inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Cormican, John A; Horokhovskyi, Yehor; Soh, Wai Tuck; Mishto, Michele; Liepe, Juliane

doi:10.1016/j.mcpro.2022.100432

Cited by 9 publications

(16 citation statements)

References 62 publications

(96 reference statements)

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“…Among the available measures, the similarity between experimentally observed and predicted fragment intensities has gained particular attention as one of the most important metrics to estimate the quality of a PSM [22]. Data‐driven rescoring increases the number of confidently identified PSMs by a substantial amount which resulted in the development of multiple tools such as MS 2 Rescore [27], Inferys Rescoring [28], Inspire [29], and MSBooster [30]. We have developed Prosit, a neural network for peptide property prediction which is available via a web interface that allows users to perform the rescoring workflow on MaxQuant results or to generate in silico spectral libraries.…”

Section: Mainmentioning

confidence: 99%

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

Picciani,

Gabriel,

Giurcoiu

et al. 2023

Proteomics

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Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data‐independent acquisition (DIA) data analysis to data‐driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state‐of‐the‐art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm‐lab/oktoberfest) and can easily be installed locally through the cross‐platform PyPI Python package.

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Section: Mainmentioning

confidence: 99%

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

Picciani,

Gabriel,

Giurcoiu

et al. 2023

Proteomics

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“…We and others have previously shown that rescoring PSMs using additional e.g. fragment intensity-based scores can be used effectively to separate incorrect from correct matches 20,21,[29][30][31] . However, rather than estimating the confidence of a PSM (i.e.…”

Section: Levenshtein Distance Estimate Improves Psm Scoringmentioning

confidence: 99%

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Klaproth-Andrade

Hingerl

Smith

et al. 2023

Preprint

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Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a new de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a new convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a new peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

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“…Existing rescoring tools mainly differ from each other by their use of distinct feature sets and prediction models. Some PSM rescoring tools use only a few features by default [30][31][32], while others use dozens [33,34] to 100 features [35,36]. Additionally, some tools allow adjusting the number of features used.…”

Section: Common Feature Types Used During Immunopeptide Psm Rescoringmentioning

confidence: 99%

“…Additionally, some tools allow adjusting the number of features used. For example, MSBooster [30] has an option to use correlated features or not, and inSPIRE [33] allows manual feature inclusion and exclusion. Although a minor variation in feature sets that are used will likely have a limited effect on the performance, in light of the diversity in the number and type of features used by various PSM rescoring tools, it is important to strategically select relevant and informative features.…”

Section: Common Feature Types Used During Immunopeptide Psm Rescoringmentioning

confidence: 99%

“…Another possible spectral feature is the Prosit‐delta score. This feature, used by inSPIRE [33], represents the stability of the SA after two amino acid positions are switched and is conceptually related to the ΔXCorr score originally used by Percolator [23]. Sequences with a low Prosit‐delta are more likely to be misassigned to similar, though incorrect peptide sequences.…”

Section: Common Feature Types Used During Immunopeptide Psm Rescoringmentioning

confidence: 99%

See 1 more Smart Citation

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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inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Cited by 9 publications

References 62 publications

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

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

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