Dataset Information

Machine learning prediction of novel pectinolytic enzymes in Aspergillus niger through integrating heterogeneous (post-) genomics data.

ABSTRACT: Pectinolytic enzymes are a variety of enzymes involved in breaking down pectin, a complex and abundant plant cell-wall polysaccharide. In nature, pectinolytic enzymes play an essential role in allowing bacteria and fungi to depolymerize and utilize pectin. In addition, pectinases have been widely applied in various industries, such as the food, wine, textile, paper and pulp industries. Due to their important biological function and increasing industrial potential, discovery of novel pectinolytic enzymes has received global interest. However, traditional enzyme characterization relies heavily on biochemical experiments, which are time consuming, laborious and expensive. To accelerate identification of novel pectinolytic enzymes, an automatic approach is needed. We developed a machine learning (ML) approach for predicting pectinases in the industrial workhorse fungus, Aspergillus niger. The prediction integrated a diverse range of features, including evolutionary profile, gene expression, transcriptional regulation and biochemical characteristics. Results on both the training and the independent testing dataset showed that our method achieved over 90 % accuracy, and recalled over 60 % of pectinolytic genes. Application of the ML model on the A. niger genome led to the identification of 83 pectinases, covering both previously described pectinases and novel pectinases that do not belong to any known pectinolytic enzyme family. Our study demonstrated the tremendous potential of ML in discovery of new industrial enzymes through integrating heterogeneous (post-) genomimcs data.

SUBMITTER: Peng M

PROVIDER: S-EPMC8767319 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Aspergillus niger is a filamentous fungus well known for its ability to produce a wide variety of pectinolytic enzymes, which have many applications in the industry. The transcriptional activator GaaR is induced by 2-keto-3-deoxy-L-galactonate, a compound derived from D-galacturonic acid, and plays a major role in the regulation of pectinolytic genes. The requirement for inducer molecules can be a limiting factor for the production of enzymes. Therefore, the generation of chimeric transcription factors able to activate the expression of pectinolytic genes by using underutilized agricultural residues would be highly valuable for industrial applications. In this study, we used the CRISPR/Cas9 system to generate three chimeric GaaR-XlnR transcription factors expressed by the xlnR promoter by swapping the N-terminal region of the xylanolytic regulator XlnR to that of the GaaR in A. niger. As a test case, we constructed a PpgaX-hph reporter strain to evaluate the alteration of transcription factor specificity in the chimeric mutants. Our results showed that the chimeric GaaR-XlnR transcription factor was induced in the presence of D-xylose. Additionally, we generated a constitutively active GaaR-XlnR V756F version of the most efficient chimeric transcription factor to better assess its activity. Proteomics analysis confirmed the production of several pectinolytic enzymes by ΔgaaR mutants carrying the chimeric transcription factor. This correlates with the improved release of D-galacturonic acid from pectin by the GaaR-XlnR V756F mutant, as well as by the increased L-arabinose release from the pectin side chains by both chimeric mutants under inducing condition, which is required for efficient degradation of pectin. KEY POINTS: • Chimeric transcription factors were generated by on-site mutations using CRISPR/Cas9. • PpgaX-hph reporter strain allowed for the screening of functional GaaR-XlnR mutants. • Chimeric GaaR-XlnR induced pectinolytic activities in the presence of D-xylose.

Dataset Information

Machine learning prediction of novel pectinolytic enzymes in Aspergillus niger through integrating heterogeneous (post-) genomics data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets