title: Finding Biomass Degrading Enzymes Through an Activity-Correlated Quantitative Proteomics Platform (ACPP).
abstract: The microbial secretome, known as a pool of biomass (i.e., plant-based materials) degrading enzymes, can be utilized to discover industrial enzyme candidates for biofuel production. Proteomics approaches have been applied to discover novel enzyme candidates through comparing protein expression profiles with enzyme activity of the whole secretome under different growth conditions. However, the activity measurement of each enzyme candidate is needed for confident "active" enzyme assignments, which remains to be elucidated. To address this challenge, we have developed an Activity-Correlated Quantitative Proteomics Platform (ACPP) that systematically correlates protein-level enzymatic activity patterns and protein elution profiles using a label-free quantitative proteomics approach. The ACPP optimized a high performance anion exchange separation for efficiently fractionating complex protein samples while preserving enzymatic activities. The detected enzymatic activity patterns in sequential fractions using microplate-based assays were cross-correlated with protein elution profiles using a customized pattern-matching algorithm with a correlation R-score. The ACPP has been successfully applied to the identification of two types of "active" biomass-degrading enzymes (i.e., starch hydrolysis enzymes and cellulose hydrolysis enzymes) from Aspergillus niger secretome in a multiplexed fashion. By determining protein elution profiles of 156 proteins in A. niger secretome, we confidently identified the 1,4-α-glucosidase as the major "active" starch hydrolysis enzyme (R = 0.96) and the endoglucanase as the major "active" cellulose hydrolysis enzyme (R = 0.97). The results demonstrated that the ACPP facilitated the discovery of bioactive enzymes from complex protein samples in a high-throughput, multiplexing, and untargeted fashion. Graphical Abstract ᅟ.
authors.affiliation: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
authors.forename: Hongyan
authors.initials: H
authors.lastname: Ma
authors.affiliation: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Delafield
authors.affiliation: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
authors.forename: Jianlan
authors.initials: J
authors.lastname: You
authors.affiliation: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA. si.wu@ou.edu.
authors.forename: Si
authors.identifier: 0000-0002-6346-7359
authors.initials: S
authors.lastname: Wu
mesh_terms: D001234:Aspergillus niger / Q000201:enzymology* / Q000378:metabolism; D018533:Biomass; D002482:Cellulose / Q000378:metabolism*; D005656:Fungal Proteins / Q000378:metabolism*; D006868:Hydrolysis; D040901:Proteomics / Q000379:methods*; D000520:alpha-Glucosidases / Q000378:metabolism*
chemical_list: D005656:Fungal Proteins; D002482:Cellulose; D000520:alpha-Glucosidases
keywords: Correlation; Enzyme activity; LC-MS/MS; Peptide counting; Proteomics