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IJSTR >> Volume 10 - Issue 8, August 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Food-Derived Bioactive Peptides And Artificial Intelligence Techniques For Their Prediction: A Brief Review

[Full Text]



Margarita Terziyska, Ivelina Desseva, Zhelyazko Terziyski



artificial intelligence, activity prediction, food-derived peptides, deep learning, neural networks.



Biologically active peptides (BAPs) have a positive effect on human health, which is why they are used as a basis for drug and functional foods development. They are therefore of economic interest. However, the process of their isolation is too expensive and time-consuming. Hence, it is necessary to develop more effective methods to predict the potential activity of peptides. An appropriate solution could be an in silico approach, in particular the use of computational methods based on artificial intelligence (AI) techniques. The use of AI approaches may facilitate the identification of bioactive peptides. Thus, in this paper, along with some basic information about food-derived BAPs, a brief review of the AI techniques used for their activity prediction is made.



[1] Sharma, P., Kaur, H., Kehinde, B., Chhikara, N., Sharma, D., Panghal, A.: Food-Derived Anticancer Peptides: A Review. International Journal of Peptide Research and Therapeutics, 1-16 (2020).
[2] Patil, P., Mandal, S., Tomar, S., Anand, S.: Food protein-derived bioactive peptides in management of type 2 diabetes. European journal of nutrition, 54(6), 863-880 (2015).
[3] Daliri, E. B. M., Oh, D. H., Lee, B. H. Bioactive peptides. Foods, 6(5), 32 (2017).
[4] Imai, K., Ji, D., Nwachukwu, I., Agyei, D., Udenigwe, C.: Bioinformatics and Chemometrics for Discovering Biologically Active Peptides From Food Proteins (2019).
[5] Malaguti, M., Dinelli, G., Leoncini, E., Bregola, V., Bosi, S., Cicero, A., Hrelia, S.: Bioactive peptides in cereals and legumes: agronomical, biochemical and clinical aspects. International journal of molecular sciences, 15(11), 21120-21135 (2014).
[6] Sanchez, A., Vazquez, A.: Bioactive peptides: A review. Food Quality and Safety, 1(1), 29-46 (2017).
[7] Bhat, Z., Kumar, S., Bhat, H.: Bioactive peptides from egg: a review. Nutrition & Food Science (2015).
[8] Anusha, R., Bindhu, O.: Bioactive peptides from milk. MILK PROTEINS, 101 (2016).
[9] Ryan, J., Ross, R., Bolton, D., Fitzgerald, G., Stanton, C.: Bioactive peptides from muscle sources: meat and fish. Nutrients, 3(9), 765-791 (2011).
[10] Kamran, F., Reddy, N.: Bioactive peptides from legumes: Functional and nutraceutical potential. Recent Advances in Food Science, 1(3), 134-149 (2018).
[11] Le Gouic, A., Harnedy, P., FitzGerald, R.: Bioactive peptides from fish protein by-products. Bioactive molecules in food. Cham: Springer International Publishing, 1-35 (2018).
[12] Pihlanto, A.: Lactic fermentation and bioactive peptides. Lactic Acid Bacteria—R & D for Food, Health and Livestock Purposes, 309-332 (2013).
[13] Caron, J., Cudennec, B., Domenger, D., Belguesmia, Y., Flahaut, C., Kouach, M., Lesage, J., Goossens, J., Dhulster, P., Ravallec, R.: Simulated GI digestion of dietary protein: Release of new bioactive peptides involved in gut hormone secretion. Food Research International, 89, 382-390 (2016).
[14] Brodkorb, A., Egger, L., Alminger, M., et al.: INFOGEST static in vitro simulation of gastrointestinal food digestion. Nature protocols, 14(4), 991-1014 (2019).
[15] Balgir, P., Kaur, T., Sharma, M.: Antihypertensive peptides derived from food sources. MOJ Food Processing & Technology, 2(1), 1-6 (2016).
[16] Yamaguchi, N., Kawaguchi, K., Yamamoto, N.: Study of the mechanism of antihypertensive peptides VPP and IPP in spontaneously hypertensive rats by DNA microarray analysis. European journal of pharmacology, 620(1-3), 71-77 (2009).
[17] Sipola, M., Finckenberg, P., Santisteban, J., Korpela, R., Vapaatalo, H., Nurminen, M.: Long-term intake of milk peptides attenuates development of hypertension in spontaneously hypertensive rats. J Physiol Pharmacol, 52(4 Pt 2), 745-754 (2001).
[18] Haney, E., Straus, S., Hancock, R.: Reassessing the host defense peptide landscape. Frontiers in chemistry, 7, 43 (2019).
[19] Pizzo, E., Cafaro, V., Di Donato, A., Notomista, E.: Cryptic Antimicrobial Peptides: identification methods and current knowledge of their immunomodulatory properties. Current pharmaceutical design, 24(10), 1054-1066 (2018).
[20] Udenigwe, C.: Bioinformatics approaches, prospects and challenges of food bioactive peptide research. Trends in Food Science & Technology, 36(2), 137-143 (2014).
[21] Pellegrini, A.: Antimicrobial peptides from food proteins. Current pharmaceutical design, 9(16), 1225-1238 (2003).
[22] Mohanty, D., Jena, R., Choudhury, P., Pattnaik, R., Mohapatra, S., Saini, M.: Milk derived antimicrobial bioactive peptides: a review. International Journal of Food Properties, 19(4), 837-846 (2016).
[23] Wong, F., Xiao, J., Wang, S., Ee, K., Chai, T.: Advances on the antioxidant peptides from edible plant sources. Trends in Food Science & Technology (2020).
[24] Nwachukwu, I., Aluko, R.: Structural and functional properties of food protein‐derived antioxidant peptides. Journal of Food Biochemistry, 43(1), e12761 (2019).
[25] Najafian, L., Babji, A.: A review of fish-derived antioxidant and antimicrobial peptides: their production, assessment, and applications. Peptides, 33(1), 178-185 (2012).
[26] Zou, T., He, T., Li, H., Tang, H., Xia, E.: The structure-activity relationship of the antioxidant peptides from natural proteins. Molecules, 21(1), 72 (2016).
[27] Basith, S., Manavalan, B., Hwan Shin, T., Lee, G.: Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening. Medicinal research reviews (2020).
[28] Panyayai, T., Ngamphiw, C., Tongsima, S., Mhuantong, W., Limsripraphan, W., Choowongkomon, K., Sawatdichaikul, O.: FeptideDB: A web application for new bioactive peptides from food protein. Heliyon, 5(7), e02076 (2019).
[29] Minkiewicz, P., Iwaniak, A., Darewicz, M.: BIOPEP-UWM database of bioactive peptides: Current opportunities. International journal of molecular sciences, 20(23), 5978 (2019).
[30] Li, Q., Zhang, C., Chen, H., Xue, J., Guo, X., Liang, M., Chen, M.: BioPepDB: An integrated data platform for food-derived bioactive peptides. International Journal of Food Sciences and Nutrition, 69(8), 963-968 (2018).
[31] Kumar, R., Chaudhary, K., Sharma, M., Nagpal, G., Chauhan, J., Singh, S., Gautam, A., Raghava, G.: AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides. Nucleic acids research, 43(D1), D956-D962 (2015).
[32] Nielsen, S. D., Beverly, R. L., Qu, Y., & Dallas, D. C.: Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization. Food Chemistry, 232, 673-682 (2017).
[33] Chen Z, Zhao P, Li F, et al. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics. 2018;34(14):2499‐2502.
[34] Pande, A., Patiyal, S., Lathwal, A., et al.: Computing wide range of protein/peptide features from their sequence and structure. bioRxiv, 599126 (2019).
[35] Chen Z, Zhao P, Li F, et al. iLearn: an integrated platform and meta‐learner for feature engineering, machine‐learning analysis and modeling of DNA, RNA and protein sequence data. Brief Bioinform. 2019.
[36] Wang J, Du PF, Xue XY, et al. VisFeature: a stand‐alone program for visualizing and analyzing statistical features of biological sequences. Bioinformatics. 2019.
[37] Muhammod R, Ahmed S, Md Farid D, Shatabda S, Sharma A, Dehzangi A. PyFeat: a Python‐based effective feature generation tool for DNA, RNA and protein sequences. Bioinformatics. 2019;35(19):3831‐3833.
[38] Cao D‐S, Xu Q‐S, Liang Y‐Z. Propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics. 2013;29(7): 960‐962.
[39] Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC. Pse‐in‐One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015;43(W1):W65‐W71.
[40] Liu B, Wu H, Chou K‐C. Pse‐in‐One 2.0: an improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nat Sci. 2017;9(04):67‐91.
[41] Nikam R, Gromiha MM. Seq2Feature: a comprehensive web‐based feature extraction tool. Bioinformatics. 2019;35: 4797‐4799.
[42] Dong J, Yao ZJ, Zhang L, et al. PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J Cheminform. 2018;10(1):16.
[43] Li, L., Wang, J., Zhao, M., Cui, C., Jiang, Y.: Artificial Neural Network for Production of Antioxidant Peptides Derived from Bighead Carp Muscles with Alcalase. Food Technology & Biotechnology, 44(3) (2006).
[44] Andreu, D., Torrent, M.: Prediction of bioactive peptides using artificial neural networks. In Artificial Neural Networks (pp. 101-118). Springer, New York, NY (2015).
[45] Huang, R., Du, Q., Wei, Y., Pang, Z., Wei, H., Chou, K.: Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design. Journal of theoretical biology, 256(3), 428-435 (2009).
[46] Soltani, S., Keymanesh, K.: Evaluation of structural features of membrane acting antifungal peptides by artificial neural networks (2008).
[47] Torrent, M., Andreu, D., Nogues, V., Boix, E.: Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PloS one, 6(2), e16968 (2011).
[48] Roudi, Y., Taylor, G.: Learning with hidden variables. Current opinion in neurobiology, 35, 110-118 (2015).
[49] Baldi, P.: Deep learning in biomedical data science. Annual review of biomedical data science, 1, 181-205 (2018).
[50] Müller, A., Kaymaz, A., Gabernet, G., Posselt, G., Wessler, S., Hiss, J., Schneider, G.: Sparse Neural Network Models of Antimicrobial Peptide‐Activity Relationships. Molecular informatics, 35(11-12), 606-614 (2016).
[51] Mooney, C., Haslam, N., Holton, T., Pollastri, G., Shields, D.: PeptideLocator: prediction of bioactive peptides in protein sequences. Bioinformatics, 29(9), 1120-1126 (2013).
[52] Veltri, D., Kamath, U., Shehu, A.: Deep learning improves antimicrobial peptide recognition. Bioinformatics, 34(16), 2740-2747 (2018).
[53] Fernandes, F., Rigden, D., Franco, O.: Prediction of antimicrobial peptides based on the adaptive neuro‐fuzzy inference system application. Peptide Science, 98(4), 280-287 (2012).
[54] Xiao, X., Wang, P., Lin, W., Jia, J., Chou, K.: iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical biochemistry, 436(2), 168-177 (2013).
[55] S.-J. Heo, Z. Chunwei, and E. Yu, ``Response simulation, data cleansing and restoration of dynamic and static measurements based on deep learning algorithms,'' Int. J. Concrete Struct. Mater., vol. 12, no. 1, p. 82, Dec. 2018.
[56] Terziyska, M., Todorov, Y., Doneva, M., & Metodieva, P. (2019). Distributed Adaptive Neuro Intuitionistic Fuzzy Architecture for prediction of the dose in gamma irradiated milk products. IFAC-PapersOnLine, 52(25), 75-80.
[57] Terziyska, M., Todorov, Y., & Olteanu, M. (2016, June). Input space selective fuzzification in intuitionistic semi fuzzy-neural network. In 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-7). IEEE.
[58] Todorov, Y., & Terziyska, M. (2014, September). Modeling of chaotic time series by interval type-2 neo-fuzzy neural network. In International Conference on Artificial Neural Networks (pp. 643-650). Springer, Cham.
[59] Terziyska, M., & Todorov, Y. (2016, September). Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics. In 2016 IEEE 8th International Conference on Intelligent Systems (IS) (pp. 616-621). IEEE.
[60] Boopathi, V., Subramaniyam, S., Malik, A., Lee, G., Manavalan, B., Yang, D.: mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides. International journal of molecular sciences, 20(8) (2019).
[61] Ng, X., Rosdi, B., Shahrudin, S.: Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity. BioMed research international, (2015).
[62] Mousavizadegan, M., Mohabatkar, H.: Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. Journal of bioinformatics and computational biology, 16(04), 1850016 (2018).
[63] Meher, P., Sahu, T., Saini, V., Rao, A.: Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific reports, 7(1), 1-12 (2017).
[64] Guan, X., Liu, J.: QSAR study of angiotensin i-converting enzyme inhibitory peptides using svhehs descriptor and osc-svm. International Journal of Peptide Research and Therapeutics, 25(1), 247-256 (2019).
[65] Li, B., Zhang, Y., Jin, M., Huang, T., Cai, Y.: Prediction of protein-peptide interactions with a nearest neighbor algorithm. Current Bioinformatics, 13(1), 14-24 (2018).
[66] Wang, L., Niu, D., Wang, X., Shen, Q., Xue, Y.: A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency. BioRxiv (2020).
[67] Lee, J., Lee, K., Joung, I., Joo, K., Brooks, B., Lee, J.: Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest. BMC bioinformatics, 16(1), 94 (2015).
[68] Manavalan, B., Lee, J., Lee, J.: Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms. PloS one, 9(9), e106542 (2014).
[69] Chang, K., Yang, J.: Analysis and prediction of highly effective antiviral peptides based on random forests. PloS one, 8(8), e70166 (2013).
[70] Laengsri, V., Nantasenamat, C., Schaduangrat, N., Nuchnoi, P., Prachayasittikul, V., Shoombuatong, W.: TargetAntiAngio: A sequence-based tool for the prediction and analysis of anti-angiogenic peptides. International journal of molecular sciences, 20(12), 2950 (2019).
[71] Manavalan, B., Shin, T., Kim, M., Lee, G., “AIPpred: sequence-based prediction of anti-inflammatory peptides using random forest.” Frontiers in pharmacology, 9, 276 (2018).