A de novo MS1 feature detector for the Bruker timsTOF Pro

1Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptides fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursors mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptides peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706). 2 Author SummaryIn the identification of peptides in mass spectrometry data, much attention has been given to the targeting and extraction of mass spectra produced by fragmentation of precursor ions. However, important information about the peptide is revealed by the data representing the precursor ion itself, such as the peptides charge state, mass-to-charge ratio, intensity, and retention time. The timsTOF produces the additional dimension of ion mobility, which provides richer information about the precursor. Although tools exist for the analysis of timsTOF data, they are hampered by limited dynamic range. In this work, we describe a de novo feature detector called 3DID that detects peptide features across the full mass range. Our detector can detect more peptides than existing tools across a broader range of abundance, which enables more comprehensive analysis of the data. We believe 3DID will make a valuable contribution to the proteomics toolbox.