A yak bone anti-aging peptide targeting MMP-1 TGPPGPSGI and application thereof

Abstract

The present application relates to the technical field of small molecule peptide, in particular to a kind of anti-aging peptide TGPPGPSGI of yaks bone targeted to MMP-1 and application thereof.The amino acid sequence is as shown in SEQ ID NO:1.The anti-aging peptide TGPPGPSGI obtained in the present application can be applied in the preparation of health products and cosmetics.The present application successfully screens and verifies the yaks bone source bioactive peptide TGPPGPSGI with good MMP-1 inhibitory activity, which provides new natural raw materials and theoretical basis for the development of anti-skin aging related products.

Description

Technical Field

[0001] This invention relates to the field of small molecule peptide technology, specifically to a yak bone anti-aging peptide TGPPGPSGI targeting MMP-1 and its applications. Background Technology

[0002] Yaks are a distinctive livestock breed found at high altitudes, and their bones are a major byproduct of meat processing, rich in collagen and minerals, possessing immense resource potential. However, currently, yak bones are mostly discarded, easily causing environmental pollution and resource waste. There is an urgent need to transform them into high-value-added bioactive components to enhance the economic and ecological benefits of the yak industry.

[0003] The structural integrity of the type I collagen network provides the mechanical basis for skin elasticity and tensile strength. Skin aging is closely related to the degradation of type I collagen; external stimuli such as ultraviolet radiation upregulate matrix metalloproteinase-1 (MMP-1), accelerating collagen breakdown. Therefore, identifying natural bioactive inhibitors that can regulate this proteolytic process is a key strategy for developing preventative anti-aging functional foods and cosmetics.

[0004] Currently, various MMP-1 inhibitors have been used in anti-aging activity studies, but most existing inhibitors are mainly chemically synthesized small molecules or complex chemical components from plant extracts. While these components can inhibit MMP-1 activity to some extent, some chemical inhibitors may have issues such as skin irritation, unclear long-term safety, complex composition, difficulty in quality control, or insufficient biocompatibility, limiting their widespread application in functional foods, cosmetics, and daily skincare products. Therefore, developing novel MMP-1 inhibitory active ingredients with clear sources, well-defined structures, and high safety profiles is of great significance.

[0005] Bioactive short peptides have gained widespread attention in recent years for their small molecular weight, well-defined structure, easy absorption, ease of modification, and synthetic potential, particularly in areas such as antioxidation, anti-inflammation, anti-aging, and skin barrier repair. Some short peptides exhibit low cytotoxicity and good biocompatibility, offering significant safety advantages in the development of functional foods and cosmetics.

[0006] Therefore, in response to the problems mentioned in the background art, this invention provides an anti-aging peptide screening scheme that combines deep learning virtual screening, molecular simulation and experimental verification, and successfully extracted the highly active anti-aging peptide TGPPGPSGI, providing new natural raw materials and theoretical basis for the development of anti-skin aging related products. Summary of the Invention

[0007] The purpose of this invention is to provide a yak bone anti-aging peptide TGPPGPSGI targeting MMP-1 and its application, in order to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A yak bone anti-aging peptide TGPPGPSGI targeting MMP-1, the amino acid sequence of which is shown in SEQ ID NO:1.

[0009] The application of the above-mentioned yak bone anti-aging peptide TGPPGPSGI targeting MMP-1 in the preparation of health products and cosmetics.

[0010] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention significantly improves the efficiency of anti-aging peptide screening by employing deep learning virtual screening, molecular simulation, and experimental verification. It can rapidly identify the most promising TGPPGPSGI peptides from a vast array of sequences derived from yak bone collagen hydrolysates, thereby saving substantial experimental costs and time and significantly enhancing the discovery efficiency of lead anti-aging peptides. This efficient and economical screening method not only reduces resource waste but also improves the accuracy and reliability of the screening process.

[0011] 2. This invention successfully screened and verified yak bone-derived bioactive peptide TGPPGPSGI with good MMP-1 inhibitory activity, providing new natural raw materials and theoretical basis for the development of anti-skin aging related products. Its excellent performance has great application potential in the field of preparing health products and cosmetics. Attached Figure Description

[0012] Figure 1 This is a graph showing the ROC curves for each fold in this invention; Figure 2 This is a diagram of the average confusion matrix of the model in this invention; Figure 3 This is a bar chart showing the enzyme activity inhibition of samples at different concentrations in this invention; Figure 4 The concentration-effect curve and IC50 of the enzyme inhibiting sample in this invention are shown. 50 value; Figure 5 This is a schematic diagram of the overall structure after the sample is docked with MMP-1; Figure 6 A schematic diagram of the residues that form hydrogen bonds when the sample binds to MMP-1: Figure 7 A detailed schematic diagram of the binding sites between the sample and MMP-1; Figure 8 This is a three-dimensional conformation and HOMO orbital distribution diagram of the sample in this invention; Figure 9 This is a three-dimensional conformation of the sample and a diagram showing the LUMO orbital distribution in this invention. Detailed Implementation

[0013] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figures 1 to 9 The present invention provides: Example 1: Data Collection and Cleaning This invention retrieved 282 peptide sequences from the AagingBase database; simultaneously, using "anti-aging peptides" as the keyword, a systematic search was conducted on relevant literature and patents published between 2023 and 2025, supplementing the database with 29 peptides that have been experimentally verified to possess anti-aging activity. All included sequences were experimentally confirmed to have anti-aging functions, ultimately constructing a positive sample set containing 311 peptides. Since no dedicated anti-aging peptide negative samples were available, this study selected 311 peptide sequences from the DRAMP 4.0 database as negative samples, ensuring that their sequence distribution characteristics remained consistent with the positive samples.

[0015] Example 2: Development and Evaluation of Predictive Models To capture the high-dimensional biological information of peptides, this invention employs a pre-trained protein language model ESM2 (esm2_t33_650M_UR50D). By extracting the hidden states of the last layer of the ESM2Transformer module, each peptide sequence is encoded into a 1280-dimensional fixed-length numerical vector, providing a stable semantic representation for the peptide sequence based on evolutionary information.

[0016] To achieve accurate classification of anti-aging peptides, this invention constructs a dedicated deep learning architecture, AgeAttnMLP. This model uses a pre-activated backbone network as its core and contains four residual blocks. Each residual block integrates layer normalization, batch normalization, and the GELU activation function to ensure stable gradient propagation and efficient feature extraction. Simultaneously, it introduces efficient channel attention (ECA) and coordinate attention mechanisms to capture local cross-channel interaction information and embed class space sequence information without dimensionality reduction. The model further enhances generalization ability through path dropout and random weight averaging (SWA) and employs a dynamic focus loss function combined with label smoothing to alleviate class imbalance and strengthen the learning effect on difficult-to-classify samples.

[0017] This invention compares AgeAttnMLP with six machine learning and deep learning models: Random Forest (RF), Support Vector Machine (SVM), XGBoost, Transformer, and MLP. The machine learning models use the Optuna framework for automatic hyperparameter optimization, while the deep learning models are manually tuned based on validation set performance to ensure optimal prediction performance. A five-fold cross-validation strategy is employed to ensure model reliability and generalization. The model's stability is verified by comprehensively evaluating multiple metrics, including accuracy, precision, recall, F1 score, MCC, AUC, and AP, and calculating the mean and standard deviation.

[0018] The AgeAttnMLP model constructed in this invention is evaluated for performance using five-fold cross-validation, and the results are as follows: Figure 1 and Figure 2 As shown. Figure 1 The average AUC for each ROC curve is 0.891 ± 0.037. Figure 2 The average confusion matrix of the model is shown, where the number of true negatives, false positives, false negatives, and true positives are 48.0, 14.2, 10.0, and 52.2, respectively, which intuitively reflects the prediction accuracy of the model in the classification task.

[0019] Multidimensional analysis of key performance indicators validated that the AgeAttnMLP model of this invention exhibits the best performance in core indicators such as accuracy (0.806), precision (0.789), F1 score (0.813), AUC (0.891), and average precision (AP) (0.885), as shown in Table 1 below. This model achieves a better balance between sensitivity and predictive reliability, and its overall discriminative ability is significantly superior to the comparative models, meeting the needs of large-scale high-throughput screening of biological sequences.

[0020] Table 1: Performance Comparison of Different Models for Predicting Anti-Aging Peptides RandomForest 0.785 0.779 0.798 0.786 0.572 0.868 0.857 SVM 0.783 0.764 0.821 0.789 0.571 0.862 0.841 XGBoost 0.796 0.783 0.820 0.801 0.593 0.869 0.860 Transformer 0.799 0.763 0.868 0.812 0.605 0.826 0.773 MLP 0.802 0.788 0.833 0.809 0.648 0.875 0.856 AgeAttnMLP 0.806 0.789 0.839 0.813 0.614 0.891 0.885 Example 3: Virtual Enzymatic Screening and Grading Screening Given the high homology between yak and bovine type I collagen sequences, this invention uses bovine type I collagen sequences as templates for virtual enzymatic digestion. A combination of pepsin, trypsin, and chymotrypsin A was used as a basic enzyme combination to compare the virtual enzymatic digestion efficiency of various industrial enzyme combinations. The optimal efficiency for screening active peptides was found to be found with alkaline protease. Peptide fragments of 3–9 amino acids were obtained through screening, and their anti-aging activity was then predicted using the AgeAttnMLP model.

[0021] This invention employs a tiered screening process. First, the top 50 peptides with the highest activity scores are selected using the AgeAttnMLP model. Then, molecular docking is performed between GNINA 1.3 and MMP-1, and the top 10 peptides are screened based on binding affinity. Next, the ToxinPred 3.0 Hybrid model is used for safety evaluation. TGPPGPSGI (SEQ ID NO:1, full name Thr-Gly-Pro-Pro-Gly-Pro-Ser-Gly-Ile) showed the lowest toxicity score (0.073) among all candidate peptides, indicating the best safety profile. To verify the biocompatibility of TGPPGPSGI, Caco-2 cells were used for in vitro cytotoxicity evaluation. TGPPGPSGI was treated at concentrations of 0, 100, 200, and 400 μg / mL, and cell viability was measured. The results showed that the cell viability rates for each concentration treatment group were 100.00±0.80%, 99.70±1.28%, 96.73±0.80%, and 94.90±1.21%, respectively. Even at a high concentration of 400 μg / mL, cell viability remained above 90%, indicating that TGPPGPSGI did not exhibit significant cytotoxicity within the experimental concentration range and demonstrated good in vitro biosafety. The peptide had a purity of 95% and was synthesized by Liaoning Newkin Biochemical Technology Co., Ltd., China.

[0022] Example 4: MMP-1 inhibitory activity assay The inhibitory activity of the peptide against MMP-1 was determined using fluorescence resonance energy transfer (FRET) as a fluorescent substrate. Recombinant human pro-MMP-1 was activated with 1 mM APMA at 37 °C for 2 h. A 100 μL reaction system was constructed in a black 96-well plate, containing detection buffer (50 mM Tris-HCl, pH 7.5, 10 mM CaCl2, 150 mM NaCl, 0.05% Brij-35), activated MMP-1, and the target peptide at final concentrations of 0, 100, 200, and 400 μg / mL. After pre-incubation at 37 °C for 30 min, a final concentration of 10 μM fluorescent substrate MCA-KPLGL-DPA-AR-NH2 was added to initiate the reaction. Fluorescence intensity was detected at an excitation wavelength of 320 nm and an emission wavelength of 405 nm, and the initial reaction rate was calculated.

[0023] With the enzyme activity of the control group (0 μg / mL) as 100%, the inhibition rate of the peptide against MMP-1 was calculated, a dose-response curve was plotted, and the IC50 was calculated using a four-parameter logistic model with nonlinear regression. 50 The experiment was repeated three times, and the data are expressed as mean ± standard deviation. Statistical tests were performed using one-way ANOVA.

[0024] The results are shown in the table below. Figure 3 and Figure 4As shown, TGPPGPSGI significantly inhibited the proteolytic activity of MMP-1, with the inhibitory effect increasing in a dose-dependent manner within the concentration range of 0–400 μg / mL. At 400 μg / mL, the enzyme activity of MMP-1 was reduced to less than 40% of the initial level. Nonlinear regression analysis calculated the IC50 of TGPPGPSGI for inhibiting MMP-1. 50 With a value of 145 μg / mL, it can effectively block the degradation of skin collagen and has excellent anti-skin aging activity.

[0025] Example 5: Molecular dynamics simulation combined with free energy calculation AlphaFold3 was used to predict the initial binding conformation of candidate peptides to MMP-1, using the primary sequences of MMP-1 and the target peptide as input, and simultaneously adding 2 Zn atoms. 2+ 1 Ca 2+ To accurately reconstruct the active site of metalloproteinases, a high-confidence model with high local distance difference test (pLDDT) score and low prediction alignment error (PAE) was selected as the initial structure for subsequent atomic-level simulations.

[0026] Molecular dynamics (MD) simulations were performed using Amber24 software. For proteins and peptides, the ff19SB force field was used, and the OPC water model was employed as the solvent. Zn... 2+ Ca 2+ A 12-6-4 multi-site ion model was employed to ensure the accuracy of coordination geometry. Multi-stage energy minimization was performed: first, a 5000-step steepest descent method was used, followed by a 5000-step conjugate gradient method to eliminate spatial conflicts; then, under the NVT ensemble, the system was gradually heated from 0 K to 310 K over 500 ps to apply weak constraints to the heavy atoms of the solute; next, 50 ns of equilibrium was achieved under the NPT ensemble at 1 atm and 310 K to stabilize the system density; finally, a 500 ns kinetic simulation was completed under the unconstrained NPT ensemble.

[0027] The binding free energy of the peptide to MMP-1 was quantitatively calculated using molecular mechanics / Poisson-Boltzmann surface area method (MM-PBSA) to characterize the thermodynamic driving force of their interaction. A total of 500 representative conformations were extracted from every 10 frames of the stable kinetic trajectory to ensure the statistical significance of the energy assessment.

[0028] A schematic diagram of the overall structure of the TGPPGPSGI peptide after docking with the MMP-1 molecule, as shown below. Figure 5 As shown; Figure 6 This is a schematic diagram of the residues in which the peptide binds to MMP-1 to form hydrogen bonds. It can be seen that the peptide is stably bound to the catalytically active pocket of MMP-1. Key residues L75, A76, A78 and E113 participate in the interaction with the peptide, providing core stability support for the conformation of the complex. Figure 7The TGPPGPSGI peptide also forms extensive van der Waals contacts with residues such as Asn74, His77, Gln80, His116, His122, Ser66, and Phe68 around the binding pocket, indicating good spatial complementarity between the peptide and the MMP-1 protein surface. Aromatic rings or hydrophobic segments in the peptide structure can also generate π-π related interactions with residues such as Phe79, His112, and Val109 in the protein. In summary, this binding mode indicates that the TGPPGPSGI peptide and MMP-1 protein do not bind via a single force, but rather form a stable complex interface through hydrogen bonds, van der Waals interactions, and π-π interactions. The total binding free energy of TGPPGPSGI and MMP-1 is -6.19 ± 0.26 kJ / mol, and its binding is stabilized by both electrostatic interactions and van der Waals forces, exhibiting good thermodynamic binding characteristics, as shown in Table 2 below. Table 2: MM-PBSA binding energy of peptides TGPPGPSGI and MMP-1 (kJ / mol)

[0029] Example 6: Density Functional Theory (DFT) Calculation Quantum chemical calculations were performed using Gaussian 16 software to clarify the electronic properties and reaction sites of the anti-aging peptide TGPPGPSGI. Geometric optimization of the peptide sequence was performed at the Mo6-2X / 6-31G (d,p) theoretical level, a method that can accurately describe the non-covalent interactions and electronic transitions of biomolecules. A density-based solvation model (SMD) was used to simulate the physiological environment using water as a solvent; frequency analysis was performed at the same theoretical level to confirm that the optimized structure represents a true local minimum without imaginary frequencies. The highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and molecular electrostatic potential (MEP) surface were extracted and analyzed to characterize the peptide's chemical reactivity and potential binding sites.

[0030] like Figure 8 and Figure 9 As shown, the spatial distribution and energy levels of the highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO) of each peptide are clearly defined. The HOMO energy level of TGPPGPSGI is -4.28 eV, indicating that this peptide has a stronger electron-donating ability and can effectively scavenge reactive oxygen species in the body, possessing excellent antioxidant application potential. Molecular orbital band gaps can characterize chemical stability and reactivity. TGPPGPSGI has a relatively larger band gap value, maintaining good structural stability while ensuring reactivity.

[0031] This invention designs a method for preparing the MMP-1 inhibitory peptide TGPPGPSGI from yak bone collagen and its application in anti-skin aging products. It aims to address the problems of poor stability and strong irritation of existing anti-aging ingredients, as well as the low efficiency and high cost of traditional screening methods for natural active peptides, and provides a new approach for the development of natural anti-aging active peptides. By combining deep learning prediction, virtual enzymatic digestion, molecular dynamics simulation, and experimental verification, a hierarchical virtual screening technique is used to identify the potential MMP-1 inhibitory peptide TGPPGPSGI in yak bone collagen. The implementation of this method not only significantly improves the screening efficiency of anti-aging active peptides but also greatly reduces the cost and time required for traditional experimental screening. Studies have shown that TGPPGPSGI exhibits significant MMP-1 inhibitory activity in vitro, with an IC50 value of [missing information]. 50 The concentration is as low as 145 μg / mL, and safety assessments showed no significant toxicity, demonstrating good biocompatibility. Furthermore, molecular docking, kinetic simulations, and DFT calculations confirmed that TGPPGPSGI exerts its inhibitory effect by specifically binding to the MMP-1 active pocket and forming a stable interaction network, while also exhibiting excellent antioxidant potential, further validating its application potential as a natural anti-aging active peptide. This invention successfully screened and validated TGPPGPSGI, a yak bone-derived bioactive peptide with good MMP-1 inhibitory activity, providing new natural raw materials and theoretical basis for the development of anti-skin aging-related products. Simultaneously, this invention provides a new direction for the research and development of natural anti-aging active peptides based on yak bone collagen.

[0032] In summary, this invention not only has high application prospects and social benefits, but also provides new ideas for the further research and development of natural MMP-1 inhibitory peptides and anti-aging active ingredients, and has important scientific significance and economic value.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A yak bone anti-aging peptide TGPPGPSGI targeting MMP-1, characterized in that, Its amino acid sequence is shown in SEQ ID NO:

1.

2. The application of the yak bone anti-aging peptide TGPPGPSGI targeting MMP-1 as described in claim 1 in the preparation of health products and cosmetics.

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