pharmacophore fit score measures how well each compound\u0026apos;s features match the key elements of the SFP model. For example, CMNPD10454 achieved the highest score of 110.3849, indicating a nearly perfect match with the pharmacophoric features, such as HBA, HPho, and Ar, making it a strong candidate for \u0026gamma;-secretase inhibition (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eSimilarly in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, the alignment of the SFP model with CMNPD10455 is shown and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed shows the fit of the model with CMNPD10456 and CMNPD10457 respectively. As the six compounds are highly specific to the SFP map, their structural complexity makes them unsuitable as direct drug candidates. To address this, we created synthetic models of \u0026gamma;-secretase modulators by blending the bioactive portions of these hit compounds with the 4-chlorobenzenesulfonamide ring from BMS 299897 and ELN 318463, as explained in Method Section \u003cspan class=\"InternalRef\"\u003e4.3\u003c/span\u003e. This approach helps retain the essential pharmacophoric features while simplifying the structure to make the compounds more viable as potential drugs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Conjugation of Screened Compounds with the 4-Chlorobenzenesulfonamide Ring and Chemical Scaffold Analysis\u003c/h2\u003e\n \u003cp\u003eFor this study, the \u0026gamma;-secretase inhibitors BMS 299897 and ELN 318463 were chosen as test sets. Using the criteria from Section \u003cspan class=\"InternalRef\"\u003e4.3\u003c/span\u003e, the Alvabuilder software was utilized to generate 50 new molecular models. During this process, the 4-chlorobenzenesulfonamide ring (SMILES: ClC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C1)S(=\u0026thinsp;O)(=\u0026thinsp;O)N) was kept fixed, and new fragments from the CMNPD-screened compounds were added. The full list of these compounds and their respective scores can be found in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, with the top five compounds displayed in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. These top compounds were evaluated for their binding characteristics and molecular structure. The similarity score between the new models and the original \u0026gamma;-secretase inhibitors exceeded 50%, with about half of the similarity attributed to the 4-chlorobenzenesulfonamide ring.\u003c/p\u003e\n \u003cp\u003eMany of the new compounds incorporated pyrrole rings or fused with aliphatic segments. To avoid halogen overloading, we limited the halogens to a maximum of four, considering the presence of chlorine in the original ring. The SAScore, a measure of synthetic feasibility, was kept below 5 for most models, with many below 4.15, indicating strong practical viability for synthesis. All 50 synthetic models were selected for further molecular docking analysis to identify potential drug candidates. The SMARTS.plus tool was used to compare three newly designed compounds with the chemical scaffold of BMS 299897 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). As shown in Figs. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec, the 4-chlorobenzenesulfonamide ring was the common feature between the predicted molecules and the control drug, while the rest of the fragments were newly added.\u003c/p\u003e\n \u003cp\u003eUsing Alvabuilder, we maintained the crucial 4-chlorobenzenesulfonamide ring and integrated new bioactive fragments, aiming to enhance the effectiveness of \u0026gamma;-secretase inhibitors for potential drug development.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Virtual Screening and Molecular Interaction Analysis of Selected Compounds\u003c/h2\u003e\n \u003cp\u003eIn the second round of virtual screening, we tested the 50 newly designed \u0026gamma;-secretase modulator models against the 5A63 protein using PyRx. We performed docking selectively with the PSEN1 subunit of the gamma secretase enzyme. The results, summarized in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, showed that nearly all compounds had better docking scores than the control drug BMS 299897, which had a docking score of -8.9 kcal/mol. The top performers were Molecule 6, Molecule 24, and Molecule 28, all of which not only demonstrated strong docking scores (ranging from \u0026minus;\u0026thinsp;10.8 to -9.6 kcal/mol) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) but also passed additional ADMET analysis (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e), suggesting they can cross the Blood-Brain Barrier (BBB) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the docking energy and interaction details for the top compounds are compared with the control drug BMS 299897. The control drug forms hydrogen bonds with amino acids ASN55, ASN142, GLY144, and ASP336, while also establishing hydrophobic interactions with HIS58, ALA56, and VAL138. These interactions play a significant role in its binding efficiency to the 5A63 protein. For Molecule 6, although no hydrogen bonds were observed, the compound exhibited strong hydrophobic interactions with several key residues such as PHE411, VAL94, LEU35, ALA39, and ILE408. The extensive hydrophobic interactions strengthen the overall binding, contributing to its superior docking score of -10.8 kcal/mol.\u003c/p\u003e\n \u003cp\u003eSimilarly, Molecule 24 formed no hydrogen bonds but showed significant hydrophobic interactions with residues like ILE690, LEU169, PHE173, and TYR119, contributing to its strong binding and docking score of -9.7 kcal/mol. Molecule 28 displayed similar interaction patterns to Molecule 24, forming hydrophobic contacts with ILE690, LEU169, PHE173, ALA694, and PHE682, resulting in a docking score of -9.6 kcal/mol. These interactions highlight the compound\u0026rsquo;s strong binding potential to the 5A63 protein.\u003c/p\u003e\n \u003cp\u003eIn summary, the top compounds identified through virtual screening showed stronger binding affinities than the control drug due to their extensive hydrophobic interactions with the 5A63 protein. This interaction is crucial for stabilizing the ligand within the active site, making these compounds strong candidates for further investigation as \u0026gamma;-secretase modulators.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Molecular Dynamics Simulation Outcomes\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.1. RMSD Analysis\u003c/h2\u003e\n \u003cp\u003eThe root mean square deviation (RMSD) metrics for the \u0026gamma;-secretase-molecule complexes were computed to ascertain the stability of these complexes over time. The observations indicated that the \u0026gamma;-secretase-Molecule-6, \u0026gamma;-secretase-Molecule-24, \u0026gamma;-secretase-Molecule-28, and \u0026gamma;-secretase-BMS-299897 complexes exhibited average RMSD values of 5.19\u0026Aring;, 4.03\u0026Aring;, 5.65\u0026Aring;, and 4.64\u0026Aring;, respectively, throughout the entire simulation duration (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). The most pronounced deviation was recorded for the \u0026gamma;-secretase-Molecule-6 complex at 49 ns, with a peak RMSD value of 7.05\u0026Aring;; nevertheless, a trend of decreasing structural deviation was noted post 52 ns. A comparable RMSD trend was also observed for the \u0026gamma;-secretase-Molecule-28 complex as well as the reference complex. Among the four complexes analyzed, the \u0026gamma;-secretase-Molecule-24 complex exhibited the least deviation, maintaining a relatively stable profile throughout the simulation in comparison to the control.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.2. RMSF Analysis\u003c/h2\u003e\n \u003cp\u003eThe root-mean-square fluctuation (RMSF) values were derived to evaluate the degree of flexibility within the complexes during the simulation timeframe. In contrast to RMSD, which monitors positional variances across entire structures over time, RMSF specifically quantifies the fluctuations of individual amino acid residues throughout the simulation process. This analysis offers valuable insights into the dynamic alterations occurring within the amino acid residues of the protein chain amid protein-ligand interactions. From the findings, it can be deduced that both the control and the predicted complexes exhibited a comparable degree of flexibility within the protein architecture throughout the 100 ns simulation. The majority of residues demonstrated minimal variability in RMSF values, indicating the overall stability of the complexes. The control complex exhibited greater deviations of 9.706 \u0026Aring;, 10.839 \u0026Aring;, 9.714 \u0026Aring;, and 9.703 \u0026Aring; at the ASN204, GLY205, SER206, and VAL_261 residues, respectively. Among the tested molecules, Molecule-6 displayed the highest deviations of 9.4 \u0026Aring;, 9.202 \u0026Aring;, and 9.607 \u0026Aring; at GLY205, SER206, and SER242, respectively. The average RMSF values recorded were 2.07 \u0026Aring; for \u0026gamma;-secretase-Molecule-6, 1.76 \u0026Aring; for \u0026gamma;-secretase-Molecule-24, 1.81 \u0026Aring; for \u0026gamma;-secretase-Molecule-28, and 2.17 \u0026Aring; for the \u0026gamma;-secretase-Control complex (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.3. Radius of Gyration Evaluation\u003c/h2\u003e\n \u003cp\u003eAn analysis of the Radius of Gyration (Rg) was performed to assess the compactness and rigidity of the drug-protein complexes. Within the framework of interactions between proteins and small molecules, the atomic configuration along the axis is evaluated through the examination of the radius of gyration (Rg). Rg serves as a paramount predictive model, facilitating the calculation and conceptualization of the overall compactness of the complex throughout the simulation duration. In terms of compactness and rigidity, the \u0026gamma;-secretase-Molecule-24 complex exhibited a lower Rg value compared to the other three complexes, with all molecular complexes reflecting lower values than the control complex. The determined Rg values for Molecule 6, Molecule 24, Molecule 28, and the control complex in relation to the target protein were (36.75\u0026ndash;38.81), (37.41\u0026ndash;38.68), (36.87\u0026ndash;39.11), and (37.91\u0026ndash;39.50) \u0026Aring;, respectively, indicating that the interactions with ligands were compact, resulting in minimal structural alterations in the protein binding sites in contrast to the control (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.4. SASA Evaluation\u003c/h2\u003e\n \u003cp\u003eWe executed an analysis of the Solvent Accessible Surface Area (SASA) of the complexes to examine the expansion of the surface area during the simulation. The \u0026gamma;-secretase-Molecule-6 complex presented an average surface area of 51345.42 \u0026Aring;\u0026sup2;, while the \u0026gamma;-secretase-Molecule-24, \u0026gamma;-secretase-Molecule-28, and \u0026gamma;-secretase-BMS 299897 exhibited surface areas of 51666.00 \u0026Aring;\u0026sup2;, 50394.39 \u0026Aring;\u0026sup2;, and 50707.09 \u0026Aring;\u0026sup2;, respectively. Notably, the surface area of the \u0026gamma;-secretase-Molecule-28 complex demonstrated a decrease after 13 ns, continuing to diminish over time. Conversely, the surface area of the \u0026gamma;-secretase-control complex exhibited fluctuations alongside a declining trajectory after 84 ns until the simulation\u0026apos;s conclusion. Initially, the surface area of \u0026gamma;-secretase-Molecule-6 was less than that of the \u0026gamma;-secretase-Molecule-24 complex; however, after 24 ns, both displayed a comparable decreasing trend over the designated time frame (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.5 Intermolecular Interactions\u003c/h2\u003e\n \u003cp\u003eThe intermolecular interactions within the protein-ligand complexes were analyzed through a 100 ns simulation. Hydrogen bonds, hydrophobic interactions, ionic bonds, and water bridges are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. In the \u0026gamma;-secretase-control complex (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea), transient hydrogen bonds were noted at active site residues THR90 and THR10, while a more persistent hydrogen bond interaction was observed at ILE135. Beyond hydrogen bonds, hydrophobic interactions were significant among the amino acids at the active site. For the \u0026gamma;-secretase-Molecule-6 complex (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), hydrogen bonds were detected at THR407 and ILE127, alongside prominent hydrophobic interactions at PHE411 and ILE127. In the \u0026gamma;-secretase-Molecule-24 complex (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec), a hydrogen bond was identified at CYS4, with ionic interactions noted at LEU243 and LEU244. For the \u0026gamma;-secretase-Molecule-28 complex (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed), a hydrogen bond was formed at LEU244, with considerable hydrophobic interactions also observed.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eSelective inhibition of \u0026gamma;-secretase complexes is critical for treating Alzheimer\u0026rsquo;s disease, as this enzyme is responsible for the production of amyloid-beta (A\u0026beta;) peptides, which accumulate and form plaques in the brains of affected individuals [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The active site of \u0026gamma;-secretase resides within its PSEN1 subunit, making it a key target for drug development [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. While previous efforts have led to the creation of \u0026gamma;-secretase inhibitors and modulators aimed at PSEN1, these drugs have faced significant challenges, particularly due to off-target effects. Notably, many of these compounds inadvertently affect Notch signaling pathways, which can lead to adverse effects in the gastrointestinal system and other tissues [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn this study, we explore the potential of marine-derived metabolites in the search for more selective and effective PSEN1-targeted \u0026gamma;-secretase modulators. Marine natural products are known for their structural diversity, offering unique features not commonly found in terrestrial organisms [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. This natural chemical diversity provides an opportunity to discover novel compounds that might exhibit greater specificity for PSEN1, reducing off-target interactions and associated side effects. By leveraging the distinct characteristics of marine metabolites, we aim to identify compounds that can selectively inhibit \u0026gamma;-secretase activity without affecting other critical pathways, such as Notch signaling, thus offering a promising new direction in Alzheimer\u0026rsquo;s disease drug discovery.\u003c/p\u003e\n\u003cp\u003eBy focusing on this therapeutic target, the research leveraged the known modulators BMS 299897 and ELN 318463 to develop a Shared Features Pharmacophore (SFP) model, which was used as a reference for the discovery of novel modulators. The ligand-based pharmacophore modeling approach allowed for the identification of key pharmacophoric features such as hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), hydrophobic regions (HPho), aromatic moieties (Ar), and halogen bond donors (XBD). The alignment of these features in BMS 299897 and ELN 318463 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec) resulted in a robust SFP model, reflecting the critical interaction points necessary for \u0026gamma;-secretase modulation. This model was instrumental in the virtual screening of over 43,000 marine-derived compounds from the Comprehensive Marine Natural Products Database (CMNPD), leading to the identification of six compounds with promising pharmacophoric fit scores (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These compounds may be ineffective against Alzheimer\u0026rsquo;s disease due to their structural complexity and poor permeability across the blood-brain barrier, which limits the0ir ability to reach the brain and exert therapeutic effects. One of the key findings from this study was the identification of the 4-chlorobenzenesulfonamide ring as a central structural feature in both BMS 299897 and ELN 318463. This group contributed significantly to hydrophobic interactions and was retained in the design of new synthetic \u0026gamma;-secretase modulators.\u003c/p\u003e\n\u003cp\u003eUsing fragment-based drug design and QSAR modeling, 50 new synthetic models were generated, incorporating bioactive portions of the identified marine compounds. The top-performing models showed not only structural similarity to known modulators but also favorable drug-like properties as per Lipinski\u0026rsquo;s Rule of Five, synthetic feasibility (SAScore\u0026thinsp;\u0026le;\u0026thinsp;5), and pharmacokinetic parameters (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the 50 modeled compounds, we identified 3 that were predicted to be BBB (blood-brain barrier) permeable. The identification of BBB-permeable compounds is particularly important since many promising drug candidates fail due to their inability to cross the BBB [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The examination of chemical scaffolds and the comparative analysis of structures among the newly designed compounds revealed similarity in their chemical motifs and scaffolds of the anticipated pharmaceuticals with the control (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMolecular docking analysis revealed several synthetic compounds with stronger binding affinities than the control drug BMS 299897, further validating the efficacy of the newly designed molecules. Molecule 6, in particular, exhibited a docking energy of -10.8 kcal/mol, outperforming the control with extensive hydrophobic interactions with residues such as PHE411, VAL94, and ILE408 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, we employed precision docking targeting the specific region of \u0026gamma;-secretase containing its active site. The interaction within the PSEN1 subunit\u0026apos;s active site suggests that the designed molecules have the potential to modulate \u0026gamma;-secretase activity effectively. Interestingly, none of the top-performing compounds formed hydrogen bonds with the active site residues, suggesting that hydrophobic interactions may be the dominant factor in their inhibitory action.\u003c/p\u003e\n\u003cp\u003ePharmacokinetic analysis further strengthened the potential of these compounds as viable drug candidates. Molecules 6, 24, and 28 demonstrated favorable drug-like properties, including high gastrointestinal absorption and the ability to cross the blood-brain barrier (BBB), which is crucial for CNS-targeted therapies (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, these compounds met key drug-likeness criteria, such as Lipinski\u0026apos;s, Ghose, and Veber rules, indicating a high likelihood of success in preclinical trials (Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe molecular dynamics simulations of \u0026gamma;-secretase bound to Molecule-6, Molecule-24, Molecule-28, and the control compound BMS-299897 provide detailed insights into the stability, flexibility, compactness, and surface behavior of these complexes over time. By evaluating the results of RMSD, RMSF, Rg, and SASA analyses, we can assess the potential effectiveness and stability of these molecules in binding to the \u0026gamma;-secretase enzyme. The RMSD values of the \u0026gamma;-secretase-ligand complexes reveal the structural stability of the complexes throughout the simulation. A lower RMSD value generally indicates greater structural stability. Among the complexes, \u0026gamma;-secretase-Molecule-24 exhibited the lowest average RMSD (4.03 \u0026Aring;), indicating that it induced the least structural deviation in the protein, suggesting a more stable interaction (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). RMSF analysis is also crucial for assessing the flexibility of individual residues during the simulation. The \u0026gamma;-secretase-Control complex displayed higher fluctuations at residues ASN204, GLY205, SER206, and VAL261, indicating that these regions are more flexible, potentially leading to instability. Molecule-6 exhibited the greatest fluctuations at GLY205, SER206, and SER242, reflecting some degree of local flexibility. Molecule-24 showed lowest RMSF values (1.76 \u0026Aring;), implying better overall stability across the protein chain. This lower fluctuation suggests a stronger and more stable interaction, which aligns with its favorable RMSD profile (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). Rg values provide information about the compactness and rigidity of the complexes. The Rg values indicated that Molecule-24 (37.41\u0026ndash;38.68 \u0026Aring;) resulted in a more compact complex compared to Molecule-6 (36.75\u0026ndash;38.81 \u0026Aring;), Molecule-28 (36.87\u0026ndash;39.11 \u0026Aring;), and the control (37.91\u0026ndash;39.50 \u0026Aring;). A lower Rg value is generally favorable, as it suggests a more compact complex, implying that the ligand has induced less structural change in the protein. Molecule-24 had the highest average SASA value (51666.00 \u0026Aring;\u0026sup2;), followed by Molecule-6 (51345.42 \u0026Aring;\u0026sup2;), indicating that these complexes maintained relatively consistent exposure to solvent throughout the simulation (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). Molecule-28 showed a decreasing trend after 13 ns, suggesting some level of contraction or reduced exposure over time. The control complex displayed fluctuations and a declining trend after 84 ns, possibly indicating some instability. In case of intermolecular bonds, hydrogen bonds and hydrophobic interactions were key contributors to the stability of these complexes (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Molecule-6 formed hydrogen bonds with THR407 and ILE127, along with significant hydrophobic interactions with PHE411 and ILE127. Molecule-24 formed hydrogen bonds at CYS4, with ionic interactions at LEU243 and LEU244. In comparison, Molecule-28 formed a single hydrogen bond at LEU244, with prominent hydrophobic interactions, while the control complex showed less consistent hydrogen bonding. Based on the analysis of RMSD, RMSF, Rg, SASA, and intermolecular bonds, Molecule-24 appears to be the best candidate for binding \u0026gamma;-secretase, producing better results than the control complex. It demonstrated the most stable RMSD profile, the lowest RMSF values, maintained a compact structure throughout the simulation (low Rg), and had consistent solvent exposure (SASA). The strong intermolecular interactions, particularly hydrogen bonds and ionic bonds, further support its efficacy as a ligand.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eThe methodology of the study to identify novel γ-secretase modulators for Alzheimer's disease have been illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Retrieval of γ-secretase inhibitors for Ligand-based Pharmacophore Modeling\u003c/h2\u003e \u003cp\u003eIn this study, ligand-based pharmacophore modeling [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was employed to develop a pharmacophore map targeting the γ-secretase inhibitors BMS 299897 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and ELN318463 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] ( Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The software Ligandscout v4.4 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was used to generate a Shared Features Pharmacophore (SFP) model (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.inteligand.com/ligandscout/\u003c/span\u003e\u003cspan address=\"https://docs.inteligand.com/ligandscout/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Initially, individual pharmacophore maps for BMS 299897 and ELN318463 were created. This process involved identifying and mapping key chemical features, such as hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic regions (HyPho), aromatic moieties (Ar), and halogen bond donors (XBD), which are important for their interaction with γ-secretase [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The pharmacophore maps were generated by analyzing the Inhibitors' three-dimensional structures and detecting the spatial arrangement of these features. Using Ligandscout\u0026rsquo;s feature alignment algorithms, the individual maps were then aligned to highlight common features between the two inhibitors. This alignment step allowed us to superimpose and combine the most critical pharmacophoric elements from both ligands. This model was further used for virtual screening to identify novel γ-secretase modulators based on the identified features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Ligand Library Preparation and Pharmacophore-based Virtual Screening\u003c/h2\u003e \u003cp\u003eFor the virtual screening process, a ligand library was prepared using the Comprehensive Marine Natural Products Database (CMNPD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cmnpd.org/\u003c/span\u003e\u003cspan address=\"https://www.cmnpd.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which contains about 47,451 compounds derived from diverse marine resources. The entire dataset of compounds was initially converted to the MOL2 format using OpenBabel v2.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openbabel.org/\u003c/span\u003e\u003cspan address=\"https://openbabel.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), ensuring compatibility with standard molecular modeling tools. Since Ligandscout requires a specific library format, the MOL2-formatted dataset was further converted to .ldb format within Ligandscout [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The prepared .ldb library of 43,212 compounds after removing duplicates was then employed for virtual screening against the previously generated SFP model. The screening process aimed to identify compounds that fit the pharmacophoric features of the γ-secretase inhibitors BMS 299897 and ELN318463. This initial round of screening provided a subset of compounds that exhibited high potential for interaction with γ-secretase based on the SFP model features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. De Novo Molecular Descriptors Design of Synthetic γ-secretase modulators\u003c/h2\u003e \u003cp\u003eAfter the initial virtual screening, we identified several promising hits that strongly interacted with the Shared Features Pharmacophore (SFP) map. However, since these hits were directly obtained from raw data, their complex structures raised concerns about their practical effectiveness as γ-secretase modulators. Despite this, their inclusion as potential hits indicated that certain features could be valuable in designing new synthetic modulators. Both BMS 299897 and ELN318463 share a common structural element, the 4-chlorobenzenesulfonamide ring [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e), which plays a key role in hydrophobic interactions. Using this shared feature, we aimed to generate synthetic models of γ-secretase modulators. We employed Alvabuilder [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a toolkit from Alvascience [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which uses genetic algorithms for de novo molecular design. The process included fragment-based design, QSAR modeling, drug energy minimization, stability analysis, and descriptor calculation for newly designed compounds. Two compounds (BMS 299897 and ELN318463) were used as the training set, while the hit compounds served as the test set. During the molecular design process, 50 new models were generated by keeping the 4-chlorobenzenesulfonamide ring fixed in all designs. Other important conditions, such as Lipinski's Rule of Five (molecular weight\u0026thinsp;\u0026le;\u0026thinsp;500 g/mol, hydrogen bond acceptors\u0026thinsp;\u0026le;\u0026thinsp;5, hydrogen bond donors\u0026thinsp;\u0026le;\u0026thinsp;10, lipophilicity\u0026thinsp;\u0026lt;\u0026thinsp;5, and TPSA 20 to 130 \u0026Aring;\u0026sup2;) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], were applied. We also ensured that the SAScore was \u0026le;\u0026thinsp;5, halogen\u0026thinsp;\u0026le;\u0026thinsp;4 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and ESOL was \u0026le;-10 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to prioritize compounds that are both theoretically effective and synthetically feasible. Additionally, the tool was instructed to avoid adding aromatic rings of less than three and to include fragment crossover with 4-chlorobenzene sulfonamide rings, ensuring that functional groups were compatible and capable of forming stable covalent or non-covalent interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe key metric in this design process was the SAScore, which evaluates the synthetic accessibility of a compound. A lower SAScore (range 1 to 5) indicates that a compound is easier to synthesize [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], with our analysis setting the SAScore to \u0026le;\u0026thinsp;5 for optimal synthetic feasibility. In total, 50 synthetic γ-secretase inhibitor models were designed, all retaining the fixed 4-chlorobenzenesulfonamide ring. These models were subsequently screened to identify potential candidates for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Molecular Docking Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1. Preparation of γ-Secretase Protein and Active Site Identification\u003c/h2\u003e \u003cp\u003eWe retrieved the crystal structure of human γ-secretase (PDB ID: 5A63, 3.4 \u0026Aring; resolution) from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. All heteroatoms, water molecules, metal ions, and cofactors were removed from the structure using PyMOL [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To optimize the protein, we performed energy minimization using the Swiss-PDB Viewer tool [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.expasy.org/spdbv/\u003c/span\u003e\u003cspan address=\"http://www.expasy.org/spdbv/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The CASTp web server was used to identify the protein's active site. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sts.bioe.uic.edu/castp/\u003c/span\u003e\u003cspan address=\"http://sts.bioe.uic.edu/castp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] with a default probe radius of 1.4 \u0026Aring;, which helped us calculate the area, volume, and sequence ID of the binding pocket. Additionally, BIOVIA Discovery Studio, a visualizer was used to further confirm the protein's binding sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2. Structure-based Virtual Screening\u003c/h2\u003e \u003cp\u003eTo explore the interactions between our designed compounds and the target protein, we employed molecular docking using PyRx [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyrx.sourceforge.io/\u003c/span\u003e\u003cspan address=\"https://pyrx.sourceforge.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with AutoDock Vina [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] as the docking engine. The drug molecules were prepared as ligands, and the protein was prepared as a macromolecule. Both were converted into pdbqt format in PyRx. The docking grid was centered at X: 125.101, Y: 138.470, Z: 131.2866, with dimensions of X: 80.615, Y: 131.1684, Z: 81.7397 \u0026Aring;. After docking, the compound with the highest binding affinity (measured in kcal/mol) was identified and visualized using BIOVIA Discovery Studio Visualizer for further analysis. During the de novo molecular generation, we applied Lipinski\u0026rsquo;s Rule of Five as a key criterion. To further ensure the compounds could cross the Blood-Brain Barrier (BBB), we also performed additional pharmacokinetic analysis using the Swiss-ADME server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Drug Compatibility Test and Scaffold Analysis\u003c/h2\u003e \u003cp\u003eAfter the final screening, we identified several potential synthetic γ-secretase inhibitor conformers. The next step was to assess the compatibility of these new compounds by comparing them with BMS 299897 as the control drug. We utilized a chemo-informatics approach to analyze and describe the chemical patterns and scaffolds of the predicted drugs in relation to the control. This analysis was conducted using the SMARTS.plus tool [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://smarts.plus/\u003c/span\u003e\u003cspan address=\"https://smarts.plus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) from the ZBH \u0026ndash; Center for Bioinformatics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.zbh.uni-hamburg.de/en.html\u003c/span\u003e\u003cspan address=\"https://www.zbh.uni-hamburg.de/en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which applies a chemical pattern language called SMARTS (SMiles ARbitrary Target Specification). This language helps define specific chemical substructures and interactions within compounds. The SMARTS.plus software allows for the identification and visualization of these patterns, providing a textual and graphical representation of the chemical features and scaffold present in the newly predicted drugs. The scaffold analysis focused on identifying shared and unique chemical frameworks between the newly designed modulators and BMS 299897.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Molecular Dynamics (MD) Simulation of the Selected Protein-ligand Complexes\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulation is conventionally employed to assess the stability of complexes formed between potential pharmaceutical compounds and their target proteins [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In our study, MD simulations were executed over a duration of 100 nanoseconds utilizing the Desmond software suite developed by Schr\u0026ouml;dinger LLC. Prior to the initiation of simulations, the protein-ligand complexes underwent a series of preprocessing steps, encompassing optimization and minimization, facilitated by the Protein Preparation Wizard within the Maestro software. The assembly of the system was accomplished via the System Builder tool. To replicate authentic environmental conditions, we employed the TIP3P solvent model incorporated within an orthorhombic simulation box [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The simulations were conducted using the OPLS_2005 force field, with counter ions introduced as necessary to achieve model neutrality [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In order to simulate physiological conditions, a 0.15 M NaCl solution was incorporated. Throughout the simulation process, equilibrium was maintained through the application of NVT and NPT ensembles, ensuring the conservation of the number of moles (N), pressure (P), and temperature (T) at 310 K and 1 atm, respectively. The models were subjected to pre-simulation relaxing techniques. To assess the stability of the simulations, we computed several metrics, including the radius of gyration (RG), solvent-accessible surface area (SASA), root mean square deviation (RMSD) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], root mean square fluctuations (RMSF), and intermolecular binding interactions for both the control and the three foremost selected complexes.\u003c/p\u003e \u003cp\u003eIn this context, the Root Mean Square Deviation (RMSD) serves as a metric for quantifying the average displacement variation of a selection of atoms for a specific frame relative to a reference frame. It is computed for all frames within the trajectory. The RMSD for a given frame x is expressed as:\u003c/p\u003e \u003cp\u003eRMSD\u003csub\u003ex\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{(\\frac{1}{N}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{N}(\\text{r}{\\prime\\:}\\text{i}\\)\u003c/span\u003e\u003c/span\u003e (t\u003csub\u003ex\u003c/sub\u003e)) - r\u003csub\u003ei\u003c/sub\u003e (t\u003csub\u003eref\u003c/sub\u003e))\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003ewhere N signifies the total number of atoms in the selection; t ref denotes the reference time (typically, the first frame is utilized as the reference, designated as time t\u0026thinsp;=\u0026thinsp;0); and r' represents the coordinates of the selected atoms in frame x after superimposing onto the reference frame, where frame x is recorded at time tx. This calculation is reiterated for each frame throughout the simulation trajectory.\u003c/p\u003e \u003cp\u003eThe Root Mean Square Fluctuation (RMSF) is instrumental in characterizing localized alterations along the protein chain. The RMSF for residue i is defined as:\u003c/p\u003e \u003cp\u003eRMSF\u003csub\u003ei\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{(\\frac{1}{T}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{t=1}^{T}\u0026lt;(\\text{r}{\\prime\\:}\\text{i}\\)\u003c/span\u003e\u003c/span\u003e (t)) - r\u003csub\u003ei\u003c/sub\u003e (t\u003csub\u003eref\u003c/sub\u003e))\u003csup\u003e2\u003c/sup\u003e \u0026gt;)\u003c/p\u003e \u003cp\u003ewhere T represents the trajectory time interval over which the RMSF is computed, tref indicates the reference time, ri denotes the coordinates of residue i; r' signifies the coordinates of atoms within residue i following superposition on the reference, and the angle brackets denote that the average of the squared distance is computed across the selection of atoms within the residue.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMd. Sakhawat Hossain, Md. Alamin and Md Masuder Rahman: Conceptualization. Md. Sakhawat Hossain and Md. Alamin: Data acquisition; Formal analysis; Writing- original draft. Md. Saruar Alam Sakib, Akhi Akter and Liton Chandra Das: Data acquisition; Writing- original draft. Md. Sakhawat Hossain, Md. Alamin and Md. Nurul Islam: Molecular Dynamics and simulation analysis. Md Masuder Rahman: Writing-review \u0026amp; editing; Final approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any financial support or funding from external sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and resources utilized in this study are available through publicly accessible repositories and platforms. The ligand library used for virtual screening was sourced from the Comprehensive Marine Natural Products Database (CMNPD) and is available at [https://www.cmnpd.org/]. Molecular docking simulations were performed using PyRx (version 0.8) and AutoDock Vina (version 1.1.2), both accessible at [https://pyrx.sourceforge.io/] and [https://autodock.scripps.edu/], respectively. The crystal structure of the \u0026gamma;-secretase enzyme (PDB ID: 5A63) was retrieved from the Protein Data Bank (PDB) at [https://www.rcsb.org/].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest/Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR. 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Theory Comput.\u003c/em\u003e, vol. 13, no. 4, 2017, doi: 10.1021/acs.jctc.7b00028.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Individual pharmacophoric Features of \u0026gamma;-secretase inhibitor and Shared feature pharmacophore (SFP) model.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026gamma;-secretase inhibitor name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003ePharmacophoric Features\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBMS 299897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHBD: 0, HBA: 5, HPho: 4, Ar: 2, XBD: 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eELN 318463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHBD: 1, HBA: 3, HPho: 4, Ar: 2, XBD: 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eSFP Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHBD: 1, HBA: 3, HPho: 4, Ar: 2, XBD: 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Top six hit compounds with their pharmacophore fit scores within the SFP map.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSMILES formula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacophore fit score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD10454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eBrc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O)\u003c/p\u003e\n \u003cp\u003e(OCC)c3c[nH]c4c3ccc(Br)c4)C(=O)OC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e110.3849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD10455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eBrc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O)(OC)c3c\u003c/p\u003e\n \u003cp\u003e[nH]c4c3ccc(Br)c4)C(=O)OC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e87.093346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD10456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eBrc2cc1n(cc(c1cc2)[C@@](S(=O)(=O)O)(O)c3c\u003c/p\u003e\n \u003cp\u003e[nH]c4c3ccc(Br)c4)C(=O)OC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e86.5644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD10457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eBrc4cc3n(cc(S(=O)(=O)c1c[nH]c2c1ccc\u003c/p\u003e\n \u003cp\u003e(Br)c2)c3cc4)C(=O)O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e86.39001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD15725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eClc1c(OC)ccc(c1)[C@H]3[C@H](O)[C@@H](O)\u003c/p\u003e\n \u003cp\u003e[C@@](O)(c2cc(Cl)c(O)cc2)C3=O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e86.31028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCMNPD15122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eBrc2cc1n(cc(c1cc2)[C@H](S(=O)(=O)O)c3c\u003c/p\u003e\n \u003cp\u003e[nH]c4c3ccc(Br)c4)C(=O)OC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e86.07951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eTop five compounds from the generated 50 synthetic models with their average similarity score and SAScore.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"840\" height=\"1044\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eBinding affinity results of reported compounds and their interacted residues with amino acids.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocking energy (kcal/mol) with 5A63 protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bond interaction-AA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic bond interaction-AA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eBMS 299897 (control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eASN55, ASN142, GLY144, ASP336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHIS58, ALA56, VAL138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMolecule 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePHE411, VAL94, VAL412, LEU35, VAL36, ALA39, VAL97, ILE408, PHE411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMolecule 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eILE690, LEU169, PHE173, ILE679, PRO16, LEU20, VAL176, ALA228, PHE682, TYR119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMolecule 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eILE690, LEU169, PHE173, TYR119, ILE690, ALA694, PHE682, PHE698, TYR119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003ePhysiochemical and pharmacokinetics properties of targeted compounds.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003cp\u003e(g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eTPSA (\u0026Aring;\u0026sup2;) and Molar Refractivity (MR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eH. Bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eLipophilicity\u003c/p\u003e\n \u003cp\u003e(Consensus Log Po/w)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eWater Solubility\u003c/p\u003e\n \u003cp\u003e(Log \u003cem\u003eS\u003c/em\u003e (ESOL))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003ePharmacokinetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eDrug likeness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003eMolecule 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e349.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eTPSA: 71.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eAcceptor: 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eGI absorption: High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eLipinski: Yes\u003c/p\u003e\n \u003cp\u003eGhose: Yes\u003c/p\u003e\n \u003cp\u003eVeber: Yes\u003c/p\u003e\n \u003cp\u003eEgan: Yes\u003c/p\u003e\n \u003cp\u003eMuegge: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eMR:89.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eDonor: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBBB permeant: Yes \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003eMolecule 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e428.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eTPSA:71.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eAcceptor: 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eGI absorption: \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eLipinski: Yes\u003c/p\u003e\n \u003cp\u003eGhose: Yes\u003c/p\u003e\n \u003cp\u003eVeber: Yes\u003c/p\u003e\n \u003cp\u003eEgan: Yes\u003c/p\u003e\n \u003cp\u003eMuegge: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eMR: 97.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eDonor: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBBB permeant: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003eMolecule 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e428.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eTPSA: 71.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eAcceptor: 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eGI absorption: \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eLipinski: Yes\u003c/p\u003e\n \u003cp\u003eGhose: Yes\u003c/p\u003e\n \u003cp\u003eVeber: Yes\u003c/p\u003e\n \u003cp\u003eEgan: Yes\u003c/p\u003e\n \u003cp\u003eMuegge: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eMR:97.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eDonor: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBBB permeant: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBMS 299897 (control)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e511.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eTPSA: \u0026nbsp;83.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eAcceptor: 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eGI absorption: \u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eLipinski: Yes\u003c/p\u003e\n \u003cp\u003eGhose: Yes\u003c/p\u003e\n \u003cp\u003eVeber: Yes\u003c/p\u003e\n \u003cp\u003eEgan: Yes\u003c/p\u003e\n \u003cp\u003eMuegge: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eMR: 123.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eDonor: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBBB permeant: \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e Description of \u0026gamma;-secretase Inhibitors used for initial pharmacophore map generation.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSL no.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gamma;-secretase Inhibitor name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubChem CID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIUPAC name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand Formula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eBMS 299897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11249248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e4-[2-[(1R)-1-(N-(4-chlorophenyl)sulfonyl-2,5-difluoroanilino)ethyl]-5-fluorophenyl]butanoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e21\u003c/sub\u003eClF\u003csub\u003e3\u003c/sub\u003eNO\u003csub\u003e4\u003c/sub\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eELN 318463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e46883899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003eN-[(4-bromophenyl)methyl]-4-chloro-N-[(3R)-hexahydro-2-oxo-1H-azepin-3-yl]-Benzenesulfonamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eC\u003csub\u003e19\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eBrClN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"[email protected]","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":"γ-Secretase Modulators, Amyloid-Beta (Aβ), Marine-Derived Compounds, Pharmacophore Modeling, and Alzheimer’s disease","lastPublishedDoi":"10.21203/rs.3.rs-5369025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5369025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease is primarily caused by the accumulation of amyloid-beta (Aβ) proteins, with γ-secretase playing a key role in the formation of Aβ (1\u0026ndash;42). This study aimed to identify novel γ-secretase modulators from marine resources that selectively lower Aβ production. Using BMS 299897 and ELN 318463 as reference drugs, we developed a Shared Feature Pharmacophore (SFP) map featuring 1 hydrogen bond donor, 3 hydrogen bond acceptors, 4 hydrophobic regions, 2 aromatic bonds, and 2 halogen bond donors. Screening a library of 47,451 marine-derived compounds through this map identified six promising hits. Synthetic γ-secretase modulators were designed using fragment-based drug design by integrating bioactive fragments from these hits with the essential 4-chlorobenzenesulfonamide ring of the reference drugs. Molecular docking and pharmacokinetic analyses highlighted three compounds (Molecule 6, Molecule 24, and Molecule 28) with stronger binding affinities than BMS 299897 and favorable blood-brain barrier permeability. Additionally, 100 ns molecular dynamics simulations demonstrated stable conformational dynamics and robust interactions for Molecule 24. While these findings are promising, further experimental validation is necessary to confirm the effectiveness and safety of these compounds as potential Alzheimer\u0026rsquo;s treatments.\u003c/p\u003e","manuscriptTitle":"Pharmacophore-Guided Computational Modeling of Marine-Derived γ-Secretase Modulators for Amyloid-Beta Reduction in Alzheimer’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 12:32:47","doi":"10.21203/rs.3.rs-5369025/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"91298370-bb31-471b-96c9-e6507690b9a6","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41613872,"name":"Biological sciences/Drug discovery/Drug screening/Virtual screening"},{"id":41613873,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimers disease"},{"id":41613874,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-12-16T12:32:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 12:32:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5369025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5369025","identity":"rs-5369025","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}