Data‐Independent Acquisition Enhancement of a Competitive Activity‐Based Protein Profiling Platform for Kinase Inhibitor Screening

Kinase inhibitors represent a vital class of therapeutic agents widely used in cancer research, immunology, and other disease areas. Mass spectrometry (MS) employing specially designed small-molecule kinase-binding probes has become an essential strategy for identifying novel kinase drug targets. While traditional MS approaches often rely on targeted proteomics (e.g., multiple reaction monitoring [MRM]) or data-dependent acquisition (DDA), data-independent acquisition (DIA) offers broader and more reproducible quantification, especially for low-abundance peptides. In this study, we systematically developed an activity-based protein profiling (ABPP) platform leveraging DIA, through integrated in-house informatics tools for data filtering and motif analysis, to provide an effective kinase profiling workflow. Compared to DDA, the DIA approach yielded more than a 100% increase in identified biotinylated peptides and over 40% improvement in kinase peptide coverage, while reducing the analysis time by half (90 min vs. 180 min per sample). Additionally, there was a modest improvement to the coefficient of variation (CV) in kinase peptide quantification (decrease from 11.41% to 10.70%; mean CV). Shorter liquid chromatography (LC) gradient times (60, 45, and 30 min) were evaluated as a means for increasing sample analysis throughput. Notably, no significant loss in kinase peptide coverage was observed due to shorter gradients, highlighting the capability of DIA to significantly enhance the efficiency and scalability of kinase profiling workflows.