Use of an active tripeptide in the preparation of a medicament for improving alzheimer's disease

Abstract

The application discloses application of an active tripeptide in preparation of a medicine for improving Alzheimer's disease, and relates to the technical field of biological medicines. In view of the problems of single medicine target for improving Alzheimer's disease, and lack of multi-target short peptides with acetylcholinesterase inhibition and neuroprotective activity, the application provides a new medical use of the active tripeptide with an amino acid sequence of LLW. The active tripeptide has free radical scavenging capacity and acetylcholinesterase inhibition activity; network pharmacology and molecular docking show that the active tripeptide can be combined with core targets such as AKT1 and CASP3 to regulate cell survival and apoptosis signaling pathways; in-vitro cell and in-vivo nematode experiments show that the active tripeptide can improve the survival rate of A beta 25‑35 induced damaged HT22 nerve cells and delay the paralysis process of nematodes induced by A beta. The active tripeptide has small molecular weight and good safety, and can be used for preparing the medicine for improving Alzheimer's disease and a neuroprotective agent.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of an active tripeptide in the preparation of drugs to improve Alzheimer's disease. Background Technology

[0002] Alzheimer's disease (AD) is a neurodegenerative disease of the central nervous system, clinically manifested as memory loss, cognitive impairment, and personality changes. Its main pathological features include β-amyloid (Aβ) plaque deposition, neurofibrillary tangles caused by Tau protein hyperphosphorylation, loss of cholinergic neurons, and oxidative stress damage. Notably, mild memory and cognitive decline often accompanies normal aging, and its neuropathological basis is also closely related to abnormal Aβ protein aggregation, oxidative stress damage, and cholinergic dysfunction. Therefore, developing active ingredients with neuroprotective effects is of significant practical importance for alleviating Aβ-induced neurological damage, scavenging free radicals, maintaining cholinergic system function, delaying age-related cognitive decline, and improving the quality of life for middle-aged and elderly individuals.

[0003] Currently, drugs used clinically to treat Alzheimer's disease (AD) mainly include acetylcholinesterase inhibitors (such as donepezil and galantamine) and NMDA receptor antagonists (such as memantine). However, most existing AChE inhibitors are small molecule compounds, primarily improving cognitive symptoms in mild to moderate AD patients, with limited intervention on disease progression, and are accompanied by certain peripheral side effects (such as gastrointestinal reactions and elevated liver enzymes). NMDA receptor antagonists also have limited efficacy in moderate to severe patients. More importantly, AD is a complex disease involving multiple targets and mechanisms, including Aβ deposition, Tau phosphorylation, cholinergic neuron loss, and oxidative stress. Single-target drugs cannot simultaneously cover all of these pathological processes, posing a significant challenge to clinical translation. Therefore, there is an urgent need in this field to develop short peptide candidates with small molecular weights, access to the central nervous system, synergistic effects across multiple targets such as AChE inhibition and antioxidant / neuroprotective effects, and good safety profiles.

[0004] Square Sipunts ( Sipunculus nudus Sipunculus nudus is an important edible marine economic organism, characterized by high protein and low fat. Existing patent research on Sipunculus nudus bioactive peptides mainly focuses on anti-oxidation, promoting hair regeneration, treating osteoporosis, and metal chelation to supplement minerals. Although short peptides derived from marine organisms have attracted much attention in the field of neuroprotection due to their high bioactivity, low toxicity, and potential blood-brain barrier (BBB) ​​penetration potential, there are currently no reports on the application of Sipunculus nudus tripeptide LLW in improving Alzheimer's disease. Summary of the Invention

[0005] The purpose of this invention is to provide an active tripeptide for the preparation of drugs to improve Alzheimer's disease, thereby addressing the problems existing in the prior art. This active tripeptide has the functions of inhibiting acetylcholinesterase activity, scavenging free radicals, and alleviating oxidative stress, and can exert a multi-target synergistic effect in improving Alzheimer's disease by targeting cholinergic defects and oxidative stress.

[0006] To achieve the above objectives, the present invention provides the following solution: This invention provides the application of an active tripeptide in the preparation of drugs to improve Alzheimer's disease, wherein the amino acid sequence of the active tripeptide is LLW.

[0007] The present invention also provides the application of an active tripeptide in the preparation of a neuroprotective drug, wherein the amino acid sequence of the active tripeptide is LLW.

[0008] The present invention also provides the application of an active tripeptide in the preparation of a drug for relieving oxidative stress, wherein the amino acid sequence of the active tripeptide is LLW.

[0009] Furthermore, the drug includes pharmaceutically acceptable excipients.

[0010] Furthermore, the excipients include excipients, diluents, fillers, binders, disintegrants, emulsifiers, flavoring agents, preservatives, or colorants.

[0011] Furthermore, the dosage form of the drug is an oral preparation.

[0012] Furthermore, the oral preparation is a powder, tablet, granule, suspension, capsule, pill, or oral liquid.

[0013] The present invention also provides the application of an active tripeptide in the preparation of an acetylcholinesterase inhibitor, wherein the amino acid sequence of the active tripeptide is LLW.

[0014] The present invention discloses the following technical effects: (1) Synergistic effect of multiple targets: The present invention found that LLW has both AChE inhibitory activity and free radical scavenging ability (DPPH, ABTS method), and can exert a synergistic effect on multiple pathological factors such as cholinergic deficiency and oxidative stress in AD.

[0015] (2) Small molecular weight and central penetration potential: The molecular weight of LLW is only 430.55 Da. According to deep learning prediction, it has the potential to penetrate the blood-brain barrier (BBB), which is conducive to reaching the central nervous system to play a role.

[0016] (3) Definite in vitro and in vivo neuroprotective activity: LLW can increase Aβ 25-35Survival rate of HT22 neurons induced by damage; delaying the Aβ-induced paralysis process in the transgenic AD model nematode CL4176.

[0017] Furthermore, this invention elucidates the mechanism by which LLW improves Alzheimer's disease (AD) on two levels: First, at the enzymatic level, in vitro enzyme activity experiments revealed that LLW exhibits inhibitory activity against acetylcholinesterase (AChE), and molecular docking and molecular dynamics simulations structurally elucidated its binding mode with the AChE catalytic pocket. Second, at the pathway level, network pharmacology predictions and molecular docking suggest that LLW may regulate cell survival and apoptosis signaling pathways through interactions with potential core targets such as AKT1 and CASP3. These two levels complement each other, jointly supporting the claim that LLW improves AD through multi-target synergistic action.

[0018] (4) The mechanism has a network pharmacology basis: Network pharmacology prediction and molecular docking results show that LLW has good binding affinity to potential core targets AKT1 (binding energy -8.503 kcal / mol) and CASP3 (binding energy -7.645 kcal / mol), suggesting that it may exert neuroprotective effects by regulating the AKT / Caspase apoptosis signaling axis.

[0019] (5) Good safety profile: LLW is derived from the edible protein of Sipunculus nudus, and deep learning prediction shows that it is non-toxic and non-allergenic; cell experiments show that it has no significant toxicity to HT22 cells in the concentration range of 62.5~1000 μmol / L. LLW has good safety profile and can be used to prepare drugs to improve Alzheimer's disease and neuroprotective agents. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a global spectral analysis and safety assessment diagram of the non-redundant peptide group of Sipuncula lataniae; where A is the peptide length distribution diagram; B is the molecular weight distribution diagram; C is the overall amino acid composition; D is the N-terminal amino acid preference; E is the C-terminal amino acid preference; F is the position-specific amino acid distribution heatmap; G is the Venn diagram of the intersection of toxic and sensitizing peptides; H is the filtration process from the total peptide library to the safe peptide library. Figure 2This is a multi-target bioactivity analysis diagram based on distribution morphology threshold optimization; where A is the probability density distribution of the five bioactivities of the safe peptide; B is the positive rate at different threshold levels; C is the threshold decay curve; D is the UpSet diagram of the intersection of the five bioactivities; and E is the Pearson correlation matrix of the predicted probability. Figure 3 This is a comparison and structure-activity relationship analysis diagram among different bioactive categories; where A is peptide length distribution; B is molecular weight distribution; C is a heatmap of differential amino acid frequencies; D is a comparison of N-terminal preference; and E is a comparison of C-terminal preference. Figure 4 This is a screening diagram of AD candidate peptides based on a safe peptide library; Figure 5 The HPLC chromatogram of LLW, a candidate peptide from Sipuncula lataniae; Figure 6 MS spectrum of LLW, a candidate peptide from Sipuncula latani; Figure 7 The graph shows the detection results of the DPPH and ABTS free radical scavenging rates of the candidate peptides from Sipuncula lataniae. Among them, A is a comparison of the DPPH free radical scavenging rates of the three active peptides and the positive control vitamin C at a concentration of 200 μM; B is a statistical graph of the DPPH free radical scavenging rates under different concentrations of LLW; C is a statistical graph of the ABTS free radical scavenging rates under different concentrations of LLW. Figure 8 The graph shows the results of detecting the inhibitory activity of Sipunculus nudus candidate peptides against acetylcholinesterase (AChE); where A is a comparison of the inhibition rates of the three active peptides at a concentration of 200 μmol / L and the positive control Huperzine A at a concentration of 10 μmol / L against AChE; B is a statistical graph of the inhibition rates of different concentrations of LLW against AChE. Figure 9 A 3D simulation diagram (A) and a 2D diagram (B) showing the docking of the square-patterned sipuncula peptide LLW with AChE molecules to the protein surface (A). Figure 10 The diagram shows the molecular dynamics simulation of the lattice-shaped sipunculus peptide LLW and AChE; where A is the root mean square deviation (RMSD); B is the root mean square fluctuation (RMSF); C is the radius of gyration; and D is the number of hydrogen bonds. Figure 11 Radar chart for evaluating the drug-likeness of square-grid Sipunculus peptide LLW; Figure 12 For LLW to Aβ 25-35 The results of the detection of the protective effect of LLW against induced HT22 cell damage are shown in the figure; where A represents the cytotoxicity analysis of LLW against HT22 cells; B represents the effect of LLW on Aβ. 25-35 Protection against induced HT22 cell damage; Figure 13 The curve of LLW peptide delaying Aβ-induced paralysis in nematodes; Figure 14 Biological prediction diagrams of LLW and AD targets are shown; where A is a pie chart of the target distribution of LLW and AD; and B is a Venn diagram of the intersection targets of LLW and AD. Figure 15 PPI network diagram of potential AD targets for LLW; Figure 16 PPI network core node selection diagram for improving AD intersection target points in LLW; Figure 17 A graph showing the GO enrichment analysis between the square-grid Sipunculus peptide LLW and AD; Figure 18 A graph showing the KEGG pathway enrichment analysis between the fusiform sipunculid peptide LLW and AD; Figure 19 The diagram shows the molecular docking results of the square-grid sipuncula peptide LLW with the core target AKT1; where A is a 3D protein surface binding simulation diagram; and B is a 2D ligand-protein interaction diagram. Figure 20 The diagram shows the molecular docking results of the square-grid sipuncula peptide LLW with the core target CASP3; where A is a 3D simulation of protein surface binding; and B is a 2D ligand-protein interaction diagram. Detailed Implementation

[0022] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0023] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included within the scope of this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0024] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0025] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0026] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0027] Example 1: Construction of a Sipuncula pylori peptide library and screening and synthesis of candidate peptide LLW 1. Experimental Materials The Sipuntia used in this embodiment were collected from the Zhanjiang Seafood Market. After morphological identification by Dr. Lao Zan, the former curator of the Aquatic Biology Museum of Guangdong Ocean University, they were transported to the laboratory on ice.

[0028] The proteases used in this embodiment include alkaline protease, neutral protease, flavorzyme, and trypsin, and the organic solvent is acetonitrile. The main instruments and equipment include a pH meter, rotary evaporator, high-speed centrifuge, water bath, electric meat grinder, freeze dryer, liquid chromatography-mass spectrometry, and vacuum centrifuge concentrator.

[0029] 2. Experimental Methods 2.1 Extraction of polypeptides from Sipuncula lataniae Sample pretreatment: To maximize the sequence diversity of the obtained peptides, 22 enzymatic hydrolysis conditions were designed using four food-grade proteases: alkaline protease, neutral protease, trypsin, and flavorzyme, including single-enzyme, dual-enzyme, and triple-enzyme combinations. The Sipuncula samples were washed, chopped, and homogenized with distilled water at a 1:3 (w / v) ratio. Enzyme hydrolysis was performed at pH 7.0-8.0 and an enzyme-substrate ratio of 1% or 2% (w / w). After incubation at 50℃ for 3 h, the enzyme was inactivated at 100℃ for 10 min. The hydrolysate was cooled and centrifuged at 8000 rpm for 15 min. The supernatant was concentrated by rotary evaporation at 45℃ and then lyophilized for downstream analysis.

[0030] 2.2 High-resolution LC-MS / MS peptide identification Lyophilized peptide powder was dissolved in ultrapure water, and low molecular weight peptides were enriched by 10 kDa molecular weight cutoff ultrafiltration. The peptides were then subjected to DTT reduction, IAM alkylation, C18 desalting, and vacuum drying. Separation and tandem mass spectrometry analysis were performed using Easy-nLC 1200 nanoliter liquid chromatography coupled with an OrbitrapExploris 240 mass spectrometer (nanoLC-MS / MS). Gradient elution was performed on a C18 analytical column, and positive ion mode was used to collect and select high-abundance precursor ions for HCD fragmentation.

[0031] 2.3 De novo sequencing and data filtering Raw data were processed using PEAKS Studio 8.5 software. Unannotated novel peptides were identified by comparing with the UniProt Sipunculidae database and combining de novo sequencing. The quality control criteria were: de novo score ≥90%, -10lgP ≥20, and peptide length ≥2 amino acids.

[0032] 2.4 Security Assessment Prior to bioactivity prediction, a safety-first screening protocol was implemented. All identified unique sequences were assessed for potential toxicity and sensitization using specific modules within the UniDL4BioPep framework. Peptides predicted to be toxic (probability > 0.5) or sensitizing were strictly excluded from the functional library to ensure the translational feasibility and safety of candidate peptides in subsequent drug development.

[0033] 2.5 Computer-based prediction of bioactivity using deep learning models The UniDL4BioPep general deep learning prediction framework (integrating the pre-trained protein language model ESM-2 and a convolutional neural network classifier) ​​is used to output the predicted probability (0-1) for each peptide's five biological activities, namely, hypotensive, antibacterial, antioxidant, blood-brain barrier (BBB) ​​penetration, and cell membrane-penetrating peptide (CPP), through transfer learning.

[0034] 2.6 Threshold Optimization Based on the distribution pattern of the predicted activity probabilities of the safe peptide population (n=5463), matching thresholds were set for different distribution types (e.g., high confidence threshold for bimodal distribution, 90th percentile for hilly broad-peak distribution, and standard threshold for skewed distribution) to replace the uniform threshold and improve the specificity of candidate peptide screening.

[0035] 3. Experimental Results 3.1 Construction and Safety Assessment of Non-Redundant Peptides from Sipuncula lataniae To maximize the sequence diversity of the Sipuncula peptidomide peptidomide genome, 22 different enzymatic digestion conditions were systematically employed. A non-redundant library containing 10,827 unique peptides was constructed using high-resolution LC-MS / MS acquisition and de novo sequencing. Figure 1 The sequence length of the peptide groups is mainly concentrated in the range of 4-10 amino acids (median: 6.0; mean: 7.0 aa), and the molecular weight is concentrated in the range of 500-1000 Da (median: 674 Da; mean: 762 Da; see...). Figure 1 (AB). Amino acid composition analysis ( Figure 1 The N-terminal region (C) shows an abundance of hydrophobic residues, particularly proline (P) and leucine (L). Position-specific preference analysis indicates that leucine dominates the N-terminus (C). Figure 1 D and F), while proline mainly occupies the C-terminus ( Figure 1 (E and F in the middle).

[0036] Safety assessments using UniDL4BioPep identified 1,910 peptides as potentially toxic and 3,763 as potentially sensitizing, with 309 peptides exhibiting both hazards simultaneously. Figure 1 Structural analysis of potentially toxic and sensitizing sequences revealed that potentially toxic peptides exhibited significant enrichment of hydrophobic residues at specific positions. Compared to the global peptide set, sensitizing peptides had larger sizes (average length: 8.6 residues; average molecular weight: 928 Da) and were significantly enriched for glutamic acid (E). After systematically excluding these sequences, a safe peptide library of 5463 peptides was established. Figure 1 (H in the middle) is used for subsequent analysis.

[0037] 3.2 Artificial Intelligence-Driven Prediction of Bioactivity The predicted probability distribution of 5463 safe peptides showed significant morphological differences across five biological activities. Figure 2 (A) The predictions for blood pressure reduction and antibacterial activity showed a distinct bimodal distribution, with the main clusters concentrated at the 0.0 and 1.0 ends. The probabilities of antioxidant and blood-brain barrier (BBB) ​​crossing peptides showed a wider hilly distribution. The probability distribution of cell-penetrating peptides (CPPs) showed a significant right skewness.

[0038] Based on these different distribution patterns, three customized threshold strategies were implemented. Figure 2 (B and C). For the bimodal distribution (Type I), a strict 0.90 threshold was used for both antihypertensive and antibacterial predictions. For the hilly distribution (Type II), the 90th percentile of the safe peptide population was used to obtain an antioxidant threshold ≥0.89 and a BBB activity threshold ≥0.80. The right-skewed CPP distribution (Type III) retained a baseline threshold of 0.50. After this calibration, the candidate peptide population was limited to: antihypertensive 1422 (26.0%); CPP 697 (12.8%); antibacterial 653 (12.0%); antioxidant 572 (10.5%); BBB 529 (9.7%). UpSet intersection plot ( Figure 2The results (D) show that while single-target antihypertensive peptides constitute the largest independent subset, there is significant overlap between bifunctional and multifunctional candidate peptides. Pearson correlation analysis of predicted probabilities (…) Figure 2 The results showed a moderate positive correlation between blood pressure reduction and antioxidant activity (r = 0.48), and also a moderate positive correlation between blood pressure reduction and CPP (r = 0.41).

[0039] 3.3 Comparison and structure-activity relationship analysis among different bioactive categories Chain length and molecular weight distribution ( Figure 3 Figures A and B reveal different dimensional characteristics among the five bioactive categories. Antihypertensive and antioxidant peptides exhibit a broader distribution, with a median concentration of 6.0 residues and approximately 650–750 Da. Conversely, antimicrobial peptides, BBB-penetrating peptides, and CPP peptides tend towards a compact molecular conformation.

[0040] Global amino acid frequency heatmap Figure 3 (C) indicates that each activity class has unique sequence characteristics. The antihypertensive group is characterized by extensive enrichment driven by proline (P) and leucine (L). The antibacterial group is dominated by leucine (L) and glycine (G), and characterized by a high frequency of lysine (K). Antioxidant peptides show specific enrichment of proline (P) and glycine (G), while tyrosine (Y), aspartic acid (D), and histidine (H) levels are elevated. Both the BBB-penetrating peptide and the CPP candidate peptide sequences show accumulation of proline (P) and leucine (L).

[0041] Terminal residue analysis ( Figure 3 (D and E) showed that the N-terminus of all five bioactivities was consistently dominated by leucine (L). However, the C-terminal landscape exhibited significant class-specific differences. Proline (P) was the major terminal blocking residue for the antihypertensive peptide, CPP, antioxidant peptide, and BBB-penetrating peptide. In addition to proline, the antioxidant peptide showed significant secondary C-terminal enrichment of tyrosine (Y) and histidine (H). The C-terminus of the antimicrobial peptide was primarily anchored by leucine (L) and lysine (K). Furthermore, the BBB-penetrating peptide showed a secondary C-terminal preference for leucine (L) and histidine (H), while the CPP showed specific enrichment of arginine (R).

[0042] 3.4 Screening of candidate peptides for improving Alzheimer's disease To screen for candidate peptides that improve Alzheimer's disease (AD), this invention performed multiple filtering on 5463 safe peptides: first, screening was based on antioxidant activity, blood-brain barrier (BBB) ​​and cell penetration (CPP); then, short sequences (≤7 aa), aromatic amino acids, and structural novelty were selected, ultimately narrowing down to 3 candidate peptides (3-5 aa) Figure 4To investigate the effect of aromatic residue types on neuroprotective efficacy, candidate peptides were divided into two groups: the first group contained tryptophan (amino acid sequence LLW), whose indole ring was expected to provide π-π electron density that would facilitate π-π stacking and hydrophobic interactions with the target site; the second group contained phenylalanine / tyrosine (amino acid sequences PDPF, FKVFY), serving as a structural control group. The activity of both groups was verified by subsequent experiments.

[0043] Example 2: In vitro antioxidant activity and AChE inhibitory activity of tripeptide LLW 1. Solid-phase synthesis and purity identification of candidate peptides Candidate peptides LLW, PDPF, and FKVFY were all prepared using the Fmoc solid-phase peptide synthesis method. HPLC and mass spectrometry analysis showed that the purity of all three Sipuncula candidate peptides was greater than 95%, meeting the requirements for subsequent experiments. Among them, candidate peptide LLW had an average relative molecular mass of 430.55 Da, and its main peak was [M+H]. + m / z is 431.2 ( Figures 5-6 The molecular weights of the other two candidate peptides, PDPF and FKVFY, are 474.51 Da and 702.84 Da, respectively.

[0044] 2. Methods for determining the in vitro antioxidant capacity and in vitro AChE inhibitory activity of Sipunculus nudus candidate peptides. 2.1 Experimental Materials The reagents used in this embodiment, including DPPH, ABTS, potassium persulfate, acetylcholinesterase (AChE), acetylthiocholine iodide (ATCh), DTNB, and BSA, were all purchased from commercial sources, and the solutions were prepared according to conventional methods.

[0045] 2.2 Experimental Methods (1) DPPH free radical scavenging ability test The method for detecting the scavenging activity of 2,2-biphenyl-1-picrylhydrazyl (DPPH) free radicals is as follows: A 0.16 mmol / L DPPH methanol solution was prepared and homogenized by vortexing until completely dissolved. The *Sipunculus nudus* peptide sample was dissolved in ultrapure water to different concentrations. The experiment was divided into four groups: Experimental group (A1): 50 μL of *Sipunculus nudus* peptide solution was added to a 96-well plate, followed by 50 μL of DPPH solution; Experimental control group (A2): 50 μL of DPPH solution was replaced with methanol, otherwise the same as the experimental group; Blank group (A3): 50 μL of ultrapure water / PBS + DPPH solution; Blank control group (A4): 50 μL of ultrapure water / PBS + methanol. The 96-well plates were placed in the dark and reacted at room temperature for 30 min, and the absorbance was measured at 517 nm. Ascorbic acid was used as a control. All samples were repeated three times for analysis. The DPPH free radical scavenging rate was calculated according to the following formula: Clearance rate (%) = [(A3-A4)-(A1-A2)] / (A3-A4)×100%.

[0046] (2) ABTS free radical scavenging ability test The antioxidant activity of *Sipunculus nudus* active peptides was determined using the 2,2'-adiazon-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) method. The specific method is as follows: ABTS solution preparation: 7.4 mmol / L ABTS and 2.6 mmol / L potassium persulfate solution were mixed in equal volumes and incubated at room temperature in the dark for 16 h. Before testing, the ABTS solution was diluted with ultrapure water to an absorbance of 0.70 ± 0.02 to obtain the ABTS free radical working solution. *Sipunculus nudus* peptide samples were dissolved in ultrapure water to different concentrations. A 96-well plate method was used: 25 μL of *Sipunculus nudus* peptide solutions of different concentrations were added to 75 μL of the ABTS free radical working solution, mixed thoroughly, and reacted at room temperature in the dark for 6 min. The absorbance was then measured at 734 nm. Vitamin C was used as a control. All samples were tested and analyzed in triplicate. The ABTS free radical scavenging rate was calculated using the following formula: Clearance rate (%) = [1-(A1-A2) / A0]×100%; Where A1 is the absorbance of the experimental group, A0 is the absorbance of an equal volume of ultrapure water replacing the sample, and A2 is the absorbance of an equal volume of PBS replacing the ABTS working solution.

[0047] (3) Acetylcholinesterase (AChE) activity inhibition test: The modified Ellman's colorimetric method was used to determine the inhibitory activity of the samples against AChE in a 96-well plate. The principle is that thioacetylcholine (ATCh) is converted to thiocholine under the catalysis of AChE. Thiocholine reacts with the colorimetric reagent DTNB to generate a yellow product (TNB). The absorbance changes at 405 nm. The AChE inhibition rate is calculated by substituting the results into the formula.

[0048] Samples containing LLW, PDPF, and FKVFY from *Sipunculus nudus* were prepared at different concentrations using phosphate buffer. A 96-well plate assay was used: 10 μL of sample solution (or phosphate buffer), 40 μL of phosphate buffer, 10 μL of 0.2 U / mL AChE solution (or BSA solution), and 20 μL of 5 mmol / L DTNB solution were mixed thoroughly and reacted at 37°C for 10 min. Then, 20 μL of 10 mmol / L ATCh solution was added and reacted at 37°C for another 20 min. The absorbance was then measured at 405 nm. Huperzine A was used as a positive control. All samples were analyzed in triplicate. The inhibition rate was calculated using the following formula: Inhibition rate (%) = [(A3-A4)-(A1-A2)] / (A3-A4)×100%; Where A1 is the absorbance of the experimental group, A2 is the absorbance without acetylcholinesterase, A3 is the absorbance of an equal volume of PBS replacing the sample, and A4 is the absorbance without the sample and without acetylcholinesterase.

[0049] 3. Experimental Results 3.1 LLW possesses good in vitro antioxidant potential. The antioxidant activity of *Sipunculus nudus* peptides was detected by DPPH and ABTS methods. At a concentration of 200 μmol / L, there were significant differences in the scavenging activity of three active peptides (LLW, PDPF, and FKVFY) and the positive control vitamin C against DPPH free radicals. Figure 7 (A). Among them, the vitamin C clearance rate reached approximately 92%, verifying the reliability of the detection system; among the candidate peptides, LLW exhibited the strongest DPPH clearance activity, with a clearance rate of 43.12%, significantly higher than PDPF (12.52%) and FKVFY (15.52%) (p<0.05); there was no statistically significant difference between FKVFY and PDPF.

[0050] The scavenging rate of LDPH free radicals by LLW increased in a concentration-dependent manner. Figure 7 (Medium B). At 1 μmol / L, the clearance rate was only 3.45%, gradually increasing with increasing concentration, reaching 43.12% at 200 μmol / L and 48% at 500 μmol / L. Further increasing the concentration to 1000 μmol / L, the clearance rate rose to 60.89%. Further increases to 2000 μmol / L and 3000 μmol / L resulted in clearance rates of approximately 72.84% and 74.53%, respectively, with the growth rate slowing significantly and plateauing.

[0051] The scavenging rate of ABTS radicals by LLW also increased in a concentration-dependent manner. Figure 7 (C). At 1 μmol / L, the scavenging rate was 17.00%, higher than that for DPPH at the same concentration, indicating that LLW has a stronger scavenging effect on ABTS radicals than on DPPH radicals. The scavenging rate continued to increase with increasing concentration, reaching 50.05% at 200 μmol / L and 60.78% at 1000 μmol / L. Further increases to 2000 μmol / L and 3000 μmol / L showed a slower rate of increase.

[0052] 3.2 Analysis of the inhibitory effect of Sipunculus nudus peptide on AChE enzyme activity The inhibitory activity of three active peptides (LLW, PDPF, and FKVFY) at 200 μmol / L and the positive control huperzine A at 10 μmol / L on acetylcholinesterase (AChE) was detected. Significant differences in inhibition rates were observed among the groups. Figure 8 (A). Among them, the positive control huperzine A showed an inhibition rate of approximately 93% at a concentration of 10 μmol / L, verifying the reliability of the detection system. LLW exhibited the strongest AChE inhibitory activity among the three active peptides, with an inhibition rate of 36%, significantly higher than the other two peptides. FKVFY showed the second highest inhibition rate at 8.24%, while PDPF showed relatively low inhibitory activity. These results indicate that, at the same concentration, LLW's inhibitory ability against AChE is significantly better than that of PDPF and FKVFY, suggesting that LLW has greater application potential in anti-AChE activity.

[0053] Given the superior AChE inhibitory potential of LLW, further concentration gradient experiments were conducted. The results showed that the inhibitory activity of LLW against AChE was significantly dose-dependent (…). Figure 8 (B)

[0054] The above results indicate that the LLW peptide from the square-grid sipunculus larvae exhibits good antioxidant and anti-AChE properties. Therefore, this invention selected LLW for further mechanism and in vivo activity verification.

[0055] Example 3: Molecular docking and molecular dynamics simulation of tripeptide LLW with AChE 1. Experimental Methods 1.1 Molecular docking parameter settings for LLW and AChE This embodiment investigates the binding of LLW peptide to acetylcholinesterase (AChE). First, the amino acid sequence of the square-patterned sipunculus peptide was converted to SMILES format using RDKit, generating its 3D structure. Subsequently, the generated structure was energy-minimized using OpenBabel 3.1.1 and converted to mol2 file format. In AutoDockTools 1.5.6, hydrogen was added to the ligand molecule, Gasteiger charges were assigned, and the number of torsions of the rotatable bonds was set, with the final output in pdbqt format. Simultaneously, the 3D crystal structure of human AChE (PDBID: 4EY7) was obtained from the Protein Database (PDB). Protein receptor pretreatment included: removing water molecules and the original co-crystallized ligand from the structure, adding polar hydrogen atoms, assigning Kollman charges using AutoDockTools v1.5.6, and setting the grid box centered on the catalytically active site of AChE to cover the binding pocket. Docking simulations were performed using AutoDock Vina v1.2.0 software. Finally, visualization analysis was performed using PyMol v2.5.10 and Discovery Studio 2019.

[0056] 1.2 All-atom molecular dynamics simulation of the LLW-AChE complex To assess the stability of the protein-ligand complex after docking, 100 ns molecular dynamics simulations were performed using Gromacs 2022 software. The AMBER14SB force field was used to describe the system, the TIP3P model was used to define the aqueous solvent environment, and appropriate amounts of sodium or chloride ions were added to maintain the system's electroneutrality. Before the formal simulation, the system was equilibrated under canonical ensemble (NVT) and isothermal-isobaric ensemble (NPT) conditions to reach the set temperature and pressure, followed by the 100 ns molecular dynamics simulation. After the simulation, parameters such as root mean square deviation, root mean square fluctuation, radius of rotation, and number of intermolecular hydrogen bonds were calculated to evaluate the stability and kinetic behavior of the protein-ligand complex.

[0057] 2. Experimental Results 2.1 Molecular docking reveals the binding mode between LLW and the catalytic active site of AChE. The binding interaction between the candidate tripeptide LLW and acetylcholinesterase (AChE) was evaluated using molecular docking. The results showed that LLW and AChE have a low docking energy (-11.17 kcal / mol), and the molecular conformation of LLW is located deep within the catalytic cleft of AChE, suggesting that it may affect substrate entry into the active pocket. Figure 9 (A). Further two-dimensional interaction analysis ( Figure 9As shown in Figure B), LLW forms various non-covalent interactions with the residues surrounding the active pocket of AChE: LLW forms hydrogen bonds with TYR121 and TYR332; its tryptophan (Trp) indole ring forms π-π stacking interactions with TRP83; the LLW side chain forms hydrophobic interactions with residues such as HIS438, PHE288, PHE329, and TRP277; and it also forms van der Waals interactions with surrounding residues such as PHE286, SER200, GLY119, GLY118, and ARG287.

[0058] The above results provide molecular simulation evidence for the possible binding modes by which LLW inhibits AChE activity.

[0059] 2.2 Molecular dynamics simulations support the dynamic stability of the LLW-AChE complex. To further investigate the stability of the protein-ligand complex after docking, molecular dynamics simulations of the LLW-AChE complex were performed using Gromacs software for 100 ns. During the first 50 ns of the simulation, the RMSD value gradually increased, indicating that the system was in a structural adjustment phase. Between 50 and 100 ns, the RMSD tended to stabilize and fluctuated around 0.25-0.30 nm, indicating that the LLW-AChE complex reached a dynamic equilibrium state in the later stages of the simulation. Figure 10 (A) Figure 10 Figure B shows the results of RMSF assessment of the flexibility of individual amino acid residues. Except for the two ends and some surface loop regions, the RMSF values ​​of most AChE residues remained below 0.2 nm, suggesting that LLW binding did not cause significant perturbation to the overall structure of AChE. Figure 10 The C-value shows that the radius of gyration (Rg) of the complex remained in the range of 2.26-2.30 nm during the 100 ns simulation, without a significant upward trend, indicating that the overall conformation of the system is relatively compact. Figure 10 The simulation results show that LLW and AChE maintain 1-3 hydrogen bond interactions within the first 30 ns, and the number of hydrogen bonds decreases and appears intermittently after 30 ns. Combined with the hydrophobic interactions observed in the docking results, it is speculated that hydrogen bonds and hydrophobic interactions jointly participate in maintaining the binding conformation of LLW in the catalytic pocket.

[0060] 2.3 Drug-likeness evaluation of LLW (a type of peptide from the sipunculus nudus) To preliminarily assess its drug similarity and ADMET characteristics, this invention performed calculations and predictions on the square-shaped sipunculid peptide LLW. For example... Figure 11 As shown, some physicochemical parameters of LLW fall within the range of commonly used empirical rules and conform to multiple empirical criteria such as the Lipinski rule and the Pfizer rule. These results support the potential of LLW for further drug development.

[0061] Example 4: Tripeptide LLW on Aβ 25-35 Protective effect against HT22 cell damage 1. Experimental Materials The immortalized HT22 mouse neurons were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China).

[0062] Main reagents: DMEM high glucose medium (Gibco), trypsin-EDTA 0.25% (Gibco), fetal bovine serum FBS (Zeta Life), MTT (Yuanye Biotechnology).

[0063] Main instruments: clean bench, CO2 cell culture incubator, inverted microscope, cell counter, centrifuge, shaker.

[0064] 2. Experimental Methods 2.1 Aβ 25-35 Establishment and drug intervention of an HT22 cell injury model MTT assay for cell viability: HT22 cells in logarithmic growth phase were analyzed using 100 μL of 1.2 × 10⁻⁶ mcg / mL ... 4 100 cells / well were seeded into 96-well plates and divided into a control group (untreated) and a model group (Aβ). 25-35 Processing), sample group (Aβ) 25-35 +Sample processing). After culturing for 24 h, except for the control group which was treated with DMEM, the rest were treated with 5 μmol / L Aβ25-35 solution for 2 h. The sample group was added with sample solution, and after culturing for 24 h, the supernatant was discarded. 100 μL of MTT solution (final concentration 500 μg / mL) was added and the mixture was incubated in the dark for 4 h. The supernatant was discarded, 100 μL of DMSO was added, and the mixture was shaken at 400 r / min for 10 min. The absorbance was measured at 570 nm.

[0065] Cell viability (%) = (Experimental group OD) 570 / Blank control group OD 570 ) × 100%.

[0066] 2.2 Safety assay of HT22 cells HT22 cells were inoculated at 100 μL with 1.2 × 10⁻⁶ cells / mL. 4 Cells were seeded per well in 96-well plates and cultured for 24 h, divided into a control group (untreated) and a sample group (different concentrations of LLW). After adding the drug and culturing for 24 h, cell viability was determined using the MTT assay described above.

[0067] 2.3 Statistical Analysis Experimental data are expressed as mean ± standard deviation (Mean ± SD). Statistical software was used for plotting and data analysis, with different drug concentrations plotted on the x-axis and cell viability on the y-axis. One-way ANOVA was used for comparisons among multiple groups, combined with Dunnett's multiple comparison test to calculate statistical significance. Different letters indicate statistically significant differences between groups (p < 0.05).

[0068] 3. Experimental Results Cell safety assay results as follows Figure 12 As shown in Figure A, within the concentration range of 62.5-1000 μmol / L, the survival rate of HT22 cells in each LLW treatment group remained at approximately 100%, with no statistically significant difference compared to the blank control group (CT), suggesting that LLW did not exhibit significant cytotoxicity within this concentration range.

[0069] Neuroprotective activity assay results as follows Figure 12 As shown in Figure B, after treatment with 5 μmol / L Aβ 25-35 After treatment, the survival rate of HT22 cells in the model group decreased to approximately 65%, indicating the successful establishment of the in vitro neurotoxicity injury model. After LLW intervention, the viability of HT22 cells was improved compared to the model group, with the cell survival rate in the 250 μmol / L group recovering to approximately 78%. These results suggest that LLW can alleviate Aβ to some extent. 25-35 Induced HT22 cell damage.

[0070] Example 5: In vivo protective activity of tripeptide LLW in a transgenic AD nematode model 1. Experimental Materials The nematodes CL4176, CL802, and OP50 were all from the Caenorhabditis Genetics Center (CGC).

[0071] Main reagents: Levamisole (Aladdin), 5-FUDR (Aladdin), Benzaldehyde (Shanghai E-En Chemical).

[0072] Main instruments: constant temperature shaking incubator, mold incubator, biosafety cabinet, stereo microscope.

[0073] 2. Experimental Methods 2.1 Culture of transgenic AD nematode model OP50 purification: Take frozen OP50 from -80℃, inoculate it into LB solid medium using the four-zone streak method, and incubate it upside down at 37℃ overnight; pick a single colony and inoculate it into LB liquid medium, incubate it at 37℃ and 180 r / min for about 15 h with shaking, centrifuge to remove the supernatant, add M9 to concentrate it 10 times, take 200 μL and spread it on NGM culture plate and air dry.

[0074] Nematode resuscitation and expansion culture: The nematodes were taken out from -80℃, thawed quickly, inoculated into NGM plates containing OP50, and cultured at 16℃; when they grew to L3 stage, they were washed with M9 buffer and transferred to new OP50 NGM plates for expansion culture.

[0075] Nematode synchronization: Wash nematodes containing eggs into a 1.5 mL centrifuge tube with M9 buffer. After the nematodes settle to the bottom, discard the supernatant and wash repeatedly until clear. Keep 700 μL of M9 buffer, add 300 μL of lysis buffer, and after all nematodes have lysed, centrifuge at 2000 r / min for 2 min, wash with M9 4-5 times, and transfer to a culture plate for culture.

[0076] 2.2 Paralysis Experiment The transgenic nematode CL4176 was infused with the human Aβ1-42 gene. Aβ expression was suppressed at 16℃, while Aβ1-42 expression was activated and aggregated at 25℃, causing the nematode to exhibit a paralyzed phenotype. The CL802 nematode, which does not express Aβ1-42, served as a control strain.

[0077] The synchronized CL4176 and CL802 nematodes were transferred to NGM plates without OP50. After growing to the L2 stage, 200 μL of inactivated OP50 bacterial solution was added (inactivated at 70℃ for 30 min), with final drug concentrations of 500 μmol / L, 250 μmol / L, 125 μmol / L, and 62.5 μmol / L, respectively. After air drying, the nematodes were cultured at 16℃ for 12 h and then transferred to 25℃ for further culture. The number of paralyzed nematodes was observed under a microscope every 2 h until complete paralysis was stopped from counting.

[0078] Non-paralyzed rate (%) = (Number of non-paralyzed nematodes / Number of bus nematodes) × 100% 3. Experimental Results The results of the paralysis experiment using the transgenic Caenorhabditis elegans (C. elegans) for Alzheimer's disease (AD) are as follows: Figure 13 As shown in the figure, the CL802 nematode, as a normal control strain (not expressing toxic Aβ protein), did not exhibit a significant paralysis phenotype during the observation period induced by 25℃. In the CL4176 model group, the non-paralysis rate gradually decreased after temperature induction, dropping to approximately 15% by 36 h. After intervention with different concentrations of LLW, the non-paralysis rate curve of the CL4176 nematode shifted to the right overall, suggesting that LLW can delay the Aβ-induced paralysis process. At 36 h, the non-paralysis rates in the 62.5 μmol / L and 125 μmol / L LLW groups were approximately 24% and 28%, respectively; the rates in the 250 μmol / L and 500 μmol / L groups were approximately 32% and 36%, respectively, all higher than the untreated model group. These results suggest that LLW has a certain in vivo neuroprotective effect in the CL4176 nematode model.

[0079] Example 6: Predicting the underlying mechanisms by which LLW (Layered Least Wound) improves AD based on network pharmacology 1. Experimental Methods To investigate the potential molecular mechanism by which LLW improves AD, network pharmacology was used to systematically predict its targets and related pathways. The specific procedure is as follows: (1) Acquisition and intersection identification of LLW target and AD target The chemical structure of the tripeptide LLW was obtained and converted into SMILES encoding. This encoding was input into the SwissTargetPrediction platform (https: / / swisstargetprediction.ch / ) and the SEA database (https: / / sea.bkslab.org / ) to predict potential targets for LLW. Duplicates were removed, and the target set was merged. Simultaneously, using "Alzheimer's disease" as the keyword, AD-related disease targets were retrieved from the GeneCards (https: / / www.genecards.org / ), OpenTargets (https: / / platform.opentargets.org / ), and MalaCards (https: / / www.malacards.org / ) databases. The search results were deduplicated and normalized. The collected potential LLW targets were mapped to AD disease targets, and Venn diagrams were drawn using the ggvenn package in R. The intersection targets were extracted as potential targets for LLW to improve AD.

[0080] (2) PPI network construction and core target screening The obtained intersection targets were imported into the STRING database (https: / / string-db.org / ), with the species limited to "Homo sapiens," to construct a protein-protein interaction (PPI) network. The raw PPI network data was exported and imported into Cytoscape 3.9.1 software for topology analysis and visualization. Using the CytoHubba plugin in Cytoscape, the top-ranking genes were extracted using Degree, MCC (maximum clique centrality), and EPC (edge ​​penetration interconnectivity) algorithms.

[0081] (3) GO function and KEGG pathway enrichment analysis The list of intersecting targets was imported into the Metascape platform (https: / / metascape.org / ) for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. After exporting the analysis results, bubble charts / bar charts were used to visualize the significantly enriched biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways using relevant R language plotting packages.

[0082] (4) Molecular docking study of LLW with improving AD core target Molecular docking techniques were used to evaluate the binding affinity between LLW and the core targets (AKT1 and CASP3) screened by network pharmacology. The three-dimensional crystal structures of AKT1 (PDB ID: 4GV1) and CASP3 (PDB ID: 1GFW) were downloaded from the RCSB PDB database. The protein receptors were preprocessed using PyMOL software and AutoDockTools, including the removal of water molecules and co-crystallized ligands, the addition of polar hydrogen atoms, and the assignment of Kollman charges. The LLW structures were converted to PDBQT format after energy minimization. Docking simulations were performed using AutoDock Vina v1.2.0 software, with the mesh frame covering the active sites of the target proteins. The docking results were visualized and analyzed using PyMOL in three dimensions.

[0083] 2. Experimental Results 2.1 Network Pharmacology-Based LLW Improves Potential Target Prediction and Pathway Enrichment Analysis in Alzheimer's Disease (AD) Network pharmacology predictions show that LLW has identified 291 potential targets, with 74 overlapping targets (25.4% of the total targets for LLW) among the 1571 disease targets related to AD. Figure 14 This suggests a clear correlation between LLW and AD at the molecular level. A PPI network was constructed using the intersection targets, and three topology algorithms (MCC, Degree, and EPC) were used to screen core nodes. AKT1 and CASP3 were identified as key nodes by all three algorithms. Figures 15-16 GO enrichment was mainly concentrated in functions related to proteolysis and protein processing; KEGG analysis showed the most significant enrichment in the Alzheimer's disease pathway, and also involved signaling pathways such as apoptosis, TNF, and IL-17. Figures 17-18 The above results indicate that the potential mechanism of action of LLW may involve core pathological processes in AD, such as APP / Aβ metabolism, apoptosis regulation, and inflammatory signaling.

[0084] 2.2 Molecular docking of LLW with the core target AKT1 / CASP3 Molecular docking showed that LLW had a docking affinity of -8.503 kcal / mol with AKT1 and -7.645 kcal / mol with CASP3, both exhibiting good binding tendency. LLW could embed into the corresponding binding pockets and maintain the stability of the complex through hydrogen bonding and hydrophobic / π-related interactions. Figures 19-20 Based on the results of network pharmacology screening, it is suggested that LLW may exert potential neuroprotective effects by regulating AKT1-mediated cell survival signaling and CASP3-mediated apoptosis execution pathway.

[0085] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. The application of an active tripeptide in the preparation of drugs to improve Alzheimer's disease, characterized in that, The amino acid sequence of the active tripeptide is LLW.

2. The application of an active tripeptide in the preparation of neuroprotective drugs, characterized in that, The amino acid sequence of the active tripeptide is LLW.

3. The application of an active tripeptide in the preparation of a drug for alleviating oxidative stress, characterized in that, The amino acid sequence of the active tripeptide is LLW.

4. The application according to any one of claims 1-3, characterized in that, The drug includes pharmaceutically acceptable excipients.

5. The application according to claim 4, characterized in that, The excipients include excipients, diluents, fillers, binders, disintegrants, emulsifiers, flavoring agents, preservatives, or colorants.

6. The application according to claim 4, characterized in that, The drug is an oral formulation.

7. The application according to claim 6, characterized in that, The oral preparations are powders, tablets, granules, suspensions, capsules, pills, or oral liquids.

8. The application of an active tripeptide in the preparation of acetylcholinesterase inhibitors, characterized in that, The amino acid sequence of the active tripeptide is LLW.

Images