Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach

Xing Wan*, Sazzad Shahrear, Shea Wen Chew, Francisco Vilaplana, Miia R. Mäkelä*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Background: Laccases can oxidize a broad spectrum of substrates, offering promising applications in various sectors, such as bioremediation, biomass fractionation in future biorefineries, and synthesis of biochemicals and biopolymers. However, laccase discovery and optimization with a desirable pH optimum remains a challenge due to the labor-intensive and time-consuming nature of the traditional laboratory methods. 

Results: This study presents a machine learning (ML)-integrated approach for predicting pH optima of basidiomycete fungal laccases, utilizing a small, curated dataset against a vast metagenomic data. Comparative computational analyses unveiled the structural and pH-dependent solubility differences between acidic and neutral-alkaline laccases, helping us understand the molecular bases of enzyme pH optimum. The pH profiling of the two ML-predicted alkaline laccase candidates from the basidiomycete fungus Lepista nuda further validated our computational approach, showing the accuracy of this comprehensive method. 

Conclusions: This study uncovers the efficacy of ML in the prediction of enzyme pH optimum from minimal datasets, marking a significant step towards harnessing computational tools for systematic screening of enzymes for biotechnology applications. Graphical Abstract: (Figure presented.)

Original languageEnglish
Article number120
Number of pages15
JournalBiotechnology for Biofuels and Bioproducts
Volume17
Issue number1
DOIs
Publication statusPublished - 11 Sept 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Alkaline laccase
  • Basidiomycete fungi
  • Machine learning
  • pH optimum
  • Prediction

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