Аннотация

Методы биоинформатического анализа – вспомогательный инструмент в проведении предварительного этапа исследований процесса биокаталитической конверсии белков с прогнозируемым высвобождением биологически активных пептидов. Однако существует ряд факторов, не учитывающихся в современных стратегиях при проектировании биологически активных пептидов, что препятствует полномасштабному прогнозированию их биологических свойств. Это обуславливает актуальность выбранной цели исследования – разработку гибридной стратегии биоинформатического моделирования для изучения биологически активных пептидов молочного белка с учетом ранжирования ключевых критериев на основе высокопроизводительных алгоритмов протеомных баз данных.
Объектом исследования является научная литература, касающаяся методов in silico биологически активных пептидов. Применялись современные таксонометрические методы поиска информации с использованием баз данных РИНЦ, Scopus и Web of Science.
Сформирован и поэтапно описан оптимальный алгоритм гибридной стратегии in silico изучения биологически активных пептидов молочного белка с учетом оценки безопасности всех продуктов гидролиза, их физико-химических и технологических свойств. Алгоритм стратегии сформирован исходя из аналитических данных о белковом профиле, аминокислотной последовательности белков, входящих в состав сырья с учетом их полиморфизма, и последующей идентификации биоактивных аминокислотных сайтов в структуре белка. В алгоритм включен подбор оптимальных ферментных препаратов и моделирование гидролиза с оценкой биоактивности пептидов по протеомным базам данных.
Предложенная стратегия in silico позволит на предварительном этапе проведения гидролиза белка научно прогнозировать направленное высвобождение стабильных пептидных комплексов биологически активных пептидов с доказанными биоактивностью, безопасностью и сенсорными характеристиками. Гибридный алгоритм будет способствовать аккумулированию необходимых первичных данных для сокращения временных и финансовых затрат на проведение реальных экспериментов.

Ключевые слова

Молочные белки, пептиды, база данных, биоинформатика, in silico

ФИНАНСИРОВАНИЕ

Исследование выполнено за счет гранта Российского научного фонда (РНФ) № 21-76-00044.

СПИСОК ЛИТЕРАТУРЫ

1. Karami Z, Akbari-Adergani B. Bioactive food derived peptides: a review on correlation between structure of bioactive peptides and their functional properties. Journal of Food Science and Technology. 2019;56(2):535–547. https://doi.org/10.1007/s13197-018-3549-4
2. Hafeez Z, Cakir-Kiefer C, Roux E, Perrin C, Miclo L, Dary-Mourot A. Strategies of producing bioactive peptides from milk proteins to functionalize fermented milk products. Food Research International. 2014;63:71–80. https://doi.org/10.1016/j.foodres.2014.06.002
3. Kamali Alamdari E, Ehsani MR. Antimicrobial peptides derived from milk: A review. Journal of Food Biosciences and Technology. 2017;7(1):49–56.
4. Ryazantseva KA, Agarkova EYu, Fedotova OB. Continuous hydrolysis of milk proteins in membrane reactors of various configurations. Foods and Raw Materials. 2021;9(2):271–281. https://doi.org/10.21603/2308-4057-2021-2-271-281
5. Kruchinin AG, Savinova OS, Glazunova OA, Moiseenko KV, Agarkova EYu, Fedorova TV. Hypotensive and hepatoprotective properties of the polysaccharide-stabilized foaming composition containing hydrolysate of whey proteins. Nutrients. 2021;13(3). https://doi.org/10.3390/nu13031031
6. Peighambardoust SH, Karami Z, Pateiro M, Lorenzo JM. A review on health-promoting, biological, and functional aspects of bioactive peptides in food applications. Biomolecules. 2021;11(5). https://doi.org/10.3390/biom11050631
7. Bielecka M, Cichosz G, Czeczot H. Antioxidant, antimicrobial and anticarcinogenic activities of bovine milk proteins and their hydrolysates – A review. International Dairy Journal. 2021;127. https://doi.org/10.1016/j.idairyj.2021.105208
8. Huang S, Gong Y, Li Y, Ruan S, Roknul Azam SM, Duan Y, et al. Preparation of ACE-inhibitory peptides from milk protein in continuous enzyme membrane reactor with gradient dilution feeding substrate. Process Biochemistry. 2020;92:130–137. https://doi.org/10.1016/j.procbio.2020.02.030
9. Chalamaiah M, Yu W, Wu J. Immunomodulatory and anticancer protein hydrolysates (peptides) from food proteins: A review. Food Chemistry. 2018;245:205–222. https://doi.org/10.1016/j.foodchem.2017.10.087
10. Giacometti J, Buretić-Tomljanović A. Peptidomics as a tool for characterizing bioactive milk peptides. Food Chemistry. 2017;230:91–98. https://doi.org/10.1016/j.foodchem.2017.03.016
11. Hayes M, Stanton C, Fitzgerald GF, Ross RP. Putting microbes to work: Dairy fermentation, cell factories and bioactive peptides. Part II: Bioactive peptide functions. Biotechnology Journal. 2007;2(4):435–449. https://doi.org/10.1002/biot.200700045
12. Etemadian Y, Ghaemi V, Shaviklo AR, Pourashouri P, Sadeghi Mahoonak AR, Rafipour F. Development of animal/plant-based protein hydrolysate and its application in food, feed and nutraceutical industries: State of the art. Journal of Cleaner Production. 2021;278. https://doi.org/10.1016/j.jclepro.2020.123219
13. Sánchez A, Vázquez A. Bioactive peptides: A review. Food Quality and Safety. 2017;1(1):29–46.
14. Nongonierma AB, FitzGerald RJ. Strategies for the discovery and identification of food protein-derived biologically active peptides. Trends in Food Science and Technology. 2017;69:289–305. https://doi.org/10.1016/j.tifs.2017.03.003
15. Yu Z, Chen Y, Zhao W, Zheng F, Ding L, Liu J. Novel ACE inhibitory tripeptides from ovotransferrin using bioinformatics and peptidomics approaches. Scientific Reports. 2019;9(1). https://doi.org/10.1038/s41598-019-53964-y
16. Tu M, Cheng S, Lu W, Du M. Advancement and prospects of bioinformatics analysis for studying bioactive peptides from food-derived protein: Sequence, structure, and functions. TrAC – Trends in Analytical Chemistry. 2018;105:7–17. https://doi.org/10.1016/j.trac.2018.04.005
17. Barati M, Javanmardi F, Jabbari M, Mokari-Yamchi A, Farahmand F, Eş I, et al. An in silico model to predict and estimate digestion-resistant and bioactive peptide content of dairy products: A primarily study of a time-saving and affordable method for practical research purposes. LWT. 2020;130. https://doi.org/10.1016/j.lwt.2020.109616
18. Panyayai T, Ngamphiw C, Tongsima S, Mhuantong W, Limsripraphan W, Choowongkomon K, et al. FeptideDB: A web application for new bioactive peptides from food protein. Heliyon. 2019;5(7). https://doi.org/10.1016/j.heliyon.2019.e02076
19. Agarkova EYu, Kruchinin AG. Enzymatic conversion as a method of producing biologically active peptides. Vestnik of MSTU. 2018;21(3):412–419. (In Russ.). https://doi.org/10.21443/1560-9278-2018-21-3-412-419
20. FitzGerald RJ, Cermeño M, Khalesi M, Kleekayai T, Amigo-Benavent M. Application of in silico approaches for the generation of milk protein-derived bioactive peptides. Journal of Functional Foods. 2020;64. https://doi.org/10.1016/j.jff.2019.103636
21. Agyei D, Tsopmo A, Udenigwe C. Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides. Analytical and Bioanalytical Chemistry. 2018;410(15):3463–3472. https://doi.org/10.1007/s00216-018-0974-1
22. Ryskaliyeva A, Henry C, Miranda G, Faye B, Konuspayeva G, Martin P. Alternative splicing events expand molecular diversity of camel CSN1S2 increasing its ability to generate potentially bioactive peptides. Scientific Reports. 2019;9(1). https://doi.org/10.1038/s41598-019-41649-5
23. Ryazantzeva KA, Agarkova EYu. Using in silico methods to obtain bioactive peptides of whey. Food Industry. 2021;(5):32–35. (In Russ.). https://doi.org/10.52653/PPI.2021.5.5.007
24. Peredo-Lovillo A, Hernández-Mendoza A, Vallejo-Cordoba B, Romero-Luna HE. Conventional and in silico approaches to select promising food-derived bioactive peptides: A review. Food Chemistry: X. 2021;13. https://doi.org/10.1016/j.fochx.2021.100183
25. Lafarga T, O'Connor P, Hayes M. Identification of novel dipeptidyl peptidase-IV and angiotensin-I-converting enzyme inhibitory peptides from meat proteins using in silico analysis. Peptides. 2014;59:53–62. https://doi.org/10.1016/j.peptides.2014.07.005
26. Kruchinin AG, Turovskaya SN, Illarionova EE, Bigaeva AV. Evaluation of the effect of κ-casein gene polymorphism in milk powder on the technological properties of acid-induced milk gels. Food Processing: Techniques and Technology. 2021;51(1):53–66. (In Russ.). https://doi.org/10.21603/2074-9414-2021-1-53-66
27. Bateman A, Martin MJ, O'Donovan C, Magrane M, Apweiler R, Alpi E, et al. UniProt: a hub for protein information. Nucleic Acids Research. 2015;43(D1):D204–D212. https://doi.org/10.1093/nar/gku989
28. Nongonierma AB, FitzGerald RJ. Enhancing bioactive peptide release and identification using targeted enzymatic hydrolysis of milk proteins. Analytical and Bioanalytical Chemistry. 2018;410(15):3407–3423. https://doi.org/10.1007/s00216-017-0793-9
29. Iwaniak A, Minkiewicz P, Darewicz M, Sieniawski K, Starowicz P. BIOPEP database of sensory peptides and amino acids. Food Research International. 2016;85:155–161. https://doi.org/10.1016/j.foodres.2016.04.031
30. Dyachenko EA, Slugina MA. Intraspecific variability of the Sus1 sucrose synthase gene in Pisum sativui accessions. Vavilov Journal of Genetics and Breeding. 2018;22(1):108–114. (In Russ.). https://doi.org/10.18699/VJ18.338
31. Kruchinin AG, Turovskaya SN, Illarionova EE, Bigaeva AV. Molecular genetic modifications of к-casein. News of Institutes of Higher Education. Food Technology. 2020;376(4):12–16. (In Russ.). https://doi.org/10.26297/0579-3009.2020.4.3
32. Asledottir T, Le TT, Petrat-Melin B, Devold TG, Larsen LB, Vegarud GE. Identification of bioactive peptides and quantification of β-casomorphin-7 from bovine β-casein A1, A2 and I after ex vivo gastrointestinal digestion. International Dairy Journal. 2017;71:98–106. https://doi.org/10.1016/J.IDAIRYJ.2017.03.008
33. Gilmanov KhKh, Semipyatnyi VK, Bigaeva AV, Vafin RR, Turovskaya SN. New determination method for the ratio of the relative proportions of ϰ-casein alleles in milk powder. Food Processing: Techniques and Technology. 2020;50(3):525–535. (In Russ.). https://doi.org/10.21603/2074-9414-2020-3-525-535
34. Nielsen SD, Beverly RL, Qu Y, Dallas DC. Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization. Food Chemistry. 2017;232:673–682. https://doi.org/10.1016/j.foodchem.2017.04.056
35. Nebbia S, Lamberti C, Lo Bianco G, Cirrincione S, Laroute V, Cocaign-Bousquet M, et al. Antimicrobial potential of food lactic acid bacteria: Bioactive peptide decrypting from caseins and bacteriocin production. Microorganisms. 2021;9(1). https://doi.org/10.3390/microorganisms9010065
36. Nielsen SD, Beverly RL, Underwood MA, Dallas DC. Differences and similarities in the peptide profile of preterm and term mother’s milk, and preterm and term infant gastric samples. Nutrients. 2020;12(9). https://doi.org/10.3390/nu12092825
37. Pa’ee KF, Razali N, Sarbini SR, Ramonaran Nair SN, Yong Tau Len K, Abd-Talib N. The production of collagen type I hydrolyzate derived from tilapia (Oreochromis sp.) skin using thermoase PC10F and its in silico analysis. Food Biotechnology. 2021;35(1):1–21. https://doi.org/10.1080/08905436.2020.1869040
38. Minkiewicz P, Iwaniak A, Darewicz M. BIOPEP-UWM database of bioactive peptides: Current opportunities. International Journal of Molecular Sciences. 2019;20(23). https://doi.org/10.3390/ijms20235978
39. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10. https://doi.org/10.1186/1471-2105-10-421
40. González-Pech RA, Stephens TG, Chan CX. Commonly misunderstood parameters of NCBI BLAST and important considerations for users. Bioinformatics. 2019;35(15):2697–2698. https://doi.org/10.1093/bioinformatics/bty1018
41. Wang Z, Wang G. APD: The antimicrobial peptide database. Nucleic Acids Research. 2004;32:D590–D592.
42. Li Q, Zhang C, Chen H, Xue J, Guo X, Liang M, et al. BioPepDB: an integrated data platform for food-derived bioactive peptides. International Journal of Food Sciences and Nutrition. 2018;69(8):963–968. https://doi.org/10.1080/09637486.2018.1446916
43. Iwaniak A, Minkiewicz P, Pliszka M, Mogut D, Darewicz M. Characteristics of biopeptides released in silico from collagens using quantitative parameters. Foods. 2020;9(7). https://doi.org/10.3390/foods9070965
44. Kartal C, Türköz BK, Otles S. Prediction, identification and evaluation of bioactive peptides from tomato seed proteins using in silico approach. Journal of Food Measurement and Characterization. 2020;14(4):1865–1883. https://doi.org/10.1007/s11694-020-00434-z
45. Kusumah J, Real Hernandez LM, de Mejia EG. Antioxidant potential of mung bean (Vigna radiata) albumin peptides produced by enzymatic hydrolysis analyzed by biochemical and in silico methods. Foods. 2020;9(9). https://doi.org/10.3390/foods9091241
46. Baghban R, Ghasemali S, Farajnia S, Hoseinpoor R, Andarzi S, Zakariazadeh M, et al. Design and in silico evaluation of a novel cyclic disulfide-rich anti-VEGF peptide as a potential antiangiogenic drug. International Journal of Peptide Research and Therapeutics. 2021;27(4):2245–2256. https://doi.org/10.1007/s10989-021-10250-8
47. Gu Y, Li X, Liu H, Li Q, Xiao R, Dudu OE, et al. The impact of multiple-species starters on the peptide profiles of yoghurts. International Dairy Journal. 2020;106. https://doi.org/10.1016/j.idairyj.2020.104684
48. Tu M, Liu H, Zhang R, Chen H, Fan F, Shi P, et al. Bioactive hydrolysates from casein: generation, identification, and in silico toxicity and allergenicity prediction of peptides. Journal of the Science of Food and Agriculture. 2018;98(9):3416–3426. https://doi.org/10.1002/jsfa.8854
49. Nutten S, Schuh S, Dutter T, Heine RG, Kuslys M. Design, quality, safety and efficacy of extensively hydrolyzed formula for management of cow's milk protein allergy: What are the challenges? Advances in Food and Nutrition Research. 2020;93:147–204. https://doi.org/10.1016/bs.afnr.2020.04.004
50. Gromov DA, Borisova AV, Bakharev VV. Food allergens and methods for producing hypoallergenic foods. Food Processing: Techniques and Technology. 2021;51(2):232–247. (In Russ.). https://doi.org/10.21603/2074-9414-2021-2-232-247
51. Loh W, Tang MLK. The epidemiology of food allergy in the global context. International Journal of Environmental Research and Public Health. 2018;15(9). https://doi.org/10.3390/ijerph15092043
52. Flom JD, Sicherer SH. Epidemiology of cow’s milk allergy. Nutrients. 2019;11(5). https://doi.org/10.3390/nu11051051
53. Sharma N, Patiyal S, Dhall A, Pande A, Arora C, Raghava GPS. AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes. Briefings in Bioinformatics. 2021;22(4). https://doi.org/10.1093/bib/bbaa294
54. Vens C, Rosso MN, Danchin EG. Identifying discriminative classification-based motifs in biological sequences. Bioinformatics. 2011;27(9):1231–1238. https://doi.org/10.1093/bioinformatics/btr110
55. Kharitonov VD, Agarkova EYu, Kruchinin AG, Ryazantseva KA, Korolyeva OV, Fedorova TV, et al. Impact of new fermented dairy product with whey protein hydrolysate on tolerance and dynamics of atopic dermatitis manifestation in children suffering from cow's milk protein allergy. Problems of Nutrition. 2015;84(5):56–63. (In Russ.). https://doi.org/10.24411/0042-8833-2015-00048
56. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GPS. In silico approach for predicting toxicity of peptides and proteins. PloS ONE. 2013;8(9). https://doi.org/10.1371/journal.pone.0073957
57. Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, et al. A web server and mobile app for computing hemolytic potency of peptides. Scientific Reports. 2016;6. https://doi.org/10.1038/srep22843
58. Parthasarathi R, Dhawan A. In silico approaches for predictive toxicology. In: Dhawan A, Kwon S, editors. In vitro toxicology. Academic Press; 2018. pp. 91–109. https://doi.org/10.1016/B978-0-12-804667-8.00005-5
59. Yap PG, Gan CY. In vivo challenges of anti-diabetic peptide therapeutics: Gastrointestinal stability, toxicity and allergenicity. Trends in Food Science and Technology. 2020;105:161–175. https://doi.org/10.1016/j.tifs.2020.09.005
60. Guo H, Hao Y, Richel A, Everaert N, Chen Y, Liu M, et al. Antihypertensive effect of quinoa protein under simulated gastrointestinal digestion and peptide characterization. Journal of the Science of Food and Agriculture. 2020;100(15):5569–5576. https://doi.org/10.1002/jsfa.10609
61. Ji D, Udenigwe C, Agyei D. Antioxidant peptides encrypted in flaxseed proteome: An in silico assessment. Food Science and Human Wellness. 2019;8(3):306–314. https://doi.org/10.1016/j.fshw.2019.08.002
62. Lin K, Zhang L-W, Han X, Xin L, Meng Z-X, Gong P-M, et al. Yak milk casein as potential precursor of angiotensin I-converting enzyme inhibitory peptides based on in silico proteolysis. Food Chemistry. 2018;254:340–347. https://doi.org/10.1016/j.foodchem.2018.02.051
63. Tu M, Qiao X, Wang C, Liu H, Cheng S, Xu Z, et al. In vitro and in silico analysis of dual-function peptides derived from casein hydrolysate. Food Science and Human Wellness. 2021;10(1):32–37. https://doi.org/10.1016/j.fshw.2020.08.014
64. Dagan-Wiener A, Di Pizio A, Nissim I, Bahia MS, Dubovski N, Margulis E, et al. BitterDB: taste ligands and receptors database in 2019. Nucleic Acids Research. 2019;47(D1):D1179–D1185. https://doi.org/10.1093/nar/gky974
65. Wang W, Zhang L, Wang Z, Wang X, Liu Y. Physicochemical and sensory variables of Maillard reaction products obtained from Takifugu obscurus muscle hydrolysates. Food Chemistry. 2019;290:40–46. https://doi.org/10.1016/j.foodchem.2019.03.065
66. Stan GM, Constantinescu-Aruxandei D, Oancea F. In silico analysis of the formation of bioactive peptides from silver carp (Hypophthalmichthys molitrix) collagen. Proceedings. 2020;57(1). https://doi.org/10.3390/proceedings2020057024
67. Pooja K, Rani S, Prakash B. In silico approaches towards the exploration of rice bran proteins-derived angiotensin-I-converting enzyme inhibitory peptides. International Journal of Food Properties. 2017;20. https://doi.org/10.1080/10942912.2017.1368552
68. Shi P, Fan F, Chen H, Xu Z, Cheng S, Lu W, et al. A bovine lactoferrin–derived peptide induced osteogenesis via regulation of osteoblast proliferation and differentiation. Journal of Dairy Science. 2020;103(5):3950–3960. https://doi.org/10.3168/jds.2019-17425
69. Tu M, Liu H, Zhang R, Chen H, Mao F, Cheng S, et al. Analysis and evaluation of the inhibitory mechanism of a novel angiotensin-I-converting enzyme inhibitory peptide derived from casein hydrolysate. Journal of Agricultural and Food Chemistry. 2018;66(16):4139–4144. https://doi.org/10.1021/acs.jafc.8b00732
70. Sayd T, Dufour C, Chambon C, Buffière C, Remond D, Santé-Lhoutellier V. Combined in vivo and in silico approaches for predicting the release of bioactive peptides from meat digestion. Food Chemistry. 2018;249:111–118. https://doi.org/10.1016/j.foodchem.2018.01.013