Abstract
Quantitative proteomic methods require optimization at several stages, including: sample preparation, LC-MS/MS assay and subsequent data analysis, with the final analysis stage being less widely appreciated by end-users. We previously reported abundances of eight uridine-5' diphosphoglucuronosyltransferases (UGTs) generated by two laboratories [using stable isotope-labeled (SIL) peptides or quantitative concatemer (QconCAT)], which reflected significant disparity between proteomic methods. Initial analysis of QconCAT-based proteomic data showed lack of correlation with catalytic activity for several UGTs (1A4, 1A6, 1A9, 2B15), with moderate correlations observed for UGTs 1A1, 1A3 and 2B7 (Rs=0.40-0.79, p<0.05; R2=0.30). Consequently, several criteria were considered in the data analysis workflow, starting from raw data generated by LC-MS/MS, with the aim of improving accuracy, as defined by correlations against UGT-isoform specific catalytic activity. These criteria included: choice of monitored standard peptides and fragments (selected transitions), correction for isotope-label incorporation efficiency, and assessment of abundance normalization using fractional protein mass. Upon optimization, the initially poor UGT abundance-activity correlations improved significantly for six UGT enzymes (Rs=0.53-0.87, p<0.01; R2=0.48-0.73), and UGT1A9 showed an overall moderate correlation (Rs=0.47, p=0.02; R2=0.34). No spurious protein-activity relationships were identified. However, the methods remained sub-optimal for UGT1A3 and UGT1A9; here the hydrophobicity of the standard peptides was believed to be limiting. This report provides a detailed data analysis strategy and indicates, using examples, the significance of systematic data processing following acquisition by LC-MS/MS proteomics. More robust quality control indicates that we now have adequate QconCAT methods useful for quantifying five UGT enzymes.
- drug development/discovery
- enzyme kinetics
- glucuronidation/UDP-glucuronyltransferases/UGT
- in vitro-in vivo prediction (IVIVE)
- proteomics
- The American Society for Pharmacology and Experimental Therapeutics