Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Wilhelm, Mathias; Zolg, Daniel Paul; Graber, Michael; Gessulat, Siegfried; Schmidt, Tobias; Schnatbaum, Karsten; Schwencke-Westphal, Celina; Seifert, Philipp; Zerweck, Johannes; Knaute, Tobias; Bräunlein, Eva; Samaras, Patroklos; Lautenbacher, Ludwig; Klaeger, Susan; Wenschuh, Holger; Rad, Roland; Delanghe, Bernard; Hühmer, Andreas; Carr, Steven A.; Clauser, Karl R.; Krackhardt, Angela M.; Reimer, Ulf; Küster, Bernhard

doi:10.1038/s41467-021-24263-w

Cited by 5 publications

(3 citation statements)

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“…Examples of this approach are Prosit and pDeep. − It has been demonstrated that in silico predicted spectral libraries using this approach rival experimental libraries and outperform libraries generated by simple rule-based theoretical spectrum predictions, such as those used in database search engines . Predicted spectra from such models were also useful for rescoring and validating identifications found by database search engines . Another prediction approach is to relax the limitation on what fragment ions to expect and predict all possible m / z intensity value pairs.…”

Section: Introductionmentioning

confidence: 99%

“…10 Predicted spectra from such models were also useful for rescoring and validating identifications found by database search engines. 11 Another prediction approach is to relax the limitation on what fragment ions to expect and predict all possible m/z intensity value pairs. Also termed full-spectrum prediction, this approach considers nonbackbone ions and achieves higher similarity between the predicted and the corresponding experimental spectrum, reaching comparable similarity as those among experimental replicates.…”

Section: ■ Introductionmentioning

confidence: 99%

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Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Chan,

Lam

2023

J. Proteome Res.

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Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and searching against such libraries has been shown to improve the sensitivity in peptide identification over conventional sequence database searching. However, current prediction models lack support for longer peptides, and thus far, predicted library searching has only been demonstrated for backbone ion-only spectrum prediction methods. Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. We demonstrated the superiority of using full-spectrum libraries over backbone ion-only prediction approaches in spectral library searching. Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. We also show that a hybrid library combining predicted and experimental spectra can lead to 20% more confident identifications over experimental library searching or sequence database searching.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Chan,

Lam

2023

J. Proteome Res.

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“…For this, Percolator considers a set of features that describe each PSM and uses these features as well as the annotation of target and decoy PSMs in an iterative process to rerank PSMs using a new score and associated q -value. Yet, although Percolator is commonly used in database , searches, its use has thus far not been reported for spectral library searching.…”

Section: Introductionmentioning

confidence: 99%

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator

et al. 2022

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Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.

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Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Aparicio,

Theunissen,

Hervas-Stubbs

et al. 2024

Human Vaccines & Immunotherapeutics

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Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

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Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Cited by 5 publications

References 0 publications

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

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