Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching

A key analysis task in mass spectrometry proteomics is
matching
the acquired tandem mass spectra to their originating peptides by
sequence database searching or spectral library searching. Machine
learning is an increasingly popular postprocessing approach to maximize
the number of confident spectrum identifications that can be obtained
at a given false discovery rate threshold. Here, we have integrated
semisupervised machine learning in the ANN-SoLo tool, an efficient
spectral library search engine that is optimized for open modification
searching to identify peptides with any type of post-translational
modification. We show that machine learning rescoring boosts the number
of spectra that can be identified for both standard searching and
open searching, and we provide insights into relevant spectrum characteristics
harnessed by the machine learning model. The semisupervised machine
learning functionality has now been fully integrated into ANN-SoLo,
which is available as open source under the permissive Apache 2.0
license on GitHub at https://github.com/bittremieux/ANN-SoLo.