# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 1 Introduction  
Biofuels are alternative energy sources, which can be obtained from organic biomass. Cellulose is one of the primary sources of biomass on planet Earth, making up half of the weight of plants [1, 2]. It can be used in producing renewable fuels and can be found in raw materials such as corn and sugar cane. For example, in the first-generation production process of bioethanol from sugar cane, fermentable sugars can be obtained from sugar cane juice in a process known as saccharification [3]. The sugars are then used in the fermentation process to obtain bioethanol. Sugar cane bagasse is a waste resulting from this process [4]. However, it can be used as a by-product to produce second-generation biofuels. The sugarcane bagasse is composed of three main lignocellulosic structures: cellulose, hemicellulose, and lignin [5]. In the second-generation biofuel production process, the bagasse undergoes pre-treatment to break down the rigid structure of the lignocellulosic, facilitating the release of fermentable sugars for ethanol production [6]. Then, the saccharification process occurs, where sugar is extracted from the bagasse by the kinetic action of sets of enzymes. In this context, endoglucanases (E.C. 3.2.1.4) act on the cellulose structure, generating oligosaccharides of varying sizes. Then, exoglucanases (E.C. 3.2.1.91) cleave these oligosaccharides, releasing disaccharides, such as cellobiose [7]. Finally, \(\beta\)-glucosidaseases (E.C. 3.2.1.21) hydrolyze cellobiose, releasing two glucose molecules, the primary sugar used in fermentation [8]. It is widely known that their product (glucose) can inhibit \(\beta\)-glucosidaseages [9; 10; 11]. Furthermore, glucose inhibition of \(\beta\)-glucosidase generates cellobiose accumulation. Endoglucanases can be inhibited by high cellobiose concentrations [12; 13; 14]. Therefore, the class of glucose-tolerant \(\beta\)-glucosidase enzymes has been extensively studied to improve biofuel production. However, other characteristics may be necessary for more efficient industrial biofuel production, such as resistance to high temperatures. The literature has reported that high temperatures can cause the denaturation of \(\beta\)-glucosidase [15; 16; 17]. Additionally, temperature is a critical factor that depends on the production schemes adopted. In industrial production, the saccharification and fermentation processes can occur using two main schemes: SSF and SHF [6]. In the simultaneous saccharification and fermentation (SSF) process, hydrolysis and fermentation occur in the same reactor. This process reduces the inhibition of accumulated sugars since, as glucose is released into the medium, fermentation occurs, producing bioethanol. However, the saccharification and fermentation processes occur at different optimum temperatures. While saccharification occurs at an optimum temperature of 55 \({}^{\circ}\)C, fermentation occurs at 30 \({}^{\circ}\)C [18]. Thus, when carrying out these two processes simultaneously, the difference in optimal enzyme temperatures can impair the efficiency of the process. In the separate hydrolysis and fermentation (SHF) process, saccharification occurs in a reactor under controlled temperature and pH conditions [6]. The sugars released by hydrolysis are then separated from lignin and other solid residues and transferred to another reactor, where they are fermented by microorganisms such as yeast. This process has the advantage of allowing for the enzymatic hydrolysis and fermentation temperatures to be optimized, thus achieving the best production performance [19]. However, this can lead to increased costs and production time. As can be seen, SSF and SHF have advantages and disadvantages. However, in both processes, we can see the importance of thermostable enzymes, which can maintain their activity even at high temperatures. Thermostable enzymes are widely applied in industry, playing a key role in different saccharification and fermentation processes [17].