MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics*

The ion mobility enhanced MaxQuant software is introduced, offering an end to end computational workflow for LC-IMS-MS/MS shotgun proteomics data. Highly parallelizable 4D feature detection and de-isotoping algorithms are featured and non-linear multi-dimensional mass recalibration is performed. A new matching between runs (MBR) algorithm that utilizes aligned peptide collisional cross sections (CCS). MS1 level label-free quantification is also implemented providing good results. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org. Graphical Abstract Highlights Highly parallelizable 4D feature detection in ion mobility enhanced shotgun proteomics. Multidimensional non-linear mass, retention time and ion mobility recalibration. Collision cross section aware matching between runs. Label-free quantification of ion mobility MS data. Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

[1]  Melvin A. Park,et al.  High resolution trapped ion mobility spectrometery of peptides. , 2014, Analytical chemistry.

[2]  Perdita Barran,et al.  Ion Mobility Mass Spectrometry. , 2015, The Analyst.

[3]  Ronald J Moore,et al.  An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies. , 2010, Journal of proteome research.

[4]  Yasset Perez-Riverol,et al.  A multi-center study benchmarks software tools for label-free proteome quantification , 2016, Nature Biotechnology.

[5]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[6]  Jürgen Cox,et al.  High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis , 2019, Nature Methods.

[7]  Jüergen Cox,et al.  The MaxQuant computational platform for mass spectrometry-based shotgun proteomics , 2016, Nature Protocols.

[8]  Richard D. Smith,et al.  Proteomic analyses using an accurate mass and time tag strategy. , 2004, BioTechniques.

[9]  Jürgen Cox,et al.  Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data , 2018, Annual Review of Biomedical Data Science.

[10]  Richard D. Smith,et al.  Toward plasma proteome profiling with ion mobility-mass spectrometry. , 2006, Journal of proteome research.

[11]  Marco Y. Hein,et al.  The Perseus computational platform for comprehensive analysis of (prote)omics data , 2016, Nature Methods.

[12]  M. Senko,et al.  Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions , 1995, Journal of the American Society for Mass Spectrometry.

[13]  Jürgen Cox,et al.  MaxQuant goes Linux , 2018, Nature Methods.

[14]  Stefan Tenzer,et al.  Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics , 2013, Nature Methods.

[15]  Jürgen Cox,et al.  MaxQuant for in-depth analysis of large SILAC datasets. , 2014, Methods in molecular biology.

[16]  Marco Y. Hein,et al.  Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ * , 2014, Molecular & Cellular Proteomics.

[17]  Martin Eisenacher,et al.  The PRIDE database and related tools and resources in 2019: improving support for quantification data , 2018, Nucleic Acids Res..

[18]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[19]  Matthias Mann,et al.  Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device. , 2015, Journal of proteome research.

[20]  Mathias Wilhelm,et al.  Ion Mobility Tandem Mass Spectrometry Enhances Performance of Bottom-up Proteomics , 2014, Molecular & Cellular Proteomics.

[21]  Melvin A. Park,et al.  Gas-phase separation using a trapped ion mobility spectrometer , 2011, International journal for ion mobility spectrometry : official publication of the International Society for Ion Mobility Spectrometry.

[22]  Stefka Tyanova,et al.  Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research. , 2018, Methods in molecular biology.

[23]  John A. McLean,et al.  Ion Mobility-Mass Spectrometry: Time-Dispersive Instrumentation , 2014, Analytical chemistry.

[24]  M. Mann,et al.  Software Lock Mass by Two-Dimensional Minimization of Peptide Mass Errors , 2011, Journal of the American Society for Mass Spectrometry.

[25]  Johannes P C Vissers,et al.  Using ion purity scores for enhancing quantitative accuracy and precision in complex proteomics samples , 2012, Analytical and Bioanalytical Chemistry.

[26]  A. Bensadoun,et al.  Assay of proteins in the presence of interfering materials. , 1976, Analytical biochemistry.

[27]  Jürgen Cox,et al.  Computational principles of determining and improving mass precision and accuracy for proteome measurements in an Orbitrap , 2009, Journal of the American Society for Mass Spectrometry.

[28]  Ronald J Moore,et al.  Development and evaluation of a micro- and nanoscale proteomic sample preparation method. , 2005, Journal of proteome research.

[29]  F. Fernandez-Lima,et al.  Note: Integration of trapped ion mobility spectrometry with mass spectrometry. , 2011, The Review of scientific instruments.

[30]  Matthias Mann,et al.  Visualization of LC‐MS/MS proteomics data in MaxQuant , 2015, Proteomics.

[31]  J I Baumbach,et al.  Review on ion mobility spectrometry. Part 2: hyphenated methods and effects of experimental parameters. , 2015, The Analyst.

[32]  Melvin A. Park,et al.  Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer* , 2018, Molecular & Cellular Proteomics.