Discovering potential anti-skin-aging peptides in collagen: computer-assisted rapid screening and structure–activity relationships

Abstract

The application of peptides as inhibitors of skin aging is a promising area of research. Previous researches have predominantly focused on extracting anti-aging peptides from the collagen of specific animals, while large-scale rapid screening and analysis of the structure–activity relationships of these peptides have been scarcely reported. In the present investigation, we developed a machine learning model for screening potential anti-skin-aging peptides (PASAPs), achieving a Matthews correlation coefficient (MCC) of 0.927 ± 0.044 and balanced accuracy (BACC) of 0.963 ± 0.022. These metrics surpassed those of the existing PeptideRanker model, which is widely used in bioactive peptide studies. Based on in silico screening, we identified and synthesized six novel PASAPs derived from tilapia collagen: KKHVWFGE, NGTPGAMGPR, PGAAGLKGDR, DGAPGPKGDR, TGPVGMPGAR, and GAPGGAGGVGEPGR. In vitro assays revealed that all six peptides exhibited significant inhibitory activity against aging-related enzymes, with the most pronounced effects on elastase and collagenase. A comprehensive analysis of the C-terminal amino acid residues indicated that the presence of arginine (R) at the C-terminus notably enhanced peptide binding to aging-related enzymes. This enhancement was attributed to an increased number of hydrogen bonds and stronger chemical interactions, which augmented the aging-related enzyme inhibitory activity of the peptides. In summary, this study proposed an effective strategy for discovering PASAPs from collagen and validated the machine learning model through experimental evidence. Structure–activity relationship insights can guide the synthesis of bioactive peptides and the selection of proteases for bioactive peptide production.

Keywords

Machine learning / Collagen / Anti-skin-aging activity / Molecular docking / Structure–activity relationship

References

[1] LimbertG, MasenMA, PondD, et al.. Biotribology of the ageing skin—Why we should care. Biotribology, 2019, 17: 75-90.

[2] ShinJ-W, KwonS-H, ChoiJ-Y, et al.. Molecular mechanisms of dermal aging and antiaging approaches. Int J Mol Sci, 2019, 2092126.

[3] Aguilar-ToalaJE, LiceagaAM. Identification of chia seed (Salvia hispanica L.) peptides with enzyme inhibition activity towards skin-aging enzymes. Amino Acids, 2020, 52(8): 1149-1159.

[4] TobinDJ. Introduction to skin aging. J Tissue Viabil, 2017, 26(1): 37-46.

[5] SunJ, ZhaoH, ShenCA, et al.. Tideglusib promotes wound healing in aged skin by activating PI3K/Akt pathway. Stem Cell Res Therapy, 2022, 131269.

[6] LuB, HanS, WangZ, et al.. Antioxidant and anti-aging potential of collagen peptide conjugated with ionic liquid. J Mol Liquids, 2024, 394123739.

[7] Ramos-E-silvaM, CelemLR, Ramos-E-silvaS, et al.. Anti-aging cosmetics: facts and controversies. Clin Dermatol, 2013, 31(6): 750-758.

[8] LorenciniM, BrohemCA, DieamantGC, et al.. Active ingredients against human epidermal aging. Ageing Res Rev, 2014, 15: 100-115.

[9] Aguilar-ToalaJE, Hernandez-MendozaA, Gonzalez-CordovaAF, et al.. Potential role of natural bioactive peptides for development of cosmeceutical skin products. Peptides, 2019, 122170170.

[10] WangW, ChenM, WuJ, et al.. Hypothermia protection effect of antifreeze peptides from pigskin collagen on freeze-dried Streptococcus thermophiles and its possible action mechanism. Lwt-Food Sci Technol, 2015, 63(2): 878-885.

[11] CaoC, XiaoZ, GeC, et al.. Animal by-products collagen and derived peptide, as important components of innovative sustainable food systems-a comprehensive review. Crit Rev Food Sci Nutr, 2022, 62(31): 8703-8727.

[12] OharaH, MatsumotoH, ItoK, et al.. Comparison of quantity and structures of hydroxyproline-containing peptides in human blood after oral ingestion of gelatin hydrolysates from different sources. J Agric Food Chem, 2007, 55(4): 1532-1535.

[13] LiuZ, LiY, SongH, et al.. Collagen peptides promote photoaging skin cell repair by activating the TGF-β/Smad pathway and depressing collagen degradation. Food Funct, 2019, 10(9): 6121-6134.

[14] DuZ, DingX, HsuW, et al.. pLM4ACE: a protein language model based predictor for antihypertensive peptide screening. Food Chem, 2024, 431137162.

[15] LuoF, FuY, MaL, et al.. Exploration of dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides from silkworm pupae (Bombyx mori) proteins based on in silico and in vitro assessments. J Agric Food Chem, 2022, 70(12): 3862-3871.

[16] HuangP, MiaoJ, LiJ, et al.. Novel antioxidant peptides from pearl shell meat hydrolysate and their antioxidant activity mechanism. Molecules, 2023, 282864.

[17] MooneyC, HaslamNJ, PollastriG, et al.. Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity. PLoS ONE, 2012, 710e45012.

[18] KunjulakshmiR, KumarA, Vinod KumarK, et al.. AagingBase: a comprehensive database of anti-aging peptides. Database J Biol Databases Curat, 2024, 2024baae016

[19] NorthcuttCG, JiangL, ChuangIL. Confident learning: estimating uncertainty in dataset labels. J Artif Intell Res, 2021, 70: 1373-1411.

[20] PedregosaF, VaroquauxG, GramfortA, et al.. Scikit-learn: machine learning in python. J Mach Learn Res, 2011, 12: 2825-2830

[21] YuanQ, ChenK, YuY, et al.. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Brief Bioinform, 2023, 241bbac630.

[22] MinkiewiczP, IwaniakA, DarewiczM. BIOPEP-UWM database of bioactive peptides: current opportunities. Int J Mol Sci, 2019, 20235978.

[23] RobertsPR, BurneyJD, BlackKW, et al.. Effect of chain length on absorption of biologically active peptides from the gastrointestinal tract. Digestion, 1999, 60(4): 332-337.

[24] RathoreAS, ChoudhuryS, AroraA, et al.. ToxinPred 3.0: an improved method for predicting the toxicity of peptides. Comput Biol Med, 2024, 179108926.

[25] ZhuY, LiuR, WuH, et al.. Progress of structure-activity relationship of bioactive peptides. China J Tradit Chin Med Pharm, 2012, 27(10): 2625-2628

[26] LinLIU, TingfangMA, YongqiangZHU, et al.. Structure of antibacterial peptides and their bioactivity. Pharm Biotechnol, 2008, 15(1): 64-67

[27] ZhuF, HeS, NiC, et al.. Study on the structure-activity relationship of rice immunopeptides based on molecular docking. Food Chem-X, 2024, 21101158.

[28] TrottO, OlsonAJ. Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 2010, 31(2): 455-461.

[29] MeekrathokP, ThongsomS, AunkhamA, et al.. Novel GH-20 β N -acetylglucosaminidase inhibitors: virtual screening, molecular docking, binding affinity, and anti-tumor activity. Int J Biol Macromol, 2020, 142: 503-512.

[30] Pham Thi NgocA, LeB, YangSH. Anti-aging skin and antioxidant assays of protein hydrolysates obtained from salted shrimp fermented with Salinivibrio cibaria BAO-01. J Appl Biol Chem, 2020, 63(3): 203-209.

[31] WangY, YuY, ZhangY. Therapeutic properties and enzyme inhibition potentials of some natural compounds on hyaluronidase, collagenase, and elastase with molecular docking studies. Macromol Res, 2024, 32(5): 475-491.

[32] NantaratN, MuellerM, LinW-C, et al.. Sesaminol diglucoside isolated from black sesame seed cake and its antioxidant, anti-collagenase and anti-hyaluronidase activities. Food Biosci, 2020, 36100628.

[33] LinZ, AkinH, RaoR, et al.. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 2023, 379(6637): 1123-1130.

[34] Rives A, Meier J, Sercu T, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(15).

[35] Lu AX, Zhang H, Ghassemi M, et al. Self-supervised contrastive learning of protein representations by mutual information maximization. bioRxiv, 2020.

[36] DuZ, DingX, XuY, et al.. UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Brief Bioinform, 2023, 243bbad135.

[37] HouY, ChitrakarB, MaoK, et al.. Bioactivity of collagen peptides derived from commercial animals: in silico investigation. Lwt-Food Sci Technol, 2023, 187115381.

[38] YangH, ZhangQ, ZhangB, et al.. Potential active marine peptides as anti-aging drugs or drug candidates. Mar Drugs, 2023, 213144.

[39] LinK, Zhang L-W, Han X, et al.. Yak milk casein as potential precursor of angiotensin I-converting enzyme inhibitory peptides based on in silico proteolysis. Food Chem, 2018, 254: 340-347.

[40] MoraL, ToldraF. Advanced enzymatic hydrolysis of food proteins for the production of bioactive peptides. Curr Opin Food Sci, 2023, 49100973.

[41] ZhuL, LiZ, MaM, et al.. Exploration of novel DPP-IV inhibitory peptides from discarded eggshell membrane: an integrated in silico and in vitro study. Food Biosci, 2024, 59104036.

[42] ZhaoS, MaS, ZhangY, et al.. Combining molecular docking and molecular dynamics simulation to discover four novel umami peptides from tuna skeletal myosin with sensory evaluation validation. Food Chem, 2024, 433137331.

[43] Nutho B, Tungmunnithum D. Exploring major flavonoid phytochemicals from Nelumbo nucifera Gaertn. as potential skin anti-aging agents: in silico and in vitro evaluations. Int J Mol Sci. 2023;24(23), 16571.

[44] Huang Y, Jiang D, Zhang L, et al. Discovery of MMP-1 inhibitors from dandelion using molecular simulation and bioactivity test. Curr Top Med Chem. 2025.

[45] XueW, LiuX, ZhaoW, et al.. Identification and molecular mechanism of novel tyrosinase inhibitory peptides from collagen. J Food Sci, 2022, 87(6): 2744-2756.

[46] Han Q-Y, Koyama T, Watabe S, et al.. Isolation and characterization of collagen and collagen peptides with hyaluronidase inhibition activity derived from the skin of marlin (Istiophoridae). Molecules, 2023, 282889.

[47] ChenH, YaoY, XieT, et al.. Identification of tyrosinase inhibitory peptides from sea cucumber (Apostichopus japonicus) collagen by in silico methods and study of their molecular Mechanism. Curr Protein Pept Sci, 2023, 24(9): 758-766.

[48] CaoC, XiaoZ, TongH, et al.. Oral intake of chicken bone collagen peptides anti-skin aging in mice by regulating collagen degradation and synthesis, inhibiting inflammation and activating lysosomes. Nutrients, 2022, 1481622.

[49] WangL, QuL, HeB. Preparation, identification and molecular docking of two novel anti-aging peptides from perilla seed. Heliyon, 2024, 1013e33604.

[50] MjalliFS, Al-AzzawiM. Aliphatic amino acids as possible hydrogen bond donors for preparing eutectic solvents. J Mol Liquids, 2021, 330115637.

[51] HayashiK, IkeuchiT, MorishitaR, et al.. The roles of histidine and tyrosine residues in the active site of collagenase in Grimontia hollisae. J Biochem, 2020, 168(4): 385-392.

[52] LeeGY, ChoBO, ShinJY, et al.. Tyrosinase inhibitory components from the seeds of Cassia tora. Arch Pharmacal Res, 2018, 41(5): 490-496.

[53] YangY, SunX, NiH, et al.. Identification and characterization of the tyrosinase inhibitory activity of Caffeine from Camellia pollen. J Agric Food Chem, 2019, 67(46): 12741-12751.

[54] XiangZ, XueQ, GaoP, et al.. Antioxidant peptides from edible aquatic animals: preparation method, mechanism of action, and structure-activity relationships. Food Chem, 2023, 404134701.

[55] BeldowskiP, PrzybylekM, BeldowskiD, et al.. Collagen type II-hyaluronan interactions - the effect of proline hydroxylation: a molecular dynamics study. J Mater Chem B, 2022, 10(46): 9713-9723.

[56] HuangS-Y, ZouX. Advances and challenges in protein–ligand docking. Int J Mol Sci, 2010, 11(8): 3016-3034.

[57] PesaresiA. Mixed and non-competitive enzyme inhibition: underlying mechanisms and mechanistic irrelevance of the formal two-site model. J Enzyme Inhibit Med Chem, 2023, 3812245168.

[58] FlaglerMJ, TamuraM, LaughlinT, et al.. Combinations of peptides synergistically activate the regenerative capacity of skin cells in vitro. Int J Cosmet Sci, 2021, 43(5): 518-529.

[59] OharaH, IchikawaS, MatsumotoH, et al.. Collagen-derived dipeptide, proline-hydroxyproline, stimulates cell proliferation and hyaluronic acid synthesis in cultured human dermal fibroblasts. J Dermatol, 2010, 37(4): 330-338.

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