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

Shohreh Ariaeenejad,Maryam Mousivand,Maryam Hashemi,Ghasem Hosseini Salekdeh,Kaveh Kavousi,Parinaz Moradi Dezfouli

doi:10.1371/journal.pone.0205796

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.

Highlights

After cellulose, xylan is the second most abundant polysaccharide in nature which is mostly found in the plant cell wall and accounts for a large part of plants biomass
Experimental screening resulted in isolation and identification of 41 isolates producing xylanase enzyme
Applying Random Forest, Naïve Bayes, SVM, and KNN on 41 sequences listed in Table 1 for classifying the area of bacterial halos from respective expressed xylanase enzymes showed that the diameter and the area of these halos could be classified with high accuracy in one of the three categories L, M, or H

Summary

Introduction

Xylan is the second most abundant polysaccharide in nature which is mostly found in the plant cell wall and accounts for a large part of plants biomass. Xylan can be depolymerized using xylanase enzymes, an important family of hydrolases. The glycosyl hydrolases (GHs) are a very large family of enzymes which hydrolyze the glycosidic bond between carbohydrates as well as between a carbohydrate and a noncarbohydrate moiety to form heteropolysaccharides. Endo-xylanases with somewhat different sequences are found in various GH families because of the sequence-based classification of GH enzymes and despite similar structures and conserved folding [3,4,5,6,7,8,9]. Heteroxylan backbone is composed of glycoside linkages. For cleaving these bonds, the interaction of some cleavage enzymes for both main and side chains is required

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