Meier et al. TIMS


February 18, 2024


Dataset Description

The data consists of 718.917 PSMs.


  • title: Deep learning the collisional cross-sections of the peptide universe from a million experimental values
  • dataset tag: ionmobility/Meier_TIMS
  • data publication: MSP
  • machine learning publication: Nature Communications
  • data source identifier: PXD010012, PXD019086, PXD017703
  • data type: ion mobility
  • format: CSV
  • columns: index, Modified sequence, Charge, Mass, Intensity, Retention time, CCS, PT
  • instrument: maXis, timsTOF Pro,
  • organism: Homo sapiens (Human), Saccharomyces cerevisiae (Baker’s yeast)
  • fixed modifications:
  • variable modification:unmodified & oxidation & acetylation & carbamidomethyl
  • ionmobility type: TIMS
  • css calibration compounds:

Sample Protocol

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, while mass spectra are acquired in about 100 microseconds with time-of-fight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive due to its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation – serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 800,000 fragmentation spectra in standard 120 min LC runs are easily achievable, which can be used for near exhaustive precursor selection in complex mixtures or re-sequencing weak precursors. MaxQuant identified more than 6,000 proteins in single run HeLa analyses without matching to a library, and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,000 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

Data Analysis Protocol

MS raw files were analyzed with MaxQuant version, which extracts 4D isotope patterns (‘features’) and associated MS/MS spectra. The built-in search engine Andromeda74 was used to match observed fragment ions to theoretical peptide fragment ion masses derived from in silico digests of a reference proteome and a list of 245 potential contaminants using the appropriate digestion rules for each proteolytic enzyme (trypsin, LysC or LysN). We allowed a maximum of two missing values and required a minimum sequence length of 7 amino acids while limiting the maximum peptide mass to 4600 Da. Carbamidomethylation of cysteine was defined as a fixed modification, and oxidation of methionine and acetylation of protein N-termini were included in the search as variable modification. Reference proteomes for each organism including isoforms were accessed from UniProt (Homo sapiens: 91,618 entries, 2019/05; E. coli: 4403 entries, 2019/01; C. elegans: 28,403 entries, 2019/01; S. cerevisiae: 6049 entries, 2019/01; D. melanogaster: 23,304 entries, 2019/01). The synthetic peptide library (ProteomeTools54) was searched against the entire human reference proteome. The maximum mass tolerances were set to 20 and 40 ppm for precursor and fragment ions, respectively. False discovery rates were controlled at 1% on both the peptide spectrum match and protein level with a target-decoy approach. The analyses were performed separately for each organism and each set of synthetic peptides (‘proteotypic set’, ‘SRM atlas’, and ‘missing gene set’). To demonstrate the utility of CCS prediction, we re-analyzed three diaPASEF experiments from Meier et al.55 with Spectronaut 14.7.201007.47784 (Biognosys AG), replacing experimental ion mobility values in the spectral library with our predictions. Singly charged peptide precursors were excluded from this analysis as the neural network was exclusively trained with multiply charged peptides.