A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.

ABSTRACT: Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and degradation kinetics. Previous multiplex-DIA-MS workflows have employed various strategies including leveraging the highest isotopic labeling channels to enhance the detection of isotopic signal pairs. Here we present a robust workflow that integrates a machine learning algorithm and channel-specific statistical filtering, enabling dynamic adaptation to channel ratio changes across multiplexed experiments and enhancing both coverage and accuracy of protein turnover profiling. We also introduce KdeggeR, a data analysis tool optimized for pSILAC-D

SUBMITTER: Salovska B 

PROVIDER: S-EPMC12125295 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature