Article A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus Huda Ansaf 1 , Bahaa Kazem Ansaf 2, * and Sanaa S. Al Samahi 3,4   Citation: Ansaf, H.; Ansaf, B.K.; Al Samahi, S.S. A Neuro-Fuzzy Technique for Modeling of β-Glucosidase Activity from Agaricus bisporus. BioChem 2021, 1, 159–173. https://doi.org/10.3390/ biochem1030013 Academic Editor: Yehia Mechref Received: 29 June 2021 Accepted: 9 October 2021 Published: 19 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA; hah34@umsystem.edu 2 Department of Engineering, Colorado State University Pueblo, Pueblo, CO 81001, USA 3 Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 17001, Iraq; samvd@umsystem.edu 4 Department of Electrical Engineering, University of Missouri, Columbia, MO 65211, USA * Correspondence: bahaa.ansaf@csupueblo.edu Abstract: This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANFIS technique were embedded in a new fuzzy inference system to simultaneously encompass the impact of temperature and pH level on the activity of β-glucosidase. The required base rules for the developed fuzzy inference system were created to describe the antecedent (pH and temperature) implication to the consequent (enzyme activity), using the singleton Sugeno fuzzy inference technique. The simulation results from the developed models achieved high accuracy. The neuro-fuzzy approach performed very well in predicting β-glucosidase activity through comparative analysis. The proposed approach may be used to predict enzyme kinetics for several nonlinear biosynthetic processes. Keywords: Agaricus bisporus; β-glucosidase; fuzzy logic; neural network; enzyme; kinetic modeling 1. Introduction The catalytic enzyme β-glucosidase ( β-D-glucoside glucohydrolase, EC 3.2.1.21) hy- drolyzes the glycosidic bonds in carbohydrates to non-reducing terminal glycosyl residues, oligosaccharides, and glycosides. The β-glucosidase enzyme exists in a multitude of or- ganisms ranging from bacteria, archaea to eukaryotes [1]. These enzymes are responsible for the conversion of biomass in microorganisms, the breakdown of glycolipids and lig- nification processes, the activation of phytohormones, catabolism of cell walls in plants, and the interactions between plants and microbes [2]. β-Glucosidase is a major therapeutic target for Gaucher’s disease, resulting from β-glucosidase insufficiency [3]. This enzyme type is a significant component in the multi-enzyme cellulose complex and catalyzes the final step in cellulose hydrolysis. Cellulose is the most ample carbohydrate on Earth, and there is an abundance of applicable research conducted on its potential usage in numerous industries [4–7]. Cellulose enzymes catalyze cellulose to synthesize cellobiose and other short-term cello-oligosaccharides, which are hydrolyzed by β-glucosidase to glucose [5]. The dual characteristic of β-glucosidase, which allows it to synthesize as well as degrade glycosidic bonds, gives it a vast potential from an industrial point of view [2]. Current global energy demands and the increasing burden on fossil fuels have in- creased the need for more efficient production of biofuels at a substantial scale to re- place nonrenewable fuel sources. The cellulosic biofuel production method consists of decomposing lignocellulosic biomass into sugar, followed by fermentation processes to BioChem 2021, 1, 159–173. https://doi.org/10.3390/biochem1030013 https://www.mdpi.com/journal/biochem