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Modeling of the process of enzymatic hydrolysis of plant proteins in silico and prediction of the biological activity of the resulting peptides

https://doi.org/10.21323/2618-9771-2025-8-4-472-478

Abstract

The traditional approach to obtaining, identifying, and confirming the biological activity of peptides is laborious and timeconsuming. The development of bioinformatics and computer modeling made it possible to carry out a preliminary theoretical assessment of the potential biological activity of peptides. The aim of the study was to carry out theoretical enzymatic hydrolysis in silico of chickpea, rapeseed and hemp proteins, as well as to predict the profile of potential biological activity of the resulting peptides using bioinformatics tools. As a result of the search for the initial amino acid sequences of chickpea, rapeseed and hemp proteins in the UniProtKB database using the keywords “Cicer arietinum”, “Brassica napus”, “Cannabis sativa”, as well as the origin of the protein — “Storage protein”, 5 isoforms of legumin, 3 isoforms of vicilin and 2 isoforms of provicilin were found in chickpea proteins; rapeseed proteins contained 6 isoforms of cruciferin protein and 7 isoforms of napine; hemp proteins contained 3 isoforms of edestin-1 and edestin-2, 2 isoforms of edestin-3 and 1 isoform of albumin. After hydrolysis using the tools of the BIOPEP-UWM database, 10,131 amino acid sequences of chickpea proteins, 7,206 amino acid sequences of rapeseed proteins and 8,479 amino acid sequences of hemp proteins were obtained. As a result of the classification of the obtained peptides according to the predicted value of their biological activity using PeptideRanker, as well as after predicting toxicity, bitterness and allergenicity, 35 biologically active peptides (BAPS) were identified from chickpea proteins, 21 from rapeseed proteins and 22 from hemp proteins. For chickpea proteins, 29 potential ACE inhibitors, 27 DPP IV inhibitors, 6 oncostatic, 4 antioxidant, 4 antifungal and 3 antihypertensive peptides were predicted. For rapeseed, 22 potential DPP IV inhibitors and 20 ACE inhibitors, 5 antifungal peptides, 3 peptides with potential antioxidant effect, 3 with antithrombotic properties, 2 antihypertensive peptides, 2 with oncostatic properties and 1 with antibacterial activity were determined. Potential ACE-inhibiting activity was determined for 16 hemp peptides, 15 are DPP IV inhibitors, 7 have antifungal activity, 5 have antioxidant and oncostatic effects, 4 have antihypertensive properties, 1 has antituberculous effect. In the future, further in vitro and in vivo studies are needed to confirm biological activity, as there is a potential discrepancy between the results of in silico modeling of hydrolysis and prediction of biological activity and the data from experimental studies.

About the Authors

M. Gharaviri
Russian Biotechnological University
Russian Federation

Mahmood Gharaviri, Postgraduate, Department «Biotechnology and Bioorganic Synthesis»

11, Volokolamsk highway, 125080, Moscow



I. A. Degtyarev
Russian Biotechnological University
Russian Federation

Ivan A. Degtyarev, Postgraduate, Department «Biotechnology and Bioorganic Synthesis»

11, Volokolamsk highway, 125080, Moscow



D. I. Aleksanochkin
Russian Biotechnological University
Russian Federation

Denis I. Aleksanochkin, Postgraduate, Department «Biotechnology and Bioorganic Synthesis»

11, Volokolamsk highway, 125080, Moscow



I. A. Fomenko
Russian Biotechnological University
Russian Federation

Ivan A. Fomenko, Candidate  of  Technical  Sciences, Docent, Department «Biotechnology and Bioorganic Synthesis»

11, Volokolamsk highway, 125080, Moscow



N. G. Mashentseva
Russian Biotechnological University
Russian Federation

Natalya G. Mashentseva, Doctor of Technical Sciences, Professor of the Russian Academy of Sciences, Professor, Department «Biotechnology and Bioorganic Synthesis»

11, Volokolamsk highway, 125080, Moscow



References

1. Du, Z., Comer, J., Li, Y. (2023). Bioinformatics approaches to discovering foodderived bioactive peptides: Reviews and perspectives. TrAC Trends in Analytical Chemistry, 162, Article 117051. https://doi.org/10.1016/j.trac.2023.117051

2. Du, C., Gong, H., Zhao, H., Zhang, L., Wang, P. (2024). Recent progress in the preparation of bioactive peptides using simulated gastrointestinal digestion processes. Food Chemistry, 453, Article 139587. https://doi.org/10.1016/j.foodchem.2024.139587

3. Rivero-Pino, F., Espejo-Carpio, F. J., Guadix, E. M. (2021). Unravelling the α-glucosidase inhibitory properties of chickpea protein by enzymatic hydrolysis and in silico analysis. Food Bioscience, 44(Part 4), Article 101328. https://doi.org/10.1016/j.fbio.2021.101328

4. Toldrá, F., Gallego, M., Reig, M., Aristoy, M.-C., Mora, L. (2020). Recent progress in enzymatic release of peptides in foods of animal origin and assessment of bioactivity. Journal of Agricultural and Food Chemistry, 68(46), 12842–12855. https://doi.org/10.1021/acs.jafc.9b08297

5. Cermeño, M., Bascón, C., Amigo-Benavent, M., Felix, M., FitzGerald, R. J. (2022). Identification of peptides from edible silkworm pupae (Bombyx mori) protein hydrolysates with antioxidant activity. Journal of Functional Foods, 92, Article 105052. https://doi.org/10.1016/j.jff.2022.105052

6. Song, Y., Ju, J., Song, J., Wang, S., Cao, T., Lio, Z. et al. (2021). The antihypertensive effect and mechanisms of bioactive peptides from Ruditapes philippinarum fermented with Bacillus natto in spontaneously hypertensive rats. Journal of Functional Foods, 79, Article 104411. https://doi.org/10.1016/j.jff.2021.104411

7. Villaró-Cos, S., Lafarga, T. (2023). Online tools to support teaching and training activities in chemical engineering: Enzymatic proteolysis. Frontiers in Education, 8, Article 1290287. https://doi.org/10.3389/feduc.2023.1290287

8. Agyei, D., Tsopmo, A., Udenigwe, C.C. (2018). Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides. Analytical and Bioanalytical Chemistry, 410, 3463–3472. https://doi.org/10.1007/s00216-018-0974-1

9. Guo, H., Hao, Y., Yang, X., Ren, G., Richer, A. (2023). Exploration on bioactive properties of quinoa protein hydrolysate and peptides: A review. Critical Reviews in Food Science and Nutrition, 63(16), 2896–2909. https://doi.org/10.1080/10408398.2021.1982860

10. Mooney, C., Haslam, N. J., Pollastri, G., Shields, D. C. (2012). Towards the improved discovery and design of functional peptides: Common features of diverse classes permit generalized prediction of bioactivity. PLoS ONE, 7(10), Article e45012. https://doi.org/10.1371/journal.pone.0045012

11. Gallego, M., Toldrá, F., Mora, L. (2022). Quantification and in silico analysis of taste dipeptides generated during dry-cured ham processing. Food Chemistry, 370, Article 130977. https://doi.org/10.1016/j.foodchem.2021.130977

12. Wiener, A., Shudler, M., Levit, A., Niv, M. Y. (2012). BitterDB: A database of bitter compounds. Nucleic Acids Research, 40(D1), D413-D419. https://doi.org/10.1093/nar/gkr755

13. Minkiewicz, P., Iwaniak, A., Darewicz, M. (2019). BIOPEP-UWM database of bioactive peptides: Current opportunities. International Journal of Molecular Sciences, 20(23), Article 5978. https://doi.org/10.3390/ijms20235978

14. Sharma, N., Patiyal, S., Dhall, A., Pande, A., Arora, C., Raghava, G. P. S. (2021). AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes. Briefings in Bioinformatics, 22(4), Article bbaa294. https://doi.org/10.1093/bib/bbaa294

15. Lata, S., Mishra, N. K., Raghava, G. P. S. (2010). AntiBP2: Improved version of antibacterial peptide prediction. BMC Bioinformatics, 11(Suppl 1), Article S19. https://doi.org/10.1186/1471-2105-11-S1-S19

16. Rauf, A., Kiran, A., Hassan, M. T., Mahmood, S., Mustafa, G., Jeon, M. (2021). Boosted prediction of antihypertensive peptides using deep learning. Applied Sciences, 11(5), Article 2316. https://doi.org/10.3390/app11052316

17. Manavalan, B., Basith, S., Shin, T. H., Wei, L., Lee, G. (2019). AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees. Computational and Structural Biotechnology Journal, 17, 972–981. https://doi.org/10.1016/j.csbj.2019.06.024

18. Agrawal, P., Bhagat, В., Mahalwal, M., Sharma, N., Raghava, G. P. S. (2021). AntiCP 2.0: An updated model for predicting anticancer peptides. Briefings in Bioinformatics, 22(3), Article bbaa153. https://doi.org/10.1093/bib/bbaa153

19. Moretta, A., Scieuzo, C., Petrone, A. M., Salvia, R., Manniello, M.D., Falabella, P. (2022). Tools in the era of multidrug resistance in bacteria: Applications for new antimicrobial peptides discovery. Current Pharmaceutical Design, 28(35), 2856–2866. https://doi.org/10.2174/1381612828666220817163339

20. Axentii, M., Codină, G. G. (2024). Exploring the nutritional potential and functionality of hemp and rapeseed proteins: A review on unveiling anti-nutritional factors, bioactive compounds, and functional attributes. Plants, 13(9), Article 1195. https://doi.org/10.3390/plants13091195

21. Kumar, N., Hong, S., Zhu, Y., Garay, A., Yang, J., Henderson, D. et al. (2025). Comprehensive review of chickpea (Cicer arietinum): Nutritional significance, health benefits, techno-functionalities, and food applications. Comprehensive Reviews in Food Science and Food Safety, 24(2), Article e70152. https://doi.org/10.1111/1541-4337.70152

22. Grasso, N., Lynch, K. M., Arendt, E. K., O’Mahony, J. A. (2022). Chickpea protein ingredients: A review of composition, functionality, and applications. Comprehensive Reviews in Food Science and Food Safety, 21(1), 435–452. https://doi.org/10.1111/1541-4337.12878

23. Di Francesco, A., De Santis, M. A., Lanzoni, A., Pittalà, M. G. G., Saletti, R., Flagella, Z. et al. (2024). Mass spectrometry characterization of the SDS-PAGE protein profile of legumins and vicilins from chickpea seed. Foods, 13(6), Article 887. https://doi.org/10.3390/foods13060887

24. Shen, P., Yang, J., Nikiforidis, C. V., Mocking-Bode, H. C. M., Sagis, L. M. C. (2023). Cruciferin versus napin — Air-water interface and foam stabilizing properties of rapeseed storage proteins. Food Hydrocolloids, 136, Article 108300. https://doi.org/10.1016/j.foodhyd.2022.108300

25. Sun, X., Sun, Y., Li, Y., Wu, Q., Wang, L. (2021). Identification and characterization of the seed storage proteins and related genes of Cannabis sativa L. Frontiers in Nutrition, 8, Article 678421. https://doi.org/10.3389/fnut.2021.678421

26. Al Musaimi, O., Lombardi, L., Williams, D. R., Albericio, F. (2022). Strategies for improving peptide stability and delivery. Pharmaceuticals, 15(10), Article 1283. https://doi.org/10.3390/ph15101283

27. Wang, L., Wang, N., Zhang, W., Cheng, X., Yan, Z., Shao, G. et al. (2022). Therapeutic peptides: Current applications and future directions. Signal Transduction and Targeted Therapy, 7(1), Article 48. https://doi.org/10.1038/s41392-022-00904-4

28. Boschin, G., Scigliuolo, G. M., Resta, D., Arnoldi, A. (2014). ACE inhibitory activity of enzymatic protein hydrolysates from lupin and other legumes. Food Chemistry, 145, 34–40. https://doi.org/10.1016/j.foodchem.2013.07.076

29. Wang, S., Mouming Zhao, M., Hongbing Fan, H., Wu, J. (2022). Emerging proteins as precursors of bioactive peptides/hydrolysates with health benefits. Current Opinion in Food Science, 48, Article 100914. https://doi.org/10.1016/j.cofs.2022.100914

30. Hernandez, L. M. R., de Mejia, E. G. (2019). Enzymatic production, bioactivity, and bitterness of chickpea (Cicer arietinum) peptides. Comprehensive Reviews in Food Science and Food Safety, 18(6), 1913–1946. https://doi.org/10.1111/1541-4337.12504

31. Rivero-Pino, F., Millan-Linares, M. C., Montserrat-De-La-Paz, S. (2023). Strengths and limitations of in silico tools to assess physicochemical properties, bioactivity, and bioavailability of food-derived peptides. Trends in Food Science and Technology, 138, 433–440. https://doi.org/10.1016/j.tifs.2023.06.023


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For citations:


Gharaviri M., Degtyarev I.A., Aleksanochkin D.I., Fomenko I.A., Mashentseva N.G. Modeling of the process of enzymatic hydrolysis of plant proteins in silico and prediction of the biological activity of the resulting peptides. Food systems. 2025;8(4):472-478. (In Russ.) https://doi.org/10.21323/2618-9771-2025-8-4-472-478

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