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
.
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