Optimum collision energies for proteomics: The impact of ion mobility separation

Nagy, Krisztina; Gellén, Gabriella; Papp, Dávid; Schlosser, Gitta; Révész, Ágnes

doi:10.1002/jms.4957

Cited by 5 publications

(4 citation statements)

References 88 publications

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“…It is important to note that the applied collision energy has a profound impact on the information content of the obtained MS/MS spectra 24 (Supplementary Fig. S1).…”

Section: Resultsmentioning

confidence: 99%

“…It is important to note that the applied collision energy has a profound impact on the information content of the obtained MS/MS spectra 24 . Thus, collision energy calibration is needed for accurate fragment ion intensity predictions.…”

Section: Discussionmentioning

confidence: 99%

“…This kinetic energy can be transferred to internal energy, similarly to what takes place during the activation of ions in collision-induced dissociation. Because IMS energizes the peptides significantly, the use of lower collision energies is advised 24 . Similarly to what has been observed for retention time alignment 42 , we expect a benefit from collision energy alignment to account for the run-to-run fluctuations.…”

Section: Discussionmentioning

confidence: 99%

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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Adams

Gabriel

Laukens

et al. 2023

Preprint

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Immunopeptidomics plays a crucial role in identifying targets for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within HLA class-specific length restrictions needs to be considered. This leads to an inflation of the search space and results in lower spectrum annotation rates. Rescoring is a powerful enhancement of standard sequence database searching that boosts the spectrum annotation performance. In the field of immunopeptidomics low abundant peptides often occur, which is why the highly sensitive timsTOF instruments are increasingly gaining popularity. To improve rescoring for immunopeptides measured using timsTOF instruments, we trained a deep learning-based fragment ion intensity prediction model. Over 300,000 synthesized non-tryptic peptides from the ProteomeTools project were analyzed on a timsTOF-Pro to generate a dataset that was used to fine-tune an existing Prosit model. By applying our fragment ion intensity prediction model, we demonstrate up to 3-fold improvement in the identification of immunopeptides. Furthermore, our approach increased detection of immunopeptides even from low input samples.

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“…It is important to note that the applied collision energy has a profound impact on the information content of the obtained MS/MS spectra 24 (Supplementary Fig. S1).…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Adams

Gabriel

Laukens

et al. 2023

Preprint

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“…Thus, via extended IM separations, cIM has potential to offer improved LC-MS peak capacity in bottom-up LC-MS applications such as HDX-MS. The design, operation, and multifunctional capabilities of cIM have been described in detail. , A handful of publications have now used the SELECT SERIES Cyclic IMS with cIM technology for the purpose of increasing LC-MS peak capacity during peptide mapping and other omics applications. − Despite this, to date, no systematic evaluation of cIM in HDX-MS has been performed nor has its multipass functionality been utilized online in any bottom-up LC-MS or omics application; only one-pass routines have previously been reported.…”

Section: Introductionmentioning

confidence: 99%

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications

Griffiths,

Anderson,

Richardson

et al. 2024

Anal. Chem.

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Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful tool to probe protein dynamics. As a bottom-up technique, HDX-MS provides information at peptide-level resolution, allowing structural localization of dynamic changes. Consequently, the HDX-MS data quality is largely determined by the number of peptides that are identified and monitored after deuteration. Integration of ion mobility (IM) into HDX-MS workflows has been shown to increase the data quality by providing an orthogonal mode of peptide ion separation in the gas phase. This is of critical importance for challenging targets such as integral membrane proteins (IMPs), which often suffer from low sequence coverage or redundancy in HDX-MS analyses. The increasing complexity of samples being investigated by HDX-MS, such as membrane mimetic reconstituted and in vivo IMPs, has generated need for instrumentation with greater resolving power. Recently, Giles et al. developed cyclic ion mobility (cIM), an IM device with racetrack geometry that enables scalable, multipass IM separations. Using one-pass and multipass cIM routines, we use the recently commercialized SELECT SERIES Cyclic IM spectrometer for HDX-MS analyses of four detergent solubilized IMP samples and report its enhanced performance. Furthermore, we develop a novel processing strategy capable of better handling multipass cIM data. Interestingly, use of one-pass and multipass cIM routines produced unique peptide populations, with their combined peptide output being 31 to 222% higher than previous generation SYNAPT G2-Si instrumentation. Thus, we propose a novel HDX-MS workflow with integrated cIM that has the potential to enable the analysis of more complex systems with greater accuracy and speed.

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Machine learning‐based peptide‐spectrum match rescoring opens up the immunopeptidome

Adams,

Laukens,

Bittremieux

et al. 2023

Proteomics

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Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non‐tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post‐translational modifications. This inflation in search space leads to an increase in random high‐scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide‐spectrum match rescoring has emerged as a machine learning‐based solution to address challenges in mass spectrometry‐based immunopeptidomics data analysis. It involves post‐processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide‐spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide‐spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

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Optimum collision energies for proteomics: The impact of ion mobility separation

Cited by 5 publications

References 88 publications

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications

Machine learning‐based peptide‐spectrum match rescoring opens up the immunopeptidome

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