Data-Dependent Scoring Parameter Optimization in MS-GF+ Using Spectrum Quality Filter

Most database search tools for proteomics
have their own scoring
parameter sets depending on experimental conditions such as fragmentation
methods, instruments, digestion enzymes, and so on. These scoring
parameter sets are usually predefined by tool developers and cannot
be modified by users. The number of different experimental conditions
grows as the technology develops, and the given set of scoring parameters
could be suboptimal for tandem mass spectrometry data acquired using
new sample preparation or fragmentation methods. Here we introduce
a new approach to optimize scoring parameters in a data-dependent
manner using a spectrum quality filter. The new approach conducts
a preliminary search for the spectra selected by the spectrum quality
filter. Search results from the preliminary search are used to generate
data-dependent scoring parameters; then, the full search over the
entire input spectra is conducted using the learned scoring parameters.
We show that the new approach yields more and better peptide-spectrum
matches than the conventional search using built-in scoring parameters
when compared at the same 1% false discovery rate.