Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
J Am Soc Mass Spectrom. 2017 Apr;28(4):655-663. doi: 10.1007/s13361-016-1569-8. Epub 2017 Jan 12.
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.
微生物的分泌组，即生物量（即植物基材料）降解酶的集合，可以用于发现用于生物燃料生产的工业酶候选物。蛋白质组学方法已被应用于通过比较不同生长条件下整个分泌组的蛋白质表达谱与酶活性来发现新的酶候选物。然而，需要对每个酶候选物进行活性测量，才能对“活性”酶进行有把握的分配，这一点仍有待阐明。为了解决这一挑战，我们开发了一种活性相关的定量蛋白质组学平台（ACPP），该平台使用无标记定量蛋白质组学方法系统地将蛋白质水平的酶活性模式与蛋白质洗脱图谱相关联。ACPP 优化了高性能阴离子交换分离，以有效地分离复杂的蛋白质样品，同时保留酶活性。使用微孔板测定法在连续的级分中检测到的酶活性模式与使用带有相关 R 分数的自定义模式匹配算法的蛋白质洗脱图谱进行交叉相关。ACPP 已成功应用于从黑曲霉分泌组中以多重方式鉴定两种类型的“活性”生物质降解酶（即淀粉水解酶和纤维素水解酶）。通过确定黑曲霉分泌组中 156 种蛋白质的蛋白质洗脱图谱，我们有信心鉴定出 1,4-α-葡萄糖苷酶为主要的“活性”淀粉水解酶（R = 0.96），内切葡聚糖酶为主要的“活性”纤维素水解酶（R = 0.97）。结果表明，ACPP 促进了从复杂蛋白质样品中以高通量、多重和非靶向方式发现生物活性酶。
