Studies have been conducted to predict the physico-chemical characteristics and indicators of medicinal chemistry of a hypocholesterolenic food peptide responsible for its distribution, bioavailability and metabolism. The synthesized new hypocholesterolenic peptide with the amino acids sequence of CHAECGAACKEFCLEG was used as an object. The predicted molecular weight of the peptide is 1 669.64 Da. The actual value of the synthesized peptide is 1 670 Da, which indicates that the synthesized peptide meets its predicted characteristics. The peptide is lipophilic, has optimal pharmacokinetic and safety profiles. The index of pKa is 1,382 units and lower than the base (5,199 units) by 3.76 times, which indicates a high bioavailability of the peptide. Analysis of molecular descriptors: the proportion of carbon atoms sp3 (Fsp3) is 0.771 units with an optimal value of more than 0.43 units, which indicates weak colloidal aggregation, high bioavailability. The studied peptide is well soluble in biological fluids, is in an unbound state, does not form colloidal aggregation, and is able to reach target cells, which indicates its bioavailability and high biological efficiency.

Tikhonov Sergey L., Tikhonova Natalya V., Timofeeva Mariya S., Shikhalev Sergey V.

1. Gil-Ramirez A., Morales D., Soler-Rivas C. Molecular actions of hypocholesterolaemic compounds from edible mushrooms. Food and Function, 2018;9;1:63–69. https://doi.org/10.1039/c7fo00835j.
2. Gaponova L. V., Loginova T. T., Pershikova A. V., Reshetnik E. I. Soybean in medical and preventive and children's nutrition. Molochnaya promyshlennost', 1999;5:25–27. EDN NVBNEP (in Russ.).
3. Ferreira E. S., Silva M. A., Demonte A., Neves V. A. Soy β-conglycinin (7S globulin) reduces plasma and liver cholesterol in rats fed hypercholesterolemic diet. Journal of Medicinal Food, 2011;14;1–2:94–100. https://doi.org/10.1089/jmf.2009.0204.
4. Duranti M., Lovati M. R., Dani V., Barbiroli A., Scafaroni A., Castiglioni S. [et al.]. The alpha' subunit from soybean 7S globulin lowers plasma lipids and upregulates liver beta-VLDL receptors in rats fed a hypercholesterolemic diet. The Journal of Nutrition, 2004;134;6:1334–1339. https://doi.org/10.1093/jn/134.6.1334.
5. Pak V. V., Koo M. S., Kasymova T. D., Kwon D. Y. Isolation and identification of peptides from soy 11S-globulin with hypocholesterolemic activity. Chemistry of Natural Compounds, 2005;41:710–714. https://doi.org/10.1007/s10600-006-0017-6.
6. Zhong F., Zhang X., Ma J., Shoemaker C. F. Fractionation and identification of a novel hypocholesterolemic peptide derived from soy protein Alcalase hydrolysates. Food Research International, 2007;40;6:756–762. https://doi.org/10.1016/j.foodres.2007.01.005.
7. Lammi C., Zanoni C., Ferruza S., Ranaldi G., Sambuy Y., Arnoldi A. Hypocholesterolaemic activity of lupin peptides: Investigation on the crosstalk between human enterocytes and hepatocytes using a co-culture system including Caco-2 and HepG2 cell. Nutrients, 2016;8;7:437. https://doi.org/10.3390/nu8070437.
8. Lule V. K., Garg S., Pophaly S. D., Tomar S. K. Potential health benefits of lunasin: A multifaceted soy-derived bioactive peptide. Journal of Food Science, 2015;80;3:R485–R494. https://doi.org/10.1111/1750-3841.12786.
9. Xue Z., Hou X., Yu W., Wen H., Zhang Q., Li D. [et al.]. Lipid metabolism potential and mechanism of CPe-III from chickpea (Cicer arietinum L.). Food Research International, 2018;104:126–133. https://doi.org/10.1016/j.foodres.2017.03.016.
10. Reshetnik E. I., Sharipova T. V., Maksimyuk V. A. Possibility of application chick-pea flour in the production of meat-vegetable prepared foods for elderly nutrition. Dal'nevostochnyi agrarnyi vestnik, 2014;1(29):48–51. EDN TMWSRL (in Russ.).
11. Gomes M. J. C., Lima S. L. S., Alves N. E. G., Assis A., Moreira M. E. C., Toledo R. C. L. Common bean protein hydrolysate modulates lipid metabolism and prevents endothelial dysfunction in BALB/c mice fed an atherogenic diet. Nutrition, Metabolism and Cardiovascular Diseases, 2020;30;1:141–150. https://doi.org/10.1016/j.numecd.2019.07.020.
12. Marques M. R., Fontanari G. G., Pimenta D. C., Soares-Freitas R. M., Areas J. A. G. Proteolytic hydrolysis of cowpea proteins is able to release peptides with hypocholesterolemic activity. Food Research International, 2015;77;1:43–48. https://doi.org/10.1016/j.foodres.2015.04.020.
13. Bautista-Exposito S., Peñas E., Dueñas M., Silvan J. M., Frias J., Martinez-Villaluenga C. Individual contributions of Savinase and Lactobacillus plantarum to lentil functionalization during alkaline pH-controlled fermentation. Food Chemistry, 2018;257:341–349. https://doi.org/10.1016/j.foodchem.2018.03.044.
14. Moreno-Valdespino C. A., Luna-Vital D., Camacho-Ruiz R. M., Mojica L. Bioactive proteins and phytochemicals from legumes: Mechanisms of action preventing obesity and type-2 diabetes // Food Research International, 2020;130:108905. https://doi.org/10.1016/j.foodres.2019.108905.
15. Bhal S. K., Kassam K., Peirson I. G., Pearl G. M. The rule of five revisited: applying logD in place of logP in drug-likeness filters. Molecular Pharmaceutics, 2007;4;4:556–560. https://doi.org/10.1021/mp0700209.
16. Arnott J. A., Planey S. L. The influence of lipophilicity in drug discovery and design. Expert Opinion on Drug Discovery, 2012;7;10:863–875. https://doi.org/10.1517/17460441.2012.714363.
17. Wang Y., Xiong J., Xiao F., Zhang W., Cheng K., Rao J. [et al.]. LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP. Journal of Cheminformatics, 2023;15;1:76. https://doi.org/10.1186/s13321-023-00754-4.
18. Bergazin T. D., Tielker N., Zhang Y., Mao J., Gunner M. R., Francisco K. Evaluation of logP, pK(a), and logD predictions from the SAMPL7 blind challenge. Journal of Computer-Aided Molecular Design, 2021;35:771–802. https://doi.org/10.1007/s10822-021-00397-3.
19. Lukashina N., Alenicheva A., Vlasova E., Kondiukov A., Khakimova A., Magerramov E. Lipophilicity prediction with multitask learning and molecular substructures representation. In.: Machine Learning for Molecules at NeurIPS'2020, 2020. https://doi.org/10.4855/arXiv.2011.1211733.
20. Wieder O., Kuenemann M., Wieder M., Seidel T., Meyer C., Bryant S. D. [et al.]. Improved lipophilicity and aqueous solubility prediction with composite graph neural networks. Molecules, 2021;26;20:6185. https://doi.org/10.3390/molecules26206185.
21. Wu J., Wan Y., Wu Z., Zhang S., Cao D., Hsieh C. Y. [et al.]. MF-SuP-pKa: Multifidelity modeling with subgraph pooling mechanism for pKa prediction. Acta Pharmaceutica Sinica B, 2023;13;6:2572–2584. https://doi.org/10.1016/j.apsb.2022.11.010.
22. Duan D., Torosyan H., Elnatan D., McLaughlin C. K., Logie J., Shoichet M. S. [et al.]. Internal structureand preferential protein binding of colloidal aggregates. ACS Chemical Biology, 2017;12;1:282−290. https://doi.org/10.1021/acschembio.6b00791.
23. Thorne N., Inglese J., Auld D. S. Illuminating insights into firefly luciferase and other bioluminescent reporters used in chemical biology. Chemistry and Biology, 2010;17;6:646–657. https://doi.org/10.1016/j.chembiol.2010.05.012.
24. Mengqi P., Zifu Zh., Song Ch., Reshetnik E. I., Gribanova S. L., Chun L. [et al.]. Physicochemical properties and volatile components of pea flour fermented by Lactobacillus rhamnosus L08. Food Bioscience, 2022;46:101590. https://doi.org/10.1016/j.fbio.2022.101590.