MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics

Prianichnikov, Nikita; Koch, Heiner; Koch, Scarlet; Lubeck, Markus; Heilig, Raphael; Brehmer, Sven; Fischer, Román; Cox, Jürgen

doi:10.1074/mcp.tir119.001720

Cited by 154 publications

(167 citation statements)

References 33 publications

Supporting

Mentioning

166

Contrasting

Order By: Relevance

“…In total, we compiled 360 LC-MS/MS runs and processed them in the MaxQuant 36,37 software. This resulted in about 2.5 million peptide spectrum matches and 426,845 unique peptide sequences at globally controlled false discovery (FDR) rates of less than 1% at the peptide and protein levels for each organism and enzyme.…”

Section: Construction Of a Very Large Scale Peptide Ccs Data Setmentioning

confidence: 99%

“…For simplification, we here selected only the most abundant feature for each modified peptide sequence and charge state per LC-TIMS-MS run. To account for experimental drifts in the measurements of TIMS CCS values over time, we performed a hierarchical clustering (similar to 37 ) and aligned all experiments by calculating pair-wise linear offsets (y = x+b) going from the closest to the most distant nodes. To perform nearest neighbor analysis in the m/z vs. CCS space we represented the data in a Kd-tree structure using the Tschebyschow distance metric to define a rectangular area with a given mass and CCS tolerance surrounding a node of interest.…”

Section: Liquid Chromatography and Mass Spectrometry (Lc-ms) Lc-ms Wasmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning the collisional cross sections of the peptide universe from a million training samples

Meier

Brunner

et al. 2020

Preprint

View full text Add to dashboard Cite

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation -serial fragmentation (PASEF). The scale and precision (CV <1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R > 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

show abstract

Section: Construction Of a Very Large Scale Peptide Ccs Data Setmentioning

confidence: 99%

Section: Liquid Chromatography and Mass Spectrometry (Lc-ms) Lc-ms Wasmentioning

confidence: 99%

Deep learning the collisional cross sections of the peptide universe from a million training samples

Meier

Brunner

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Peptides and proteins were identified through automated database searching using MaxQuant 34,35 (version 1.6.14.0) in the TIMS-DDA mode against the human database from UniprotKB/Swiss-Prot release 2017/04 with a strict Trypsin/P specificity allowing for up to 2 missed cleavages. Carbamidomethyl (C) was set as a fixed modification.…”

Section: Database Searching and Data Processingmentioning

confidence: 99%

Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry

Ogata

Chang

Ishihama

2020

Preprint

View full text Add to dashboard Cite

The insertion of ion mobility spectrometry (IMS) between LC and MS can improve peptide identification in both proteomics and phosphoproteomics by providing structural information that is complementary to LC and MS, because IMS separates ions on the basis of differences in their shapes and charge states. However, it is necessary to know how phosphate groups affect the peptide collision cross sections (CCS) in order to accurately predict phosphopeptide CCS values and to maximize the usefulness of IMS. In this work, we systematically characterized the CCS values of 4,433 pairs of mono-phosphopeptide and corresponding unphosphorylated peptide ions using trapped ion mobility spectrometry (TIMS). Nearly one-third of the mono-phosphopeptide ions evaluated here showed smaller CCS values than their unphosphorylated counterparts, even though phosphorylation results in a mass increase of 80 Da. Significant changes of CCS upon phosphorylation occurred mainly in structurally extended peptides with large numbers of basic groups, possibly reflecting intramolecular interactions between phosphate and basic groups.

show abstract

“…A current major limitation of the PASEF proteomics method is long post-acquisition analysis time due to the high dimensionality of the data and large number of acquired MS/MS scans. MaxQuant (4,5) and PEAKS (6) are both capable of processing PASEF data but require roughly three hours to perform a standard tryptic search given a raw data file from a two hour gradient. Neither MaxQuant nor PEAKS are practical for nonspecific digest searches or open searches (7,8), which are helpful in discovering post-translational modifications.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the added ion mobility dimension, previously developed quantification tools need to be extended to LC-IMS-MS data. In MaxQuant this is done by slicing a 4-D space (ion mobility, m/z, retention time, and intensity) into multiple 3-D sub-spaces (m/z, retention time, and intensity) and tracing peaks within each sub-space (5). Though MaxQuant only uses every third TOF scan in feature detection, it represents a significant fraction of the overall analysis time.…”

Section: Introductionmentioning

confidence: 99%

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant

Haynes

Teo

et al. 2020

Preprint

View full text Add to dashboard Cite

AbstractIon 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.

show abstract

MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics

Cited by 154 publications

References 33 publications

Deep learning the collisional cross sections of the peptide universe from a million training samples

Deep learning the collisional cross sections of the peptide universe from a million training samples

Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant

Contact Info

Product

Resources

About