Discovery of Deer Antler-Derived Antioxidant Peptides Through Computational and Cell-Based Approaches
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. In Silico Hydrolysis and Antioxidant Peptide Screening
2.3. DPPH Radical Scavenging Assay
2.4. Cell Culture and Subculture
2.5. CCK-8 Cell Viability Assay
2.6. Oxidative Damage Model Construction
2.7. Protective Effects of Velvet Antler Antioxidant Peptides on D-Galactose-Induced Cytotoxicity
2.8. Molecular System Preparation
2.9. Molecular Docking
2.10. Molecular Dynamics Simulations
2.11. Method of Dynamic Cross-Correlation Matrix (DCCM) Analysis
2.12. Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) Analysis
2.13. Statistical Analysis
3. Results and Discussion
3.1. Screening of Deer Antler-Derived Peptides
3.2. DPPH Radical Scavenging Activity of Velvet Antler Antioxidant Peptides
3.3. Cytotoxicity of Velvet Antler Antioxidant Peptides in HepG2 Cells
3.4. Protective Effects of Antioxidant Peptides Against D-Galactose-Induced Oxidative Stress
3.5. Molecular Docking Analysis
3.6. Structural Stability Analysis
3.7. MM/PBSA Analysis
3.8. Dynamic Cross-Correlation Matrix (DCCM) Analysis
3.9. Secondary Structure Analysis
3.10. Prediction of Peptide Properties
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
D-Gal | D-galactose |
ROS | reactive oxygen species |
AI | artificial intelligence |
CCK-8 | Counting Kit-8 |
DPPH | 1,1-diphenyl-2-picrylhydrazyl |
FBS | fetal bovine serum |
Rg | radius of gyration |
SASA | solvent-accessible surface area |
RMSF | root-mean-square fluctuation |
DSSP | secondary structure distribution |
DCCM | dynamic cross-correlation matrices |
Keap1 | Kelch-like ECH-associated protein 1 |
References
- Kehrer, J.P.; Klotz, L.O. Free radicals and related reactive species as mediators of tissue injury and disease: Implications for Health. Crit. Rev. Toxicol. 2015, 45, 765–798. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.Q.; Wang, W.; Peng, M.; Zhang, X.Z. Free radicals for cancer theranostics. Biomaterials 2021, 266, 120474. [Google Scholar] [CrossRef] [PubMed]
- Kikuchi, K.; Tancharoen, S.; Takeshige, N.; Yoshitomi, M.; Morioka, M.; Murai, Y.; Tanaka, E. The efficacy of edaravone (radicut), a free radical scavenger, for cardiovascular disease. Int. J. Mol. Sci. 2013, 14, 13909–13930. [Google Scholar] [CrossRef] [PubMed]
- Singh, A.; Kukreti, R.; Saso, L.; Kukreti, S. Oxidative Stress: A Key Modulator in Neurodegenerative Diseases. Molecules 2019, 24, 1583. [Google Scholar] [CrossRef]
- Liu, Y.Y.; Ding, Y.F.; Sui, H.J.; Liu, W.; Zhang, Z.Q.; Li, F. Pilose antler (Cervus elaphus Linnaeus) polysaccharide and polypeptide extract inhibits bone resorption in high turnover type osteoporosis by stimulating the MAKP and MMP-9 signaling pathways. J. Ethnopharmacol. 2023, 304, 116052. [Google Scholar] [CrossRef]
- Liu, L.; Wu, L.; Wang, Y.; Sun, Z.; Shuang, R.; Shi, Z.; Dong, Y. Monomeric pilose antler peptide improves depression-like behavior in mice by inhibiting FGFR3 protein expression. J. Ethnopharmacol. 2024, 327, 117973. [Google Scholar] [CrossRef]
- Li, M.; Li, Q.; Dong, H.; Zhao, S.; Ning, J.; Bai, X.; Yue, X.; Xie, A. Pilose antler polypeptides enhance chemotherapy effects in triple-negative breast cancer by activating the adaptive immune system. Int. J. Biol. Macromol. 2022, 222 Pt B, 2628–2638. [Google Scholar] [CrossRef]
- Liu, Y.F.; Oey, I.; Bremer, P.; Carne, A.; Silcock, P. Bioactive peptides derived from egg proteins: A review. Crit. Rev. Food Sci. Nutr. 2018, 58, 2508–2530. [Google Scholar] [CrossRef]
- Ren, C.; Gong, W.; Li, F.; Xie, M. Pilose antler aqueous extract promotes the proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells by stimulating the BMP-2/Smad1, 5/Runx2 signaling pathway. Chin. J. Nat. Med. 2019, 17, 756–767. [Google Scholar] [CrossRef]
- Zheng, K.; Li, Q.; Lin, D.; Zong, X.; Luo, X.; Yang, M.; Yue, X.; Ma, S. Peptidomic analysis of pilose antler and its inhibitory effect on triple-negative breast cancer at multiple sites. Food Funct. 2020, 11, 7481–7494. [Google Scholar] [CrossRef]
- Yao, B.; Zhang, M.; Leng, X.; Liu, M.; Liu, Y.; Hu, Y.; Zhao, D.; Zhao, Y. Antler extracts stimulate chondrocyte proliferation and possess potent anti-oxidative, anti-inflammatory, and immune-modulatory properties. Vitr. Cell. Dev. Biol. Anim. 2018, 54, 439–448. [Google Scholar] [CrossRef]
- Liu, C.; Xia, Y.; Hua, M.; Li, Z.; Zhang, L.; Li, S.; Gong, R.; Liu, S.; Wang, Z.; Sun, Y. Functional properties and antioxidant activity of gelatine and hydrolysate from deer antler base. Food Sci. Nutr. 2020, 8, 3402–3412. [Google Scholar] [CrossRef]
- Wang, J.; Yang, G.; Li, H.; Zhang, T.; Sun, D.; Lu, W.P.; Zhang, W.; Wang, Y.; Ma, M.; Cao, X.; et al. Preparation and identification of novel antioxidant peptides from camel bone protein. Food Chem. 2023, 424, 136253. [Google Scholar] [CrossRef] [PubMed]
- O’Keeffe, M.B.; FitzGerald, R.J. Identification of short peptide sequences in complex milk protein hydrolysates. Food Chem. 2015, 184, 140–146. [Google Scholar] [CrossRef] [PubMed]
- Itoh, K.; Wakabayashi, N.; Katoh, Y.; Ishii, T.; Igarashi, K.; Engel, J.D.; Yamamoto, M. Keap1 Represses Nuclear Activation of Antioxidant Responsive Elements by Nrf2 through Binding to the Amino-Terminal Neh2 Domain. Genes Dev. 1999, 13, 76–86. [Google Scholar] [CrossRef] [PubMed]
- Dinkova-Kostova, A.T.; Abramov, A.Y. The Emerging Role of Nrf2 in Mitochondrial Function. Free. Radic. Biol. Med. 2015, 88 Pt B, 179–188. [Google Scholar] [CrossRef]
- Hayes, J.D.; Dinkova-Kostova, A.T. The Nrf2 Regulatory Network Provides an Interface between Redox and Intermediary Metabolism. Trends Biochem. Sci. 2014, 39, 199–218. [Google Scholar] [CrossRef]
- Wang, G.; Vaisman, I.I.; van Hoek, M.L. Machine Learning Prediction of Antimicrobial Peptides. Methods Mol. Biol. 2022, 2405, 1–37. [Google Scholar]
- Du, A.; Jia, W.; Zhang, R. Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion. J. Dairy Sci. 2024, 107, 8837–8851. [Google Scholar] [CrossRef]
- Nha Tran, T.T.; Thuan Tran, T.D.; Thuy Bui, T.T. Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides. RSC Adv. 2023, 13, 33707–33720. [Google Scholar] [CrossRef]
- Sayers, E.W.; Beck, J.; Bolton, E.E.; Brister, J.R.; Chan, J.; Connor, R.; Feldgarden, M.; Fine, A.M.; Funk, K.; Hoffman, J.; et al. Database resources of the National Center for Biotechnology Information in 2025. Nucleic Acids Res. 2025, 53, D20–D29. [Google Scholar] [CrossRef] [PubMed]
- Rathore, A.S.; Choudhury, S.; Arora, A.; Tijare, P.; Raghava, G.P.S. ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Comput. Biol. Med. 2024, 179, 108926. [Google Scholar] [CrossRef] [PubMed]
- Olsen, T.H.; Yesiltas, B.; Marin, F.I.; Pertseva, M.; García-Moreno, P.J.; Gregersen, S.; Overgaard, M.T.; Jacobsen, C.; Lund, O.; Hansen, E.B.; et al. AnOxPePred: Using deep learning for the prediction of antioxidative properties of peptides. Sci. Rep. 2020, 10, 21471. [Google Scholar] [CrossRef] [PubMed]
- Qin, D.; Jiao, L.; Wang, R.; Zhao, Y.; Hao, Y.; Liang, G. Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors. Comput. Biol. Med. 2023, 154, 106591. [Google Scholar] [CrossRef]
- Yang, J.; Huang, J.; Dong, X.; Zhang, Y.; Zhou, X.; Huang, M.; Zhou, G. Purification and identification of antioxidant peptides from duck plasma proteins. Food Chem. 2020, 319, 126534. [Google Scholar] [CrossRef]
- Lo, S.C.; Li, X.; Henzl, M.T.; Beamer, L.J.; Hannink, M. Structure of the Keap1:Nrf2 interface provides mechanistic insight into Nrf2 signaling. EMBO J. 2006, 25, 3605–3617. [Google Scholar] [CrossRef]
- Hanwell, M.D.; Curtis, D.E.; Lonie, D.C.; Vandermeersch, T.; Zurek, E.; Hutchison, G.R. Avogadro: An Advanced Semantic Chemical Editor, Visualization, and Analysis Platform. J. Cheminform. 2012, 4, 17. [Google Scholar]
- Zhou, P.; Jin, B.; Li, H.; Huang, S.Y. HPEPDOCK: A web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res. 2018, 46, W443–W450. [Google Scholar] [CrossRef]
- Schrödinger, LLC. The PyMOL Molecular Graphics System, Version 3.0.3; Schrödinger, LLC: New York, NY, USA, 2024. [Google Scholar]
- Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011, 51, 2778–2786. [Google Scholar] [CrossRef]
- Case, D.A.; Aktulga, H.M.; Belfon, K.; Ben-Shalom, I.Y.; Berryman, J.T.; Brozell, S.R.; Cerutti, D.S.; Cheatham, T.E., III; Cisneros, G.A.; Cruzeiro, V.W.D.; et al. AMBER 2022; University of California: San Francisco, CA, USA, 2022. [Google Scholar]
- Tian, C.; Kasavajhala, K.; Belfon, K.A.A.; Raguette, L.; Huang, H.; Migues, A.N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q.; et al. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16, 528–552. [Google Scholar]
- Izadi, S.; Anandakrishnan, R.; Onufriev, A.V. Building Water Models: A Different Approach. J. Phys. Chem. Lett. 2014, 5, 3863–3871. [Google Scholar] [CrossRef]
- Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888. [Google Scholar] [CrossRef]
- dos Santos Nascimento, I.J.; De Souza, M.; Medeiros, D.C.; de Moura, R.O. Dynamic Cross-Correlation Matrix (DCCM) Reveals New Insights to Discover New NLRP3 Inhibitors Useful as Anti-Inflammatory Drugs. Med. Sci. Forum 2022, 14, 84. [Google Scholar]
- Gulcin, İ. Antioxidants and antioxidant methods: An updated overview. Arch. Toxicol. 2020, 94, 651–715. [Google Scholar] [CrossRef] [PubMed]
- Kravchenko, S.V.; Domnin, P.A.; Grishin, S.Y.; Vershinin, N.A.; Gurina, E.V.; Zakharova, A.A.; Azev, V.N.; Mustaeva, L.G.; Gorbunova, E.Y.; Kobyakova, M.I.; et al. Enhancing the Antimicrobial Properties of Peptides through Cell-Penetrating Peptide Conjugation: A Comprehensive Assessment. Int. J. Mol. Sci. 2023, 24, 16723. [Google Scholar] [CrossRef] [PubMed]
- Lu, J.; Wu, D.M.; Zheng, Y.L.; Hu, B.; Cheng, W.; Zhang, Z.F. Purple sweet potato color attenuates domoic acid-induced cognitive deficits by promoting estrogen receptor-α-mediated mitochondrial biogenesis signaling in mice. Free. Radic. Biol. Med. 2012, 52, 646–659. [Google Scholar] [CrossRef] [PubMed]
- Li, N.; He, Y.; Wang, L.; Mo, C.; Zhang, J.; Zhang, W.; Li, J.; Liao, Z.; Tang, X.; Xiao, H. D-galactose induces necroptotic cell death in neuroblastoma cell lines. J. Cell. Biochem. 2011, 112, 3834–3844. [Google Scholar] [CrossRef]
- Swanson, K.; Walther, P.; Leitz, J.; Mukherjee, S.; Wu, J.C.; Shivnaraine, R.V.; Zou, J.; Valencia, A. Admet-Ai: A Machine Learning Admet Platform for Evaluation of Large-Scale Chemical Libraries. Bioinformatics 2024, 40, btae416. [Google Scholar] [CrossRef]
NO. | Sequence | Scavenger | Chelator | Score | Toxicity Prediction | Toxicity Likelihood |
---|---|---|---|---|---|---|
1 | PPPPL | 0.53 | 0.34 | 0.47 | Toxic | 0.69 |
2 | VPHGL | 0.54 | 0.29 | 0.46 | Non-Toxic | 0.25 |
3 | CAPHPL | 0.53 | 0.30 | 0.46 | Toxic | 0.74 |
4 | QQPPPAPL | 0.50 | 0.31 | 0.44 | Toxic | 0.43 |
5 | EPAHL | 0.50 | 0.30 | 0.44 | Non-Toxic | 0.16 |
6 | PHPAPTL | 0.49 | 0.30 | 0.44 | Non-Toxic | 0.30 |
7 | ANTPHL | 0.50 | 0.28 | 0.43 | Non-Toxic | 0.17 |
8 | PGEPGL | 0.51 | 0.26 | 0.43 | Non-Toxic | 0.25 |
9 | PPTGIHPL | 0.51 | 0.25 | 0.43 | Toxic | 0.48 |
10 | GGDGNHVL | 0.51 | 0.24 | 0.43 | Non-Toxic | 0.16 |
System | PHPAPTL | VPHGL | GSH |
---|---|---|---|
ΔEvdw | −29.03 ± 0.73 | −18.60 ± 0.48 | −30.90 ± 0.23 |
ΔEele | −183.62 ± 3.56 | −161.46 ± 2.63 | −53.73 ± 1.11 |
ΔGsolv | 182.76 ± 3.25 | 154.95 ± 2.54 | 85.95 ± 0.98 |
ΔGgas | −212.65 ± 3.84 | −180.05 ± 2.75 | −89.63 ± 1.16 |
ΔGtotal | −29.89 ± 0.80 | −25.10 ± 0.48 | −3.68 ± 0.64 |
Peptide | Quantitative Estimate of Druglikeness | Human Intestinal Absorption | Oral Bioavailability |
---|---|---|---|
PHPAPTL | 0.09 | 0.09 | 0.23 |
VPHGL | 0.19 | 0.45 | 0.49 |
GSH | 0.13 | 0.25 | 0.61 |
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Jiang, Y.; Zheng, J.; Zhang, Y.; Liu, Y.; Zeng, L.; Han, W. Discovery of Deer Antler-Derived Antioxidant Peptides Through Computational and Cell-Based Approaches. Antioxidants 2025, 14, 1169. https://doi.org/10.3390/antiox14101169
Jiang Y, Zheng J, Zhang Y, Liu Y, Zeng L, Han W. Discovery of Deer Antler-Derived Antioxidant Peptides Through Computational and Cell-Based Approaches. Antioxidants. 2025; 14(10):1169. https://doi.org/10.3390/antiox14101169
Chicago/Turabian StyleJiang, Yongxin, Jingxian Zheng, Yan Zhang, Yuyang Liu, Linlin Zeng, and Weiwei Han. 2025. "Discovery of Deer Antler-Derived Antioxidant Peptides Through Computational and Cell-Based Approaches" Antioxidants 14, no. 10: 1169. https://doi.org/10.3390/antiox14101169
APA StyleJiang, Y., Zheng, J., Zhang, Y., Liu, Y., Zeng, L., & Han, W. (2025). Discovery of Deer Antler-Derived Antioxidant Peptides Through Computational and Cell-Based Approaches. Antioxidants, 14(10), 1169. https://doi.org/10.3390/antiox14101169