Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach

Abstract

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Optimization ACE inhibition activity in hypertension based on random vector functional link and sine-cosine algorithm

“…ACE inhibition activity varied between different samples as it is affected directly by chemical composition and structure [6]. Noteworthy, recent studies support our results and reported that temperature has the greatest effect on protein metabolism and gene expression of amino acids in fish [1,31].…”

“…Functional properties of peptides with antihypertensive effect are directly related to their chemical structure [1]. Therefore, the structure-function relationship of peptides from protein hydrolysates needs to be analyzed in depth to allow optimal use of their chemical diversity by the chemical industry.…”

“…These include artificial neural networks (ANN) with one or more hidden layers and the backpropagation training [8,9] and the Support Vector Regression (SVR) [10,11]. In an interesting study, an adaptive neuro-fuzzy inference system (ANFIS) model have been used to predict the bioactive peptides in relation to climate change [1].…”

Emerging strategies for the development of food industries

“…This enzyme is of especial interest in the industrial starch fermentation for the production of alcohols, amino acids, high-glucose and high-maltose syrups, which relies on pullulanase to degrade a-1,6-glycosidic linkages to improve starch hydrolysis [102]. Elaziz et al [103] also used bioinformatics to generate a prediction algorithm to enhance the properties of fish products with therapeutic and industrial roles, bioactive amino acids. Using biotechnological approaches, the concentration level of these compounds was assessed in protein hydrolyzates extracted from tilapia fish.…”

Conventional and in silico approaches to select promising food-derived bioactive peptides: A review

“…Therefore, the use of these in silico tools avoids the expense of laboratory time and reagent money. Additionally, they have allowed to find new BP from alternative sources, e.g., kefir milk ( Amorim et al, 2019a ), collagen ( Nuñez, Guzmán, Valencia, Almonacid & Cárdenas, 2020 ), fish skin ( Elaziz, Hemdan, Hassanien, Oliva & Xiong, 2017 ), invasive sea grass Halophila stipulacea ( Kandemir-Cavas, Pérez-Sánchez, Mert-Ozupek & Cavas, 2019 ), Chlorella sorokiniana ( Tejano, Peralta, Yap, Panjaitan & Chang, 2019 ), cyanobacterium Arthrospira platensis ( Ji et al, 2018 ), among many others.…”