Role of subsite +1 residues in pH dependence and catalytic activity of the glycoside hydrolase family 1 β-glucosidase BGL1A from the basidiomycetePhanerochaete chrysosporium

A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV)

With the use of genetic engineering, modified and sometimes more efficient enzymes can be created for different purposes, including industrial applications. However, building modified enzymes depends on several in vitro experiments, which may result in the process being expensive and time-consuming. Therefore, computational approaches could reduce costs and accelerate the discovery of new technological products. In this study, we present a method, called structural signature variation (SSV), to propose mutations for improving enzymes’ activity. SSV uses the structural signature variation between target enzymes and template enzymes (obtained from the literature) to determine if randomly suggested mutations may provide some benefit for an enzyme, such as improvement of catalytic activity, half-life, and thermostability, or resistance to inhibition. To evaluate SSV, we carried out a case study that suggested mutations in β-glucosidases: Essential enzymes used in biofuel production that suffer inhibition by their product. We collected 27 mutations described in the literature, and manually classified them as beneficial or not. SSV was able to classify the mutations with values of 0.89 and 0.92 for precision and specificity, respectively. Then, we used SSV to propose mutations for Bgl1B, a low-performance β-glucosidase. We detected 15 mutations that could be beneficial. Three of these mutations (H228C, H228T, and H228V) have been related in the literature to the mechanism of glucose tolerance and stimulation in GH1 β-glucosidase. Hence, SSV was capable of detecting promising mutations, already validated by in vitro experiments, that improved the inhibition resistance of a β-glucosidase and, consequently, its catalytic activity. SSV might be useful for the engineering of enzymes used in biofuel production or other industrial applications.

Molecular cloning and expression of thermostable glucose-tolerant β-glucosidase of Penicillium funiculosum NCL1 in Pichia pastoris and its characterization

A partial peptide sequence of β-glucosidase isoform (Bgl4) of Penicillium funiculosum NCL1 was identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The cDNA (bgl4) encoding Bgl4 protein was cloned from P. funiculosum NCL1 RNA by consensus RT-PCR. The bgl4 gene encoded 857 amino acids that contained catalytic domains specific for glycoside hydrolase family 3. The cDNA was over-expressed in Pichia pastoris KM71H and the recombinant protein (rBgl4) was purified with the specific activity of 1,354.3 U/mg. The rBgl4 was a glycoprotein with the molecular weight of ~130 kDa and showed optimal activity at pH 5.0 and 60 °C. The enzyme was thermo-tolerant up to 60 °C for 60 min. The rBgl4 was highly active on aryl substrates with β-glucosidic, β-xylosidic linkages and moderately active on cellobiose and salicin. It showed remarkably high substrate conversion rate of 3,332 and 2,083 μmol/min/mg with the substrates p-nitrophenyl β-glucoside and cellobiose respectively. In addition, the rBgl4 showed tolerance to glucose concentration up to 400 mM. It exhibited twofold increase in glucose yield when supplemented with crude cellulase of Trichoderma reesei Rut-C30 in cellulose hydrolysis. These results suggested that rBgl4 is a thermo- and glucose-tolerant β-glucosidase and is a potential supplement for commercial cellulase in cellulose hydrolysis and thereby assures profitability in bioethanol production.

Prediction of optimal pH in hydrolytic reaction of beta-glucosidase

This is the continuation of our studies to use very basic information on enzyme to predict optimal reaction parameters in enzymatic reactions because the gap between available enzyme sequences and their available reaction parameters is widening. In this study, 23 features selected from 540 plus features of individual amino acid as well as a feature combined whole protein information were screened as independents in a 20-1 feedforward backpropagation neural network for predicting optimal pH in beta-glucosidase's hydrolytic reaction because this enzyme drew attention recently due to its role in biofuel industry. The results show that 11 features can be used as independents for the prediction, while the feature of amino acid distribution probability works better than the rest independents for the prediction. Our study paves a way to predict the optimal reaction parameters of enzymes based on the amino acid features of enzyme sequences.

Differential involvement of β-glucosidases from Hypocrea jecorina in rapid induction of cellulase genes by cellulose and cellobiose

Appropriate perception of cellulose outside the cell by transforming it into an intracellular signal ensures the rapid production of cellulases by cellulolytic Hypocrea jecorina. The major extracellular β-glucosidase BglI (CEL3a) has been shown to contribute to the efficient induction of cellulase genes. Multiple β-glucosidases belonging to glycosyl hydrolase (GH) family 3 and 1, however, exist in H. jecorina. Here we demonstrated that CEL1b, like CEL1a, was an intracellular β-glucosidase displaying in vitro transglycosylation activity. We then found evidence that these two major intracellular β-glucosidases were involved in the rapid induction of cellulase genes by insoluble cellulose. Deletion of cel1a and cel1b significantly compromised the efficient gene expression of the major cellulase gene, cbh1. Simultaneous absence of BglI, CEL1a, and CEL1b caused the induction of the cellulase gene by cellulose to further deteriorate. The induction defect, however, was not observed with cellobiose. The absence of the three β-glucosidases, rather, facilitated the induced synthesis of cellulase on cellobiose. Furthermore, addition of cellobiose restored the productive induction on cellulose in the deletion strains. The results indicate that the three β-glucosidases may not participate in transforming cellobiose beyond hydrolysis to provoke cellulase formation in H. jecorina. They may otherwise contribute to the accumulation of cellobiose from cellulose as inducing signals.

Predicting Km values of beta-glucosidases using cellobiose as substrate

The Michaelis-Menten constant Km is a very important parameter to relate enzyme with its substrate in enzymatic reaction. Although Km can be experimentally determined, the Km values are not easily available in literature. With rapid increase of newly designed enzymes, we face the shortage of parameters related to enzymatic reactions. The beta-glucosidase is a crucial enzyme for cellulose hydrolysis and cellobiose is one of its substrates. In this study, we attempt to develop models to predict Km with cellobiose as substrates using information about primary structure of beta-glucosidase. The results show that the 20-1 feedforward backpropagation neural network using the amino-acid distribution probability as predictor works best for prediction of Km values.