Finding Biomass Degrading Enzymes Through an Activity-Correlated Quantitative Proteomics Platform (ACPP)

Area of Science:

• Biochemistry
• Proteomics
• Enzymology

Background:

• Microbial secretomes contain enzymes capable of degrading biomass for biofuel applications.
• Discovering active industrial enzyme candidates requires correlating protein expression with specific enzyme activity.
• Current proteomics methods struggle to definitively identify active enzymes within complex secretomes.

Purpose of the Study:

• To develop and validate an Activity-Correlated Quantitative Proteomics Platform (ACPP) for identifying active enzymes.
• To systematically link enzymatic activity patterns with protein elution profiles in complex samples.
• To enable high-throughput, multiplexed discovery of bioactive biomass-degrading enzymes.

Main Methods:

• Developed the Activity-Correlated Quantitative Proteomics Platform (ACPP) using label-free quantitative proteomics.
• Optimized high-performance anion exchange chromatography for sample fractionation while preserving enzyme activity.
• Cross-correlated enzymatic activity patterns in fractions with protein elution profiles using a pattern-matching algorithm (R-score).

Main Results:

• Successfully applied ACPP to Aspergillus niger secretome, identifying active starch and cellulose hydrolysis enzymes.
• Confidently identified 1,4-α-glucosidase as the major active starch hydrolysis enzyme (R=0.96).
• Confidently identified endoglucanase as the major active cellulose hydrolysis enzyme (R=0.97).

Conclusions:

• The ACPP effectively facilitates the discovery of bioactive enzymes from complex protein mixtures.
• This platform enables untargeted, high-throughput, and multiplexed identification of enzymes.
• ACPP advances the search for novel industrial enzyme candidates for biofuel production and other applications.