A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features

Abstract

Xylanases are hydrolytic enzymes which based on physicochemical properties, structure, mode of action and substrate specificities are classified into various glycoside hydrolase (GH) families. The purpose of this study is to show that the activity of the members of the xylanase family in the specified pH and temperature conditions can be computationally predicted. The proposed computational regression model was trained and tested with the Pseudo Amino Acid Composition (PseAAC) features extracted solely from the amino acid sequences of enzymes. The xylanases with experimentally determined activities were used as the training dataset to adjust the model parameters. To develop the model, 41 strains of Bacillus subtilis isolated from field soil were screened. From them, 28 strains with the highest halo diameter were selected for further studies. The performance of the model for prediction of xylanase activity was evaluated in three different temperature and pH conditions using stratified cross-validation and jackknife methods. The trained model can be used for determining the activity of newly found xylanases in the specified condition. Such computational models help to scale down the experimental costs and save time by identifying enzymes with appropriate activity for scientific and industrial usage. Our methodology for activity prediction of xylanase enzymes can be potentially applied to the members of the other enzyme families. The availability of sufficient experimental data in specified pH and temperature conditions is a prerequisite for training the learning model and to achieve high accuracy.

An overview of biomass conversion: exploring new opportunities

“…The activity of xylanases can be characterized under different conditions. Computational models can help to scale down the experimental costs and save time by identifying enzymes with appropriate activity for scientific and industrial usage (Ariaeenejad et al, 2018).…”

A novel high performance in-silico screened metagenome-derived alkali-thermostable endo-β-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions

“…In-silico screening provided the opportunity to explore the extensive biodiversity of nature and thus enabling the identification of several novel enzymes from different environments. Moreover, strong power of the metagenomic and in-silico screening in identification of the novel cellulases and hemicellulases with high ability to degrade lignocellulosic biomass was confirmed [ 26 – 29 ].…”

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

“…One of the major computational approaches that has been rigorously employed in bioinformatics is machine learning, which as the literature can prove, is capable of mapping the relationships between the primary structure of proteins and their different properties and make reasonable predictions based upon them (Shastry and Sanjay, 2020). Machine learning techniques have been successfully applied so predict various properties of proteins such as activity (Ariaeenejad et al, 2018), tertiary structure (Cheng et al, 2008), subcellular localization (Almagro Armenteros et al, 2017), stability at different environmental conditions (Wu et al, 2009), etc. More specifically, Yan and Wu (2012) used a neural network to predict optimum pH and temperature of endoglucanases (EC 3.2.1.4) from their primary structure.…”

“…In a 2019 study, utilized machine learning to predict the optimum growth temperature (OGT) for microorganism based in their proteome and subsequently, used the predicted OGT alongside with the enzymes' amino acid compositions to predict their catalytic optima. In another research, Ariaeenejad et al (2018) applied a regression model based on pseudo amino acid composition to predict the optimum temperature and pH of xylanase in strains of Bacillus subtilis enzymes.…”