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In Silico Analysis of Glucose Oxidase from Aspergillus niger: Potential Cysteine Mutation Sites for Enhancing Protein Stability.

ABSTRACT: Glucose oxidase (GOx) holds considerable advantages for various applications. Nevertheless, the thermal instability of the enzyme remains a grand challenge, impeding the success in applications outside the well-controlled laboratories, particularly in practical bioelectronics. Many strategies to modify GOx to achieve better thermal stability have been proposed. However, modification of this enzyme by adding extra disulfide bonds is yet to be explored. This work describes the in silico bioengineering of GOx from Aspergillus niger by judiciously analyzing characteristics of disulfide bonds found in the Top8000 protein database, then scanning for amino acid residue pairs that are suitable to be replaced with cysteines in order to establish disulfide bonds. Next, we predicted and assessed the mutant GOx models in terms of disulfide bond quality (bond length and α angles), functional impact by means of residue conservation, and structural impact as indicated by Gibbs free energy. We found eight putative residue pairs that can be engineered to form disulfide bonds. Five of these are located in less conserved regions and, therefore, are unlikely to have a deleterious impact on functionality. Finally, two mutations, Pro149Cys and His158Cys, showed potential for stabilizing the protein structure as confirmed by a structure-based stability analysis tool. The findings in this study highlight the opportunity of using disulfide bond modification as a new alternative technique to enhance the thermal stability of GOx.

SUBMITTER: Ittisoponpisan S

PROVIDER: S-EPMC8615187 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMany filamentous fungal genomes contain complex groups of multicopper oxidase (MCO) coding genes that makes them a good source for new laccases with potential biotechnological interest. A bioinformatics analysis of the Aspergillus niger ATCC 1015 genome resulted in the identification of thirteen MCO genes. Ten of them were cloned and homologously overexpressed.ResultsA bioinformatic analysis of the A. niger ATCC 1015 genome revealed the presence of 13 MCO genes belonging to three different subfamilies on the basis of their phylogenetic relationships: ascomycete laccases, fungal pigment MCOs and fungal ferroxidases. According to in silico amino acid sequence analysis, the putative genes encoding for functional extracellular laccases (mcoA, mcoB, mcoC, mcoD, mcoE, mcoF, mcoG, mcoI, mcoJ and mcoM) were placed under the control of the glaA promoter and overexpressed in A. niger N593. Enzyme activity plate assays with several common laccase substrates showed that all genes are actually expressed and code for active MCOs. Interestingly, expressed enzymes show different substrate specificities. In addition, optimization of fungal pigment MCOs extracellular production was investigated. The performance of the widely used glucoamylase signal sequence (ssGlaA) in McoA secretion was studied. Results obtained suggest that ssGlaA do not yield higher levels of secreted McoA when compared to its native secretion signal. Also, McoB synthesis was investigated using different nitrogen sources in minimal medium liquid cultures. Higher yields of extracellular McoB were achieved with (NH4)2 tartrate.ConclusionsAspergillus niger is a good source of new laccases. The different substrate specificity observed in plate assays makes them interesting to be purified and biochemically compared. The homologous signal sequence of McoA has been shown to be a good choice for its extracellular overexpression. From the nitrogen sources tested (NH4)2 tartrate has been found to be the most appropriate for McoB production in A. niger.

Project description:BACKGROUND:The development of biological processes that replace the existing petrochemical-based industry is one of the biggest challenges in biotechnology. Aspergillus niger is one of the main industrial producers of lignocellulolytic enzymes, which are used in the conversion of lignocellulosic feedstocks into fermentable sugars. Both the hydrolytic enzymes responsible for lignocellulose depolymerisation and the molecular mechanisms controlling their expression have been well described, but little is known about the transport systems for sugar uptake in A. niger. Understanding the transportome of A. niger is essential to achieve further improvements at strain and process design level. Therefore, this study aims to identify and classify A. niger sugar transporters, using newly developed tools for in silico and in vivo analysis of its membrane-associated proteome. RESULTS:In the present research work, a hidden Markov model (HMM), that shows a good performance in the identification and segmentation of functionally validated glucose transporters, was constructed. The model (HMMgluT) was used to analyse the A. niger membrane-associated proteome response to high and low glucose concentrations at a low pH. By combining the abundance patterns of the proteins found in the A. niger plasmalemma proteome with their HMMgluT scores, two new putative high-affinity glucose transporters, denoted MstG and MstH, were identified. MstG and MstH were functionally validated and biochemically characterised by heterologous expression in a S. cerevisiae glucose transport null mutant. They were shown to be a high-affinity glucose transporter (K m = 0.5 ± 0.04 mM) and a very high-affinity glucose transporter (K m = 0.06 ± 0.005 mM), respectively. CONCLUSIONS:This study, focusing for the first time on the membrane-associated proteome of the industrially relevant organism A. niger, shows the global response of the transportome to the availability of different glucose concentrations. Analysis of the A. niger transportome with the newly developed HMMgluT showed to be an efficient approach for the identification and classification of new glucose transporters.

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In Silico Analysis of Glucose Oxidase from Aspergillus niger: Potential Cysteine Mutation Sites for Enhancing Protein Stability.

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