Removal of isobaric interference using pseudo‐multiple reaction monitoring and energy‐resolved mass spectrometry for the isotope dilution quantification of a tryptic peptide

Maroto, Alicia; dit Fouque, Dany Jeanne; Lartia, Rémy; Memboeuf, Antony

doi:10.1002/jms.5025

Cited by 2 publications

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Abdul-Khalek,

Picciani,

Wimmer

et al. 2024

Preprint

View full text Add to dashboard Cite

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the model's applicability across various research fields. The model's architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.

show abstract

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Abdul-Khalek,

Picciani,

Wimmer

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Energy-Resolved Mass Spectrometry and Mid-Infrared Spectroscopy for Purity Assessment of a Synthetic Peptide Cyclised by Intramolecular Huisgen Click Chemistry

Maroto,

Boqué,

Jeanne Dit Fouque

et al. 2024

MPs

View full text Add to dashboard Cite

Cyclic peptides have higher stability and better properties as therapeutic agents than their linear peptide analogues. Consequently, intramolecular click chemistry is becoming an increasingly popular method for the synthesis of cyclic peptides from their isomeric linear peptides. However, assessing the purity of these cyclic peptides by mass spectrometry is a significant challenge, as the linear and cyclic peptides have identical masses. In this paper, we have evaluated the analytical capabilities of energy-resolved mass spectrometry (ER MS) and mid-infrared microscopy (IR) to address this challenge. On the one hand, mixtures of both peptides were subjected to collision-induced dissociation tandem mass spectrometry (CID MS/MS) experiments in an ion trap mass spectrometer at several excitation energies. Two different calibration models were used: a univariate model (at a single excitation voltage) and a multivariate model (using multiple excitation voltages). The multivariate model demonstrated slightly enhanced analytical performance, which can be attributed to more effective signal averaging when multiple excitation voltages are considered. On the other hand, IR microscopy was used for the quantification of the relative amount of linear peptide. This was achieved through univariate calibration, based on the absorbance of an alkyne band specific to the linear peptide, and through Partial Least Squares (PLS) multivariate calibration. The PLS calibration model demonstrated superior performance in comparison to univariate calibration, indicating that consideration of the full IR spectrum is preferable to focusing on the specific peak of the linear peptide. The advantage of IR microscopy is that it is linear across the entire working interval, from linear peptide molar ratios of 0 (equivalent to pure cyclic peptide) up to 1 (pure linear peptide). In contrast, the ER MS calibration models exhibited linearity only up to 0.3 linear peptide molar ratio. However, ER MS showed better performances in terms of the limit of detection, intermediate precision and the root-mean-square-error of calibration. Therefore, ER MS is the optimal choice for the detection and quantification of the lowest relative amounts of linear peptides.

show abstract

Removal of isobaric interference using pseudo‐multiple reaction monitoring and energy‐resolved mass spectrometry for the isotope dilution quantification of a tryptic peptide

Cited by 2 publications

References 35 publications

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Energy-Resolved Mass Spectrometry and Mid-Infrared Spectroscopy for Purity Assessment of a Synthetic Peptide Cyclised by Intramolecular Huisgen Click Chemistry

Contact Info

Product

Resources

About