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Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Targeted and semitargeted mass spectrometry-based approaches are reliable methods to consistently detect and quantify low abundance proteins including proteins of clinical significance. Despite their potential, the development of targeted and semitargeted assays is time-consuming and often requires the purchase of costly libraries of synthetic peptides. To improve the efficiency of this rate-limiting step, we developed PeptideRanger, a tool to identify peptides from protein of interest with physiochemical properties that make them more likely to be suitable for mass spectrometry analysis. PeptideRanger is a flexible, extensively annotated, and intuitive R package that uses a random forest model trained on a diverse data set of thousands of MS experiments spanning a variety of sample types profiled with different chromatography setups and instruments. To support a variety of applications and to leverage rapidly growing public MS databases, PeptideRanger can readily be retrained with experiment-specific data sets and customized to prioritize and filter peptides based on selected properties.