In vitro and in silico cholinesterase inhibitory potential of metabolites from Laurencia snackeyi (Weber-van Bosse) M. Masuda

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease that causes deterioration in intelligence and psychological activities. Yet, till today, no cure is available for AD. The marine environment is an important sink of bioactive compounds with neuroprotective potential with reduced adverse effects. Recently, we collected the red algae Laurencia snackeyi from Terumbu Island, Malaysia which is known to be rich in halogenated metabolites making it the most sought-after red algae for pharmaceutical studies. The red alga was identified based on basic morphological characteristics, microscopic observation and chemical data from literature. The purplish-brown algae was confirmed a new record. In Malaysia, this species is poorly documented in Peninsular Malaysia as compared to its eastern continent Borneo. Thus, this study intended to investigate the diversity of secondary metabolites present in the alga and its cholinesterase inhibiting potential for AD. The extract inhibited both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with IC₅₀ values of  14.45 ± 0.34 $\mathrm{\mu }$g mL^-1 and 39.59 ± 0.24 $\mathrm{\mu }$g mL^-1, respectively. Subsequently, we isolated the synderanes, palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3) that was not exhibit potential. Mass spectrometry analysis detected at total of 33 additional metabolites. The computational aided molecular docking using the AChE and BChE receptors on all metabolites shortlisted 5,8,11,14-eicosatetraynoic acid (31) and 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) with best inhibitory properties, respectively with the lowest optimal combination of S-score and RMSD values. This study shows the unexplored potential of marine natural resources, however, obtaining sufficient biomass for detailed investigation is an uphill task. Regardless, there is a lot of potential for future prospects with a wide range of marine natural resources to study and the incorporation of synthetic chemistry, in vivo studies in experimental design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03725-6.

Introduction

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that causes deterioration in intelligence and psychological activities. First described in 1906, it is considered among the highest causes of death from a neurodegenerative disease globally estimated at approximately 60–70% cases. Globally, the number of AD patients is expected to rise three folds from 46.8 million by 2050 due to increase in financial and social burden (Ferreira et al. 2021). Often associated to aging, among early signs of AD are short-term memory loss, communication problems, disorientation, loss of motivation, neglect in self-care, sleep disorders and behavioral issues (Ferreira et al. 2021). With time, chronic symptoms are loss of thinking capability, behavioral disturbances, neuronal death, memory loss, cognitive deficit, and cholinergic dysfunction that are associated to the loss of neurotransmitter acetylcholine (ACh) from the neurons in the central nervous system (Srivastava et al. 2019) Apart from genetic and environment, the cholinesterase (acetyl- and butyrylcholinesterase) enzymes, $\mathrm{\beta }$-amyloid (A$\mathrm{\beta }$) aggregation, protein kinase C, tau protein, proteases with $\mathrm{\beta }$-site amyloid precursor protein cleaving cascade ($\mathrm{\beta }$- and $\mathrm{\gamma }$-secretases), glycogen-synthase kinase 3$\mathrm{\beta }$ (GSK-3$\mathrm{\beta }$), neurodegenerators, and oxidants are recognized as factors for AD (Ghoran et al. 2021). To date, there are no available drugs to stop or alter AD progression, instead only improve its symptoms for a limited time for fortunate patients (Tripathi et al. 2019). Among the common restrictions of readily available drugs (eg. rivastigmine, galantamine and donepezil) for AD are toxicity, short half-life, allergic reactions and high cost (Kabir et al. 2021). In search for pharmacologically active metabolites, much attention is given to the marine environment that is an important sink of structurally diverse chemical groups such as polysaccharides, carotenoids, polyphenols, sterol and alkaloids. Marine derived metabolites have exhibited neuroprotective activities with reduced adverse events (Kabir et al. 2021; Hu et al. 2023). Nowadays, the use of chemo-informatics study and computer-aided drug design (CADD) are the way forward in drug discovery, design, and development. Fast, inexpensive techniques to examine binding interactions, inspect pharmacokinetic houses and bioactivity parameters in search for novel inhibitors with higher biochemical interactions such as the use of molecular docking analysis, pharmacokinetics study and bioactivity predictions are making the search for potent drugs more accessible (Abduljelil et al. 2022). The red algae genus Laurencia is one of the richest marine source of secondary metabolites. Currently there are a total of 146 accepted species taxonomically and is widely distributed in tropical waters. Often associated to degraded parts of the reefs, the chemical diversity of Laurencia had been studied for more than half a decade. It is well established that several species possess unique chemical signatures, for instance, L. snackeyi (Weber-van Bosse) M. Masuda (1997) is considered a producer of synderane type metabolites, L. majuscula is known for its charmigrane type sesquiterpenes, L. similis synthesizes bromoindoles and aristolane while L. nangii is well documented for the production of C₁₅ acetogenins (Palaniveloo and Vairappan 2014; Vairappan et al. 2004; Kamada and Vairappan 2012). In a recent Marine Park dive expedition funded by the Department of Fisheries Malaysia, we collected L. snackeyi specimen at Pulau Terumbu off Port Dickson. This manuscript confirms this finding as a first record from this island and second from Peninsular Malaysia. The only known report was from Pulau Besar, Malacca in 1997 Masuda et al. (1997). This manuscript elaborates the chemical diversity in the L. snackeyi extract via chromatographic isolation and mass spectrometry analysis. The seaweed extract was evaluated for its in vitro potential as an anti-Alzheimer’s agent based on its acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzyme inhibitory activity followed by a molecular docking analysis of all metabolites identified from the extract.

Materials and methods

Sampling site and plant material

Laurencia snackeyi specimens were collected from Terumbu Island, Port Dickson, Negeri Sembilan (02.463456 ${}^{\circ }$N, 101.845939 ${}^{\circ }$E) during the Biodiversity Expedition in Malacca and Negeri Sembilan 2018. Plant materials were collected via SCUBA at the depths of 3–5 m, immediately cleaned-off from foreign matters, rinsed in clean seawater, and kept under cool (< 20 ${}^{\circ }$C) conditions. Underwater, the red alga was recognised by its thick main axes arising from a discoid holdfast and without stolon-like branches. Upright thalli are greenish purple to brownish. Its physical texture is firm and fleshy. The voucher specimen (PSM13824) was prepared and kept in IOES, Universiti Malaya.

Chemical extraction

The partially dried algae (47 g) was extracted in 500 mL of analytical grade methanol (MeOH) (Merck, Germany), concentrated in vacuo and repeatedly partitioned between analytical grade ethyl acetate (EtOAc) (Merck, Germany) and distilled water (H₂O) at a ratio of 1:3. The EtOAc solution was dried over anhydrous sodium sulphate (Na₂SO₄) (Sigma-Aldrich, USA), and evaporated to obtain a dark green paste (200 mg).

Cholinesterase inhibitory activity

Cholinesterase inhibition assays

This assay was carried out according to the modified Ellman’s method (Ellman et al. 1961; Aristyawan et al. 2022; Chang et al. 2023; Zhan et al. 2023). The Laurencia extract was dissolved in methanol to a final concentration of 1 mg mL^-1 and serially diluted (0.01–300 $\mathrm{\mu }$g mL^-1). Sample solutions (25 $\mathrm{\mu }$L) were added to a 96-well microplate, followed by 25 $\mathrm{\mu }$L of 1.5 mM acetylthiocholine iodide (ATCI, Sigma-Aldrich, USA) as a substrate for the AChE enzyme from electric eel type VI-S, and 25 $\mathrm{\mu }$L of butyrylthiocholine iodide (BTCI, Sigma-Aldrich, USA) as a substrate for the BChE enzyme from equine serum. Then 125 $\mathrm{\mu }$L of 3 mM Ellman’s reagent (DTNB, Sigma-Aldrich, USA), Tris-buffer (50 $\mathrm{\mu }$L), and AChE or BChE enzymes (25 $\mathrm{\mu }$L) 0.22 U mL^-1 was added to hydrolyze the substrate. Before measurement, the solutions were shaken for 30 s in a microplate reader (Thermo Scientific Multiskan FC). The yellow color from the product, 5-thio-2-nitrobenzoate, was measured at 405 nm every 5 s for 2 min. Every experiment was carried out in triplicates. Methanol 10% was used as a control. The enzyme activity was calculated as a percentage of the velocity of the sample compared with the negative control. The inhibitory activity was calculated based on Eq. 1.

$$\begin{array}{c}\hfill Inhibition(\%)=((V_{\text{control}}-V_{\text{sample}})/V_{\text{control}})\times 100,\end{array}$$ 1

where V is the mean velocity

Compound isolation and characterization

Laurencia snackeyi extract was subjected to high performance liquid chromatography (HPLC) profiling on a HPLC system (Nexera; Shimadzu, Japan) configured with a pump (LC-40XR), oven (CTO-40S), and a PDA detector (SPD-M40), using a Phenomenex C18(2) column (10 $\times$ 250 mm, 5 $\mathrm{\mu }$m) under gradient mode of MeOH-H₂O (MeOH: 50%, 0–5 min; 70%, 10–15 min; 90%, 20–25 min; 100%, 28–30 min), screened between 190 and 800 nm over a flow rate of 2 mL min^-1. Isolated pure compounds were subjected to ¹H-NMR and ¹³C-NMR experiment on a BRUKER 600 MHz and its corresponding chemical data was compared to literature to confirm identity of isolated metabolites.

Tandem mass spectrometry (MS/MS) analysis

High resolution MS/MS analysis was performed using an Orbitrap mass spectrometry (MS) system (Thermo Fisher Scientific, Waltham, MA, USA). Sample was pre-separated using the Dionex UltiMate 3000 UHPLC system with a Syncronis C18 column (2.1 mm $\times$ 100 mm $\times$ 1.7 $\mathrm{\mu }$m ) before being directed to the MS and analysed under conditions as described in Maran et al. (2021). Acquired data was processed and analysed using the Thermo Scientific Compound Discoverer 3.3 SP1 software with the default settings (Maran et al. 2021). Identification of compound was based on the matching of MS/MS data against mzCloud database. Unmatched signals were attempted with the built-in ChemSpider workflow using accurate mass against Chemical Entities of Biological Interest (ChEBI) (Hastings et al. 2016), ChEMBL (Davies et al. 2015; Mendez et al. 2019), LIPID MAPS^® Structure Database (LMSD) (Sud et al. 2006), National Institute of Standards and Technology (NIST) and PubMed databases. Matching tolerance was limited to 2 ppm mass error and minimum FISh scoring of 50.

Molecular docking

Preparation of receptor proteins and ligands

The two receptor proteins targeted for neurotransmitter choline inhibition selected for modelling were: (1) crystal structure of recombinant human acetylcholinesterase (AChE, 4EY6) with galantamine as reference ligand; (2) human butyrylcholinesterase (BChE, 4BDS) with tacrine as reference ligand (Cheung et al. 2012; Nachon et al. 2013). Suitable updated receptors were obtained from the RCSB PDB website (https://www.rcsb.org/). The binding sites were determined through literature report and predicted using the “Site Finder” algorithm in Molecular Operation Environment (MOE) 2015 software (Chemical Computing Group Inc, 2015), followed by validation with PDBsum (http://www.ebi.ac.uk/pdbsum) for the ligand-protein interactions. The receptor proteins were prepared using MOE 2015 QuickPrep module whereby energy convergence set at Root Mean Square (RMS) gradient of 0.01 kcal/mol/A². The receptors were prepared using Protonate3D, which was set to default to predict the hydrogen coordinates of 3D structures automatically.

For the molecular docking simulation, 33 compounds (1–3, 27–56) identified from L.snackeyi (Figs. 4, 4, 6 and 9) were analysed. All compounds and reference structural data file (.sdf) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and drawn by ChemDraw software. A compound database was created and prepared using MOE 2015 software. The compounds were washed with the default format of MOE 2015 software to eliminate unnecessary atoms and explicit hydrogens were added. The partial charge of the compounds was subjected to MMFF94x, changed into gas phase, hydrogen and lone pair electrons were adjusted. The RMS gradient in energy minimization was set at 0.01 kcal/mol/A².

Fig. 4.

Structures of the syndreans palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3) that was isolated from L. snackeyi extract

Fig. 6.

Structures of L. snackeyi metabolites detected through MS analysis as reported in Table 1 (cont.)

Fig. 9.

Chemical structures of sesquiterpene type metabolites reported from L. snackeyi

After the binding sites were selected and validated by literature reports and PDBsum, they were then viewed by PyMOL Educational 1.3 software (Copyright 2010 Schrodinger, LLC) to confirm the binding cleft of the receptor. The binding site for AChE are Trp86, Gly120, Gly121, Gly122, Glu202, Phe295, Phe297, Tyr337, and His447; while for BChE are Trp82, Glu197, Ala328, Tyr332, Trp430, and His438, both in Chain A of the receptor. The receptor proteins and ligands were docked by using the induced fit method with 100-1 poses (docked 100 times with one best-docked conformation being generated) using the MOE 2015 software.

Docking conformational analysis

The docking results were analyzed for their free binding energy (S-score) and root mean square deviation (RMSD). The S-score is a summary score of all interactions and bonds (e.g., hydrogen bond, van der Waals, electrostatic, solvation, etc). Low S-score indicates a stronger binding affinity between the ligand and protein receptor (Khelfaoui et al. 2021). The RMSD is a comparison between the docked conformation with the reference or with other docked conformation. RMSD with < 2Å is considered an accurate prediction of ligand binding model. The docked conformations 2D diagrams were further visualized and analyzed for their binding interactions using the Discovery Studio 2016 Client software.

Results

Morphological observation

The greenish purple seaweed found growing on coral rubble at depths of 3-5 meters of Terumbu Island (02.463456 ${}^{\circ }$N, 101.845939 ${}^{\circ }$E) where high surge and currents were present was identified as L. snackeyi. One to 6 upright thalli (Fig. 1) appeared from a discoid holdfast and without stolon-like branches. The thalli are greenish purple, firmly fleshy in the living state, and adheres to paper when dried. All characteristics fit the description of L. snackeyi. The light microscope image and morphology of specimen was comparable to report by Masuda et al. (1997). Voucher specimen (PSM13824) was prepared and deposited at the IOES herbarium collection at Universiti Malaya.

Fig. 1.

Dissecting microscope image of Laurencia snackeyi (left) as compared to the microscope image as obtained by Masuda et al. (1997) (right)

Cholinesterase inhibitory potential

The methanol extracted alga (47 g, DW) yielded 200 mg (0.42%) dark-green paste. The L. snackeyi extracts was evaluated in vitro for its inhibition potency towards AChE and BChE through the modified Ellman’s method, using galantamine as a positive control at 100 $\mathrm{\mu }$g mL^-1 concentration. The extract demonstrated a higher inhibition against AChE with  IC₅₀ value of 14.45 ± 0.34 $\mathrm{\mu }$g mL^-1 compared to BChE with IC₅₀ value of 39.59 ± 0.24 $\mathrm{\mu }$g mL^-1. The dose-response curve for L. snackeyi extract against AChE and BChE is shown in Fig. 2. Following this data, we decided to investigate the probable source of cholinesterase inhibitory potential from the list of metabolites identified in the extract with the assistance of molecular docking analysis.

Fig. 2.

Dose-response curve for L. snackeyi extract against AChE (left, IC₅₀ = 14.45 ± 0.34 $\mathrm{\mu }$g mL⁻¹) and BChE (right, IC₅₀ = 39.59 ± 0.24 $\mathrm{\mu }$g mL⁻¹)

Chemical profiling and isolation

The extract (20 mg) was chemically visualised using TLC and analysed using HPLC. A total of six (6) isolates (P1–P6) detected at retention times (RTs) 10.7, 13.9, 15.6, 16.8, 17.3 and 17.7 min, when scanned between UV wavelength 190 and 254 nm were collected and characterized using NMR. From a total of 20 mg extract, between 3 and 4 mg of biomass was obtained from isolates P1, P2 and P5 while P3, P4 and P6 yielded less than 2 mg. Figure 3 shows the HPLC chromatogram as well as the 2D and 3D PDA output for the profiled extract. The ¹H-NMR and ¹³C-NMR data for three of the six (6) isolates; P1, P2 and P5, matched literature for palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3). This finding is also identical to the report by Masuda from Pulau Besar in 1997. The chemical structure for the three compounds are shown in Fig. 4 and the data for the compounds are provided below;

Fig. 3.

Top—HPLC chromatogram of L. snackeyi crude extract; black (190 nm), blue (220 nm), pink (254 nm). Bottom—corresponding PDA output in 2D and 3D

Palisadin A (1)—oil; C₁₅H₂₃O₂Br; ¹H-NMR (CDCl₃, 600 MHz) $\delta$ 1.16 (3H, s, H-15), 0.92 (3H, s, H3-14), 1.26 (3H, s, H-13), 4.36 (1H, dd, J = 13, 13 Hz, H-12), 3.94 (1H, dd, J = 5, 12 Hz, H-10), 2.25 (1H, m, H-9), 1.79 (1H, ddd, J = 3,13,13 Hz, H-8$\alpha$), 1.54 (1H, ddd, J = 3, 3, 13 Hz, H-8$\beta$), 2.05 (1H, m, H- 6), 2.35 (1H, m, H-5), 5.54 (1H, brs, H-4), 4.82 (1H, brs, H-2), 3.43 (1H, dd, J = 8, 8 Hz, H-1$\beta$), 4.06 (1H, dd, J = 8, 8 Hz, H-1$\alpha$),; 13C - NMR (CDCl₃, 150 MHz) $\delta$ 31.58 (q, C-15), 18.65 (q, C-14), 22.59 (q, C-13), 71.70 (d, C-12), 41.64 (s, C-11), 66.95 (d, C- 10), 33.36 (d, C-9), 38.22 (t, C-8), 78.61 (s, C-7), 52.47 (d, C-6), 26.96 (d, C-5), 121.77 (d, C-4), 142.57(s, C- 3), 70.74 (d, C-2), 72.69 (t, C- 1). Spectroscopy data corresponds most with data in Vairappan and Tan (2005).

Aplysistatin (2)—white crystal; C₁₅H₂₃O₃Br; ¹H-NMR (CDCl₃, 600 MHz) $\delta$ (3H, s, H-15), 0.96 (3H, s, H-14), 1.17 1.29 (3H, s, H13), 3.92 (1H, dd, J = 4, 14 Hz, H-10), 2.11 (1H, m, H-9), 2.28 (1H, m, H-9), 1.61 (lH, ddd, J = 3, 3, 13 Hz, H-8$\alpha$), 1.79 (1H, ddd, J =3,13,13 Hz, H-8$\beta$), 2.04 (1H, m, H-6), 2.56 (1H, m, H-5), 6.96 (1H, brs, H-4), 5.13 (1H, brs, H-2), 3.87 (1H, dd, J = 8, 8 Hz, H-1$\alpha$), 4.49 (1H, dd, J = 8, 8 Hz, H-l$\beta$); 13C-NMR (CDCl3 150 MHz) $\delta$ 31.42 (q, C-15), 18.66 (q, C-14), 22.40 (q, C-13), 169.86 (s, C-12), 41.67 (s, C-11), 65.80 (d, C-10), 33.09 (d, C-9), 38.32 (t, C-8), 79.72 (s, C-7), 51.90 (d, C-6), 27.87 (d, C-5), 143.81 (d, C-4), 132.59 (s, C-3), 67.45 (d, C-2), 70.56 (d, C-1). Spectroscopy data corresponds most with data in Vairappan and Tan (2005).

5-Acetoxypalisadin B (3)—oil; C₁₇H₂₆O₃Br₂; ₁H-NMR (CDCl₃, 600 MHz) $\delta$ 1.22 (3H, s, H3-15), 1.03 (3H, s, H3-14), 1.65 (3H, s, H13), 1.61 (1H, m, H-8$\alpha$), 1.77 (3H, s, H3-12), 1.86 (1H, m, H-8$\beta$), 2.10 (3H, s, H3-16), 3.88 (1H, dd, J = 4, 13 Hz, H-10), 2.18 (1H, m, H-9a), 2.31 (1H, m, H-9$\beta$), 1.75 (1H, m, H-6), 5.80 (1H, d, J = 8 Hz, H-5), 5.71 (1H, d, J = 6 Hz, H-4), 4.45 (1H, d, J = 9 Hz, H-2), 3.43 (1H, dd, J = 8 Hz, 11 Hz, H-1$\alpha$), 3.72 (1H, dd, J = 3, 11 Hz, H-1$\beta$); 13C - NMR (CDCl3, 150 MHz) $\delta$ 171.10 (C-17), 22.22 (C-16), 31.58 (q, C-15), 19.45 (q, C-14), 25.99 (q, C-13), 21.91 (q, C-12), 42.09 (s, C-11), 66.80 (d, C-10), 33.51 (d, C-9), 40.09 (t, C-8), 78.62 (s, C-7), 54.56 (d, C-6), 70.43 (d, C-5), 127.72 (d, C-4), 143.25 (s, C-3), 70.87 (d, C-2), 35.50 (t, C-1). Spectroscopy data corresponds most with data in Vairappan and Tan (2005).

Spectrometry analysis

We further analysed the extract of L.snackeyi using mass spectrometry. A total of 37 metabolites; 30 from positive mode and 7 from negative mode were identified putatively via high resolution tandem mass spectrometry profiling (Table 1). These 37 metabolites mainly comprised of fatty acids (9), terpenoids (7), phenolics (4), aromatics (4), and cyclic ketones (3). Among the terpenoids, four steroids and one pentacyclic triterpenoid were detected. However, their putative identities were masked as the identification of these underivatized compounds via tandem mass spectrometry could be misleading. The main reason is that their multi-ring skeleton possess diverse stereoisomerisms and do not yield sufficient product ions species for detail identification (Murphy 2015). Figures 5 and 6 compiles the chemical structures for the LCMS detected metabolites.

Table 1.

Putatively identified metabolites from positive and negative mode of MS/MS analysis

Metabolites	Formula	Mass error (ppm)	m/z	Class	Bioactivity	References
Putatively identified metabolites from positive mode of MS/MS analysis
6-Aminocaproic acid (27)	C6H13NO2	– 0.05	132.1019	Amino acid	Antifibrinolytic	(Ferreira et al. 2021; Srivastava et al. 2019)
Carnitine (28)	C7H15NO3	– 0.93	162.1123	Amino acid	Fatty acid oxidation for energy conversion	(Ghoran et al. 2021)
2-Decenoic acid (29)	C10H18O2	– 0.64	171.1379	Fatty acid	Biofilm control	(Tripathi et al. 2019; Kabir et al. 2021)
Docosahexaenoic acid (30)	C22H32O2	– 1.05	329.2472	Fatty acid	Anti-inflammation; cell signalling; cell proliferation	(Hu et al. 2023; Abduljelil et al. 2022)
5,8,11,14-Eicosatetraynoic acid (31)	C20H24O2	– 1.14	297.1846	Fatty acid	Anti-inflammation; antipyretic	(Palaniveloo and Vairappan 2014)
2,7,12,17-Octadecanetetrol (32)	C18H38O4	– 1.68	319.2838	Fatty alcohol
10,12-Hexadecadienal (33)	C16H28O	– 1.07	237.2210	Fatty aldehyde
Erucamide (34)	C22H43NO	– 1.55	338.3412	Fatty amide	Acetylcholinesterase inhibitor; antidepressant; antimicrobial	(Vairappan et al. 2004; Kamada and Vairappan 2012; Masuda et al. 1997)
Decanophenone (35)	C16H24O	– 0.22	233.1899	Aromatic ketone
Heptanophenone (36)	C13H18O	– 0.27	191.1430	Aromatic ketone
Haplamine (37)	C15H15NO3	– 1.71	258.1120	Pyranoquinoline alkaloid	Cytotoxic; antifungal; antialgal	(Ellman et al. 1961; Aristyawan et al. 2022)
N-Benzylformamide (38)	C8H9NO	– 0.28	136.0757	Aromatic formamide
11-(2-Hexyl-5-hydroxyphenoxy)-N-(2-hydroxyethyl) undecanamide	C25H43NO4	– 1.25	422.3260	Phenolic
6-Gingerol (39)	C17H26O4	– 0.99	295.1901	Phenolic	Cytotoxic; analgesic; anti-oxidation; anti-inflammation	(Chang et al. 2023; Zhan et al. 2023; Maran et al. 2021)
Paradol (40)	C17H26O3	– 0.13	279.1954	Phenolic	Cytotoxic; anti-inflammation; neuroprotective; antidiabetic	(Hastings et al. 2016; Davies et al. 2015; Mendez et al. 2019; Sud et al. 2006)
Indole-4-carboxaldehyde (41)	C9H7NO	0.13	146.0601	Indole	Anti-inflammation	(Cheung et al. 2012)
15-Hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42)	C26H45NO4	– 0.57	436.3419	Cyclic ketone
8-[3-Oxo-2-(2-penten-1-yl)-1-cyclopenten-1-yl]octanoic acid (43)	C18H28O3	– 1.15	293.2108	Cyclic ketone
Jasmone (44)	C11H16O	0.49	165.1275	Cyclic ketone	Plant-defence activator; anti-inflammation	(Nachon et al. 2013; Khelfaoui et al. 2021)
Laurendecumenyne A (45)	C15H21BrO4	– 0.23	345.0695	Acetogenin		(Vairappan and Tan 2005)
Coronaridine (46)	C21H26N2O2	– 0.57	339.2065	Alkaloid	Antiparasite; cytotoxic	(Murphy 2015; Brown et al. 2018)
ar-Turmerone (47)	C15H20O	0.21	217.1587	Sesquiterpenoid	Antiplatelet; anti-angiogenesis; cytotoxic; anti-inflammation; anti-depression	(Coleman et al. 2021; Brass 2000; Jennings et al. 2012; Marques et al. 2015)
Retinaldehyde (48)	C20H28O	– 1.21	285.2210	Vitamin A	Visual-essential metabolite; antimicrobial; adipogenesis inhibitor	(Niemoller and Bazan 2010; Salem et al. 2001)
Abscisic acid (49)	C15H20O4	1.89	265.1428	Phytohormone		(Stuhlmeier et al. 1997)
Steroidal compound	C46H78N2O5	0.05	739.5984	Steroid
Steroidal compound	C19H28O3	– 0.91	305.2109	Steroid
Steroidal compound	C27H44O	– 1.50	385.3459	Steroid
Steroidal compound	C27H42	– 0.72	367.3357	Steroid
Pentacyclic compound	C48H82N2O5	– 0.41	767.6293	Triterpenoid
Putatively identified metabolites from negative mode of MS/MS analysis
15-oxo-11,13-Eicosadienoic acid (50)	C20H34O3	0.32	321.2436	Fatty acid
16-Hydroxypalmitic acid (51)	C16H32O3	0.02	271.2279	Fatty acid
2-Hydroxymyristic acid (52)	C14H28O3	– 0.58	243.1964	Fatty acid	Antiviral	(Li et al. 2017)
9,12,13-Trihydroxy-15-octadecenoic acid (53)	C18H34O5	0.99	329.2337	Fatty acid
Oleic Acid (54)	C18H34O2	– 1.76	281.2481	Fatty acid	Antidiabetic; anti-inflammation	(Kim et al. 2018; Xie et al. 2021)
Palmitic Acid (55)	C16H32O2	– 1.24	255.2326	Fatty acid	Antitumor; pro-inflammation; pro-apoptotic	(Cantrell et al. 2005; Ea et al. 2008; Dugasani et al. 2010)
3,5-Dibromo-4-hydroxybenzoic acid (56)	C7H4Br2O3	0.26	292.8455	Phenolic

Fig. 5.

Structures of L. snackeyi metabolites detected through MS analysis as reported in Table 1

Molecular docking studies

Molecular docking studies were conducted to investigate the cholinesterase inhibitory properties of L. snackeyi metabolites based on the inhibitory effects on the AChE and BChE proteins. For a metabolite to be regarded optimally docked, as compared to a reference, a recommended RMSD value and S-score of lower than 2Å and and −7 kcal/mol, respectively were considered. Table 1 lists the docking results for all metabolites that fulfilled the criteria along with a reference ligand. Molecular docking data for all 33 metabolites are provided in Supplementary Tables 1 and 2.

Protein-ligand interaction with AChE.

The molecular docking of the reference ligand, galantamine with AChE recorded the S-score of −−$7.47$ kcal/mol. The L. snackeyi metabolites recorded S-scores that ranged from −$4.60$ to −$8.72$ kcal/mol. A total of 12 candidates recorded S-score values of −$7$ kcal/mol and lower. Ten candidates, docosahexaenoic acid (30), 5,8,11,14-eicosatetraynoic Acid (31), 2,7,12,17-octadecanetetrol (32), erucamide (34), 6-gingerol (39), 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42), 8-[3-oxo-2-(2-penten-1-yl)-1-cyclopenten-1-yl]octanoic acid (43), 15-oxo-11,13-eicosadienoic acid (50), 9,12,13-trihydroxy-15-octadecenoic acid (53) and oleic acid (54) recorded strong binding with low S-scores ranging from -7.54 kcal/mol to −$8.72$ kcal/mol. However, taking into consideration of the RMSD value, 5,8,11,14-eicosatetraynoic acid (31) (−$8.72$ kcal/mol) had the lowest RMSD value of 1.53 Å among the ten metabolites, making it a potential inhibitor against the acetylcholinesterase enzyme.

The reference ligand, galantamine formed one conventional hydrogen bond, one carbon hydrogen bonds, and fifteen (15) van der Waals interactions with AChE, as recorded in Table 2. Figure 7 shows that the reference ligand formed hydrogen bond with amino acid residues at Glu202, with Glu202 being an amino acid residue from the binding site. The reference ligand also formed carbon hydrogen bond with Tyr337 and van der Waals interactions with Gly120, Gly121, Gly122, Tyr124, Ser125, Tyr133, Ser203, Ala204, Trp236, Phe295, Phe297, Tyr341, His447, Gly448 and Ile451. As compared to the reference ligand, 5,8,11,14-eicosatetraynoic acid (31) formed one conventional hydrogen bond, two carbon-hydron bonds, and eighteen (18) van der Waals interactions. The metabolite formed conventional hydrogen bonds with the amino acid residues at Tyr133 and carbon hydrogen bonds with Gly121 and Gly126 (Fig. 7). The metabolite also exhibited van der Waals interactions with Asp74, Asn87, Tyr119, Gly120, Gly122, Tyr124, Ser125, Ala127, Leu130, Glu202, Ser203, Als204, Trp286, Phe297, Phe338, Tyr341, His447 and Gly448. The protein-ligand interaction can be seen in Fig. 7.

Table 2.

Molecular docking results of Laurencia snackeyi metabolites with Acetylcholinesterase (AChE) and Butyrylcholinesterase (BChE) protein

Compound number	S-score (kcal/mol)	RMSD (Å)	H bond	Van der Waals	C–H bond	Amino acid residue (H bond)
Acetylcholinesterase (AChE)
Galantamine	$-$ 7.47	1.06	1	15	1	*Glu202
31	$-$ 8.72	1.53	1	18	2	Tyr133
42	$-$ 8.22	3.88	2	23	1	Ser293; Tyr341
34	$-$ 8.13	1.81	2	23	0	Trp286; Ser293
30	$-$ 7.97	1.79	0	19	0	–
50	$-$ 7.80	0.96	2	21	1	Val294
43	$-$ 7.80	2.08	2	21	1	*Trp86; Tyr341
53	$-$ 7.73	1.70	2	15	1	*Glu202; *Phe295
39	$-$ 7.73	1.92	1	19	2	*Glu202
32	$-$ 7.69	1.88	2	21	1	Tyr34; Tyr124
54	$-$ 7.54	1.59	1	21	0	Ser125
Butyrylcholinesterase (BChE)
Tacrine	$-$ 5.98	3.60	1	13	0	–
42	$-$ 8.69	1.78	0	20	1	–
30	$-$ 8.07	1.45	3	19	0	Gly116; Gly117; Ser198
34	$-$ 7.75	1.17	1	21	0	Gln71
31	$-$ 7.73	1.67	3	21	0	Gly117; Ser198; Ala199
53	$-$ 7.55	4.86	4	16	0	Gly116; Ser198; Ser198; *His438
50	$-$ 7.40	2.66	2	16	1	Gly115; Gly116
39	$-$ 7.34	1.25	1	16	2	Leu286
40	$-$ 7.25	1.92	1	20	1	*Glu197
43	$-$ 7.24	1.19	1	20	2	Leu286
54	$-$ 7.24	2.51	3	22	0	Gly116; Gly117; Ser198

^*Hydrogen bond formed with amino acid residues from binding site

Fig. 7.

The docking conformation of AChE with a galantamine; b 5,8,11,14-eicosatetraynoic acid (31)

Protein-ligand interaction with BChE

The molecular docking of the reference ligand, tacrine with BChE recorded an S-score of $-5.98$ kcal/mol. The L. snackeyi metabolites were docked with S-scores ranging from $-4.81$ to $-8.69$ kcal/mol and a total of fourteen (14) candidates recording S-score values of $-7$ kcal/mol and lower. From the total of 33 metabolites tested, docosahexaenoic acid (30), 5,8,11,14-eicosatetraynoic acid (31), 2,7,12,17-octadecanetetrol (32), 10,12-hexadecadienal (33), erucamide (34), decanophenone (35), 6-gingerol (39), paradol (40), 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42), 8-[3-oxo-2-(2-penten-1-yl)-1-cyclopenten-1-yl]octanoic acid (43), retinaldehyde (48), 15-oxo-11,13-eicosadienoic acid (50), 9,12,13-trihydroxy-15-octadecenoic acid (53) and oleic acid (54) showed strong binding with low S-score ranging from $-7.05$ to $-8.69$ kcal/mol. 15-Hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) exhibited the lowest S-score -8.69 kcal/mol and had a good RMSD value of 1.78 Å making it the best candidate to inhibit the butyrylcholinesterase enzyme.

The reference ligand, tacrine was found to form one conventional hydrogen bond and thirteen (13) van der Waals interactions with BChE, as shown in Table 1. Figure 8 shows that the reference ligand formed hydrogen bonds with amino acid residues from the binding site at His438. The reference ligand also formed van der Waals interactions at Asp70, Gly78, Ser79, Gly116, Gly117, Thr120, Tyr128, Ser198, Tyr332, Met437, Gly439, Tyr440, and Ile442. As compared to the reference ligand, 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) formed a carbon hydrogen bond and twenty (20) van der Waals interactions with BChE. 15-Hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) formed carbon hydrogen bonds at Gly116, while also displaying van der Waals interactions at Asp70, Ser79, Asn83, Gly115, Gly117, Gln119, Thr120, Tyr128, Glu197, Ser198, Pro285, Ser287, Ala328, Phe329, Tyr332, Phe398, Met437, Gly439, Tyr440, and Ile442. The protein-ligand interaction can be seen in Fig. 8.

Fig. 8.

The docking conformation of BChE with a tacrine; b 15-Hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42)

Discussion

Chemotaxonomic significance of L.snackeyi metabolites

Laurencia is well-known for their diverse halogenated metabolites, a characteristic unique to the genus (Kamada and Vairappan 2017). Since the discovery of L. snackeyi, a total of 26 sesquiterpenoids had been reported mostly from Malaysian and Okinawan waters (Table 3). The molecules are shown in Fig. 9. In the Malaysian waters, almost all L. snackeyi records were from locations in Sabah (North Borneo) and only one report was available from Peninsular Malaysia in 1997. A total of 11 metabolites have been reported from the Malaysian species that are palisadin A (1), aplysistatin (2), 5-acetoxypalisadin B (3), palisadin D (4), 15-hydroxypalisadin A (5), 5$\beta$-hydroxypalisadin B (6), 12-acetoxypalisadin B (7), 12-hydroxypalisadin B (8), palisadin B (9), 3,4-epoxypalisadin B (10) and 1,2-dehydro-3,4-epoxypalisadin B (11). Specifically, compound 1 and 2 was found consistently in all populations from Malaysia, Japan and Vietnam, while compound 3 has been reported in several populations. All these compounds are of the chemical skeleton synderane which has been suggested to be a chemotaxonomical marker to L.snackeyi (Palaniveloo and Vairappan 2014; Tan et al. 2011; Ishii et al. 2020; Wijesinghe et al. 2014).

Table 3.

Summary of sesquiterpenoids from L. snackeyi from the tropical and sub-tropical waters

Country	Location	Metabolites	References
Malaysia	Port Dickson, Straits of Malacca	1, 2, 3	This study
	Mantanani Island, Sulug Island and Dinawan Island, Kota Kinabalu	1, 2, 3, 7, 9, 16, 18, 25	(Lee et al. 2008)
	Carrington Reef, Bak-Bak Beach and Lankayan, Sabah	1, 2, 3, 6, 9, 17, 21	(Young et al. 2005)
	Sepanggar, Kota Kinabalu	1, 2, 3, 16	(Gaire et al. 2015)
	Sulug Island, Kota Kinabalu	6, 9, 16, 25	(Hattori et al. 2012)
	Bum Bum Island, Semporna	1, 3, 4, 9, 12, 13, 14, 15, 16, 18, 25	(Lee and Surh 1998)
	Pulau Besar, Melaka; Sipanggau Island, Semporna; Manukan Island, Kota Kinabalu	1, 2, 3	(Wei et al. 2017)
Japan	Okinawa	1, 2, 9, 16, 18, 19, 20, 22, 25, 26	(Cha et al. 2019)
	Kukada Island, Okinawa	5, 10, 11, 22, 23, 24	(de Moura Fé et al. 2022)
	Okinawa	1, 2, 8, 9, 12, 13, 18, 22	(Moraes et al. 2008)
Vietnam	Phu Quoc Island	1, 2	(Wei et al. 2017)

Among other non-synderane metabolites reported from L.snackeyi in literature were debromolaurinterol (12), $\alpha$-bromocuparene (13), snakeol (14), snakediol (15), palisol (16), 4-bromo-$\beta$-chamigren-8-one (17), 3,3-dimethyl-5-methylene-4-(3-methylpenta-2,4-dien-1-yl)cyclohex-1-ene (18), luzonensin (19), luzonensol acetate (20), chamigr-2,5(14)-dien-8-one (21), luzonensol (22), luzonenone (23), luzofuran (24), pacifigorgiol (25) and (3Z,6E)-1-bromo-3,7,11-trimethyl-3,6,10-dodecatrien-2-ol (26). The chemical structures of all L. snackeyi metabolites are shown in Fig. 9.

Metabolites from the genus Laurencia have also been associated to as part of diet and defense mechanism (Palaniveloo et al. 2020) of these opisthobranch molluscs. This is very much attributed to the bioactivity of the compounds. The unique chemotaxonomy of the genus Laurencia has made it possible to determine the herbivory among sea hare species such as Aplysia dactylomela and A. parvula. Previously Palaniveloo and Vairappan (2014) reported palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3) from the A. dactylomela collected off the waters of Sulug Island in Borneo and was traced back to its diet, L. snackeyi Palaniveloo and Vairappan (2014). Similar finding was also reported by Vairappan and Tan (2005) and Vairappan et al. (2007) where 12-acetoxypalisadin B (7), 12-hydroxypalisadin B (8) and palisadin B (9) were isolated. Palisadin A (1) has also been reported from A. parvula from Borneo Vairappan et al. (2009). The established chemotaxonomy for L. snackeyi has made it possible to confirm the identity of the red algae species in Port Dickson, as reported in this manuscript.

Diversity of metabolites in investigated L.snackeyi

The L.snackeyi extract from this study which was subjected to HPLC profiling coupled with a PDA and scanned from 190 to 800 nm wavelength range. Previous reports reported the isolation of compounds from L. snackeyi between 220 and 254 nm due to the degrees of unsaturation in the metabolites (Palaniveloo and Vairappan 2014). This led to the isolation of prominent peaks detected in the extracts between UV wavelengths of 190 and 254 nm. Isolated peaks were subjected to NMR spectroscopy and the proton and carbon data were compared to available literature and three major isolates were identified as Palisadin A [1], Aplysistatin [2] and 5-acetoxypalisadin B [3]. Due to low yield for further isolation, we were determined to identify other available metabolites in the extract through LCMS profiling.

To the best of our knowledge, there has been no prior report on the mass spectrometry analysis of L. snackeyi crude extract. All existing publications reports isolated compounds. Our analysis detected 37 metabolites; 30 from positive mode and 7 from negative mode that were identified putatively via high resolution tandem mass spectrometry profiling and comprised of fatty acids (9), terpenoids (7), phenolics (4), aromatics (4), and cyclic ketones (3). However, only 32 detection were successfully confirmed and did not include the isolated metabolites 1–3. This is probably due to limited databases related to marine metabolites. From our LCMS analysis, many of the detected metabolites was either cytotoxic or exhibit anti-inflammatory properties. Some of the notable detection from the LCMS data was the 6-aminocaproic acid (27) which is a derivative and analogue of lysine, which makes it an inhibitor for enzymes. 6-Aminocaproic acid (27) is reported as an antifibrinolytic agent that acts by inhibiting plasminogen activators which have fibrinolytic properties (Brown et al. 2018). Carnitine (28), an amino acid which contributes to the conversion fat through oxidation (Brass 2000) was also detected.

Fatty acids are an essential component of tissue and as many as twelve (12) fatty acids and its derivative (29–33, 50–55) were recorded. Fatty acids and its derivatives are known to exhibit a range of bioactivities with anti-inflammation being most common, followed by anti-viral, anti-diabetic and anti-tumor (Jennings et al. 2012; Marques et al. 2015; Niemoller and Bazan 2010; Salem et al. 2001; Stuhlmeier et al. 1997; Li et al. 2017; Kim et al. 2018; Xie et al. 2021; Harper et al. 1996; Obici et al. 2002; Palomer et al. 2018; Ghezzal et al. 2020; Lu et al. 2003; Zhu et al. 2021). Erucamide (34), classified as a slip agent that has anti-biofouling properties is not surprising to be part of the algal chemical list given the almost slimy surface of L.snackeyi that prevents fouling (Getachew et al. 2016). Kim et al. (2018) reported that the pre-treatment of mice with erucamide significantly prevented memory deficits suggesting its potential to aid in Alzheimer’s disease by modulation of cholinergic functions (Kim et al. 2018). Haplamine (37) is an alkaloid that had been reported to exhibit anti-fungal properties. The MS detection of 6-gingerol (39), paradol (40) and ar-turmerone (47) in the extract of L.snackeyi raised eyebrows since these metabolites are common constituents causing the pungent smell in gingers. Indole-4-carboxaldehyde (41) was previously reported from the seaweed Sargassum thunbergii as an anti-inflammatory agent in MGO-induced inflammation in HepG2 cells (Cha et al. 2019).

Jasmone (44) is a volatile organic found in flowers or leaves of several plant species and acts as an attractant for pollinators and as a chemical cue for host localization for insects. In some reports, jasmones play a role as deterrent to pests (Moraes et al. 2008). As such, it is possible in L.snackeyi, this volatile plays are role in defence against herbivory. The detection of the acetogenin laurendecumenyne A (45) was also an interesting finding. The only previous report on the acetogenin is from L.decumbens from China (2007) Ji et al. (2007). Acetogenins from algae are quite different from plant acetogenins. They are usually halogenated, and have an enyne or a bromoallene terminal group (Wanke et al. 2015). Acetogenins are proposed as chemotaxonomic markers for L.nangii (Suzuki and Vairappan 2005). Abscisic acid (49) is a phytohormone that regulates the oxidative stress state under desiccation in seaweed species (Gomez-Cadenas et al. 2015). A wide range of bioactivity is often associated to the presence of halogens and benzene rings. Bromophenols (BPs) are such compounds detected in the L. snackeyi extract. 3,5-Dibromo-4-hydroxybenzoic acid (56) is a BP with a benzene ring with two bromines and hydroxyl-substituents. The ecological function of BPs is not yet clear, but its role in chemical defense and deterrence is highly suspected (Liu et al. 2011).

In vitro cholinesterase inhibition

Plenty of attention has been given to macroalgae for the development of new drugs, nutraceuticals, and dietary supplements due to the characteristics of macroalgae as anti-inflammatory, antioxidant, anti-tumor, anti-diabetic, and antibacterial agents. There are also evidence that macroalgal-derived compounds are capable of improving neurodegenerative conditions. A review on the potential neuroprotective compounds from macroalgae reported that between 1999–2004, Ochrophyta (class Phaeophyceae) contributed 57 compounds of the total, followed by 28 from Rhodophyta and 14 compounds Chlorophyta. Alzheimer’s associated metabolites had been reported from the red algae Gloiopeltis furcata where phlorotannins were the most prominent chemical class of cholinesterase inhibitors, followed by sterol, terpene and fatty acids (Alghazwi et al. 2016). The compounds reported from red algae genus Laurencia has long been associated to various bioactivities. Being halogenated in nature increases the chances for the metabolites produced to exhibit bioactivity (Vairappan et al. 2013; Palaniveloo et al. 2020; Alghazwi et al. 2016). Compounds 1 and 2 are the main components that can be obtained from L.snackeyi and these compounds have been associated to a wide range of bioactivity such as anti-bacterial (Vairappan et al. 2009), anti-inflammatory (Vairappan et al. 2013), cytotoxicity (Avila and Angulo-Preckler 2020). So far, the charmigranes (-)-elatol, (-)-dendroidol and (-)-cartilagineol, sesquiterpenes isolated from the Brazilian L. dendroidea were the only Laurencia derived metabolites reported with 78.5%, 72.9%, and 61.3% AChE inhibiting potential, respectively. With reference to (-)-elatol, its activity is suspected to be resulted from the interaction between the bromine and chlorine with the benzene ring center of Trp86 and Trp286, as observed during molecular docking analysis (Gonçalves et al. 2020). Given the vast bioactive potential of L. snackeyi, there has been no prior investigation on the Alzheimer’s related potential of this red algae. In fact, to date, there are no approved Alzheimer’s disease drug from a marine source making it more exciting for in depth investigation.

Molecular docking of cholinesterase inhibitors

The development of computational techniques for drug discovery facilitates the quick virtual bioactivity screening of molecules. The docking aims to anticipate the affinity strength between receptor proteins and ligands (Pinzi and Rastelli 2019), therefore increasing the likelihood of locating a suitable drug candidates. This reduces the early costs associated with hit detection. The S-score and RMSD determine the interaction between ligand and receptor protein. The more negative the S-score, the stronger the interaction (Xu et al. 2018). However, an optimal condition would be a combination of RMSD value that is below 2Å with an S-score lower than −7 kcal/mol (Khelfaoui et al. 2021).

The acetylcholinesterase (AChE, PDB ID = 4EY6) and butyrylcholinesterase (BChE, PDB ID = 4BDS) enzyme were selected as these deactivate the neurotransmitter acetylcholine (ACh) that causes the promotion of Alzheimer’s disease characterized by cholinergic deficiency (Greig et al. 2005; Kamada and Vairappan 2017; Viayna et al. 2020). Initial molecular docking for isolated compounds palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3) revealed weak potential as a cholinesterase inhibitor therefore was not the source of bioactivity in the in vitro assay. We proceeded to conduct a thorough investigation on the LCMS detected metabolites. The computational molecular docking of both the AChE and BChE receptors identified 5,8,11,14-eicosatetraynoic acid (31) and 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42), respectively as the best cholinesterase inhibitors in the algae with optimal combination of S-score and RMSD values. There has been no prior information on the cyclic ketone 42. However, there has been studies done on the role of ketones in neurodegenerative disease. The low S-value of 15-Hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) could possibly be attributed to the presence of the piperidine ring which is also found present in the existing anti-Alzheimer’s drug, donepezil (Tripathi et al. 2019). On the other hand, eicosatetraynoic acid (31) is a polyunsaturated fatty acid (PUFA). Several studies have highlighted the beneficial effect of PUFAs against neurodegenerative diseases. These metabolites are primary components of the brain, thus associating it to membrane integrity and fluidity. As such PUFAs could act as a form of nutraceutical defence against brain related diseases. The red algae are generally important source of PUFAs and there has been existing report on the moderate effect on AChE inhibition (Barbosa et al. 2014). As previously mentioned, this research was restricted by low sample biomass which hindered in vitro and in vivo experiments. Which in silico experiments provides a prediction towards the potential of a metabolite, future investigations should include evaluating the actual potential of compounds to confirm its potential.

Conclusion

The discovery of Laurencia snackeyi as a new record in Terumbu Island shows that this genus is under documented in Malaysian waters creating a great opportunity for further investigation for ecology and drug discovery. The cholinesterase inhibitory activity of chemical constituents from the algal extract creates greater interest in the species as its potential source for Alzheimer’s disease. This study confirms the potential of L. snackeyi and its metabolites as cholinesterase inhibitors through in vitro and in silico approaches. Nevertheless, this study is limited by the yield of extract and compounds for it to be evaluated via in vivo experiments. This indeed warrants more investigation and continuous research on the potential of halogenated metabolites as neurodegenerative disease agents.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization, KP; methodology, KP, YYS, SS and HS; software, HS and JKT; validation, KP, SS, YYS, JKT and HS; formal analysis, KP, SS, YYS, JKT and HS; investigation, KP, SS, YYS, JKT and HS; resources, KP, SS OKH and HS; data curation, KP and SS; writing—original draft preparation, KP and OKH; writing—review and editing, KP, SS, OKH, SAR, PSM, MRI, SH, HSY; supervision, KP, SAR; project administration, KP; funding acquisition, SAR. All authors have read and agreed to the published version of the manuscript.

Availability of data and materials

Raw data of known compounds NMR can be provided upon request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest in the publication.

Ethics approval

No human participants and/or animals were used in this publication.

Financial interest

The authors declare they have no financial interest.

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No human participants were used in this publication.

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All authors have agreed for publication.

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