Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.
Keywords: R-software; data-independent acquisition; deep-learning; proteomics; spectral predictions.