The target of the study is the gamma-secretase enzyme complex, a molecular machine embedded in cell membranes that performs the final cutting step in the production of amyloid-beta. The complex is built from four proteins: presenilin, nicastrin, APH-1, and PEN-2. When gamma-secretase cleaves the amyloid precursor protein, it can generate the longer and stickier Aβ42 peptide, which aggregates into the extracellular plaques that are a hallmark of Alzheimer’s pathology. Because mutations in the presenilin 1 gene are linked to familial forms of the disease and drive elevated Aβ42 output, the PSEN1 subunit has long been considered the prime site for therapeutic intervention.

Blocking gamma-secretase outright, however, has proven dangerous. The enzyme also processes Notch receptors, which govern cell differentiation and development, and complete inhibition has been associated with gastrointestinal toxicity and other serious side effects, a problem that contributed to the clinical failure of drugs such as semagacestat. The field has therefore shifted toward gamma-secretase modulators, compounds that selectively lower Aβ42 while leaving the processing of other substrates untouched. Even here, progress has been rocky: the modulator E2012 showed strong amyloid reduction but raised concerns about effects on cholesterol metabolism, and BMS-932481 was hampered by liver toxicity in early trials.

To guide their search for better modulators, the researchers focused on two reference compounds, BMS 299897 and ELN318463. Both bind at an allosteric pocket at the interface of transmembrane helices six and seven of the PSEN1 subunit, a region distinct from the catalytic aspartates but positioned to influence how the active site handles its substrate. ELN318463 is particularly notable because, in cell-based assays, it shows a seventy-five to one-hundred-twenty-fold preference for blocking amyloid-beta production over Notch signaling. Using the LigandScout software, the team generated individual pharmacophore maps for each drug, mapping out hydrogen bond donors and acceptors, hydrophobic regions, aromatic rings, and halogen bond donors, and then aligned the two maps to build a shared-feature pharmacophore model that captured the essential interaction points common to both inhibitors.

With this model in hand, the team screened the Comprehensive Marine Natural Products Database, a library of roughly 47,451 compounds sourced from algae, sponges, corals, and other marine organisms. After removing duplicates, 43,212 molecules were virtually screened against the shared pharmacophore over approximately forty-eight hours on a sixty-four-core processor. Six compounds emerged as top hits, with the best, CMNPD10454, achieving a pharmacophore fit score of about 110.4, indicating a near-perfect match with the key interaction features. Marine natural products are prized in drug discovery for their unusual chemical architectures, and many display antioxidant, anti-inflammatory, and neuroprotective activities, making them attractive starting points for new Alzheimer’s therapies.

The raw hits, however, were structurally complex and considered impractical as direct drug candidates. The team therefore turned to fragment-based de novo design. A key observation drove this step: both BMS 299897 and ELN318463 share a 4-chlorobenzenesulfonamide ring that plays a central role in hydrophobic interactions with the enzyme. Using the AlvaBuilder toolkit, which applies genetic algorithms to molecular design, the researchers generated fifty new synthetic modulators by keeping this ring fixed and grafting bioactive fragments from the marine hits onto it. The design constraints included Lipinski’s Rule of Five parameters, a synthetic accessibility score of five or below, limits on halogen count, and estimated aqueous solubility thresholds, all intended to ensure the resulting molecules were both effective in theory and chemically feasible to make.

The fifty designs were then filtered through absorption, distribution, metabolism, and excretion profiling with SwissADME, and only three molecules, numbered 6, 24, and 28, were predicted to cross the blood-brain barrier, a filter that has doomed many otherwise promising central nervous system drug candidates. Molecular docking against the human gamma-secretase crystal structure, using the Protein Data Bank entry 5A63, showed that all three bound more tightly than the control drug BMS 299897, which scored minus 8.9 kilocalories per mole. Molecule 6 achieved the best docking energy at minus 10.6 kilocalories per mole, driven by extensive hydrophobic contacts with residues including PHE411, VAL94, and ILE408, while Molecules 24 and 28 scored minus 9.7 and minus 9.6 respectively. Notably, none of the top compounds formed hydrogen bonds with the active site, suggesting that hydrophobic interactions dominate their binding mode.

Molecular dynamics simulations over one hundred nanoseconds, run with the Desmond software using the OPLS_2005 force field and physiological salt conditions, provided further evidence of stability. Molecule 24 produced the most stable protein-ligand complex, with the lowest root mean square deviation of 4.03 angstroms and the lowest residue fluctuation values, maintaining a compact radius of gyration and consistent solvent exposure throughout the simulation. It also formed a hydrogen bond with CYS4 and ionic interactions with LEU243 and LEU244, indicating robust and persistent binding. Toxicity profiling with the OECD QSAR Toolbox predicted no mutagenicity alerts and stable tautomeric forms for the lead compounds, along with lower bioaccumulation factors than the control drug, although renal toxicity alerts resembling a sulfasalazine-like profile warrant future experimental scrutiny.

An intriguing and unexpected finding emerged from the interaction analysis. While Molecule 6 retained strong contact with PSEN1, consistent with the pharmacophore model that guided its design, Molecules 24 and 28 preferentially anchored to the APH-1 subunit of the gamma-secretase complex instead. The precise role of APH-1 in modulating amyloid-beta generation remains uncertain, but prior structural studies suggest it participates in complex assembly and can influence the conformation of the catalytic subunit. The authors argue that these contacts may represent an alternative mechanism of modulation rather than a flaw in the design strategy, and they emphasize that future wet-laboratory experiments will be needed to determine whether APH-1 interactions contribute functionally to enzyme regulation or are merely incidental.

The study also addressed the practical question of how the lead compounds could actually be synthesized. Using the IBM RXN retrosynthesis platform, which combines template-based and template-free neural network approaches with Monte Carlo tree search, the team mapped a stepwise route for Molecule 24 starting from commercially available 4-chlorobenzene sulfonyl chloride, proceeding through click-chemistry and nucleophilic substitution steps to assemble the final structure. While all of these results remain computational predictions that require experimental validation in laboratory and animal models, the work demonstrates how marine chemical diversity, pharmacophore-guided screening, fragment-based design, and molecular simulation can be woven together to accelerate the hunt for safer Alzheimer’s therapies, offering a template for discovering next-generation gamma-secretase modulators that lower amyloid-beta without disrupting the essential cellular pathways that previous drug candidates damaged.

Subject of Research: Computational design of marine-derived gamma-secretase modulators to reduce amyloid-beta production in Alzheimer's disease

Article Title: In silico pharmacophore-guided modeling of marine-derived γ-secretase modulators for amyloid-beta reduction in Alzheimer's disease

Article References: In silico pharmacophore-guided modeling of marine-derived γ-secretase modulators for amyloid-beta reduction in Alzheimer's disease. (n.d.). https://doi.org/10.1016/j.heliyon.2026.e45453

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45453

Keywords: Alzheimer's disease, gamma-secretase modulators, marine natural products, pharmacophore modeling, molecular docking, molecular dynamics simulation, amyloid-beta, PSEN1, blood-brain barrier, drug discovery, virtual screening, de novo molecular design