Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant

Abstract

<jats:p>Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (~30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (~10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis.</jats:p>

Deep learning the collisional cross sections of the peptide universe from a million experimental values

“…With this re-calibration, CCS values essentially become intrinsic properties of a molecule—meaning they do not depend on external circumstances—similar to their molecular weights, and unlike their retention times. In this regard, we note ongoing research on minimizing ion heating effects in TIMS measurements, as this may also influence the observed cross section or result in fragmentation before MS/MS, depending on instrument settings and space-charge effects 61 – 64 . However, results presented here and in other studies 15 , 22 , 65 , 66 indicate that TIMS CCS values are generally in excellent agreement with the current gold-standard drift tube ion mobility.…”

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

“…Here, we propose a supervised semi-parametric approach to control the FDR of MBR, extending our earlier work on FDR for protein identification (3, 19) and DIA quantification (20). We implement FDR-controlled MBR in IonQuant (21), which has been extended to support LC-MS data both with and without ion mobility. We also implement a new protein abundance calculation module in IonQuant, based on the MaxLFQ strategy (13), improving upon our previously described top-N approach.…”

“…S. cerevisiae peptides transferred to the 20 H. sapiens -only runs by MBR are false positives and were used to evaluate the false positive rate. We also employed two datasets from timsTOF Pro (Bruker), as in our previous work (21). A HeLa dataset with 4 replicate injections from Meier et al (22) was used to evaluate the sensitivity (i.e., quantified protein count) and precision (i.e., coefficient of variation (CV)) of quantification across replicate runs.…”

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

“…In a first, optional step provided DDA raw MS measurements (Thermo Raw vendor format) are converted to the open, XML-based mzML format 32 . Next, the library is generated using EasyPQP (available at https://github.com/grosenberger/easypqp) which matches the provided search results (for example in pepXML format) and the corresponding DDA raw measurements to annotate and store peptide transitions and their properties in a tab-separated table 33 . The library is transformed into an assay containing a specified number of transitions of band y-ions falling into a custom mass-to-charge range.…”