Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning

ABSTRACT: In this study, we extended the ProteomeTools peptide library (PROPEL, see PXD004732 and PXD010595) to train a deep neural network resulting in chromatographic retention time and fragment ion intensity predictions for (tryptic) peptides that exceed the quality of the experimental data.

INSTRUMENT(S): Orbitrap Fusion ETD

ORGANISM(S): Homo Sapiens (human) Escherichia Coli Caenorhabditis Elegans Saccharomyces Cerevisiae (baker's Yeast) Drosophila Melanogaster (fruit Fly)

SUBMITTER: Tobias Schmidt

LAB HEAD: Bernhard Kuster

PROVIDER: PXD010871 | Pride | 2019-05-30

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	02404_F02_P025829_B00_A00_R1.raw	Raw
	02405_C04_P025938_B00_A00_R1.raw	Raw
	02405_E01_P025916_B00_A00_R1.raw	Raw
	02405_E08_P025972_B00_A00_R1.raw	Raw
	02406_D01_P026011_B00_A00_R1.raw	Raw

Items per page:

1 - 5 of 12

Publications

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

Gessulat Siegfried S Schmidt Tobias T Zolg Daniel Paul DP Samaras Patroklos P Schnatbaum Karsten K Zerweck Johannes J Knaute Tobias T Rechenberger Julia J Delanghe Bernard B Huhmer Andreas A Reimer Ulf U Ehrlich Hans-Christian HC Aiche Stephan S Kuster Bernhard B Wilhelm Mathias M

Nature methods 20190527 6

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, r ...[more]

PMID: 31133760

Similar Datasets

Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning

Project description:In this study, we extended the ProteomeTools peptide library (PROPEL, see PXD004732 and PXD010595) to train a deep neural network resulting in chromatographic retention time and fragment ion intensity predictions for (tryptic) peptides that exceed the quality of the experimental data.

2021-03-12 | MSV000087047 | MassIVE

ProteomeTools – Part II - Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Project description:"The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the second iteration of the generation and multimodal LC-MS/MS analysis of >220,000 synthetic tryptic peptides representing nearly all canonical human gene products. This resource will be extended to 1.4 million peptides and all data will be made available to the public in ProteomicsDB."

2019-05-21 | PXD010595 | Pride

Deep learning boosts immunopeptidomics one mass spectrum at a time

Project description:Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune oncology. Still, the identification of such non-tryptic peptides presents substantial computational challenges. To address these, we synthesized >300,000 peptides within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN and analyzed these by multi-modal LC-MS/MS. The resulting data enabled training of a single model using the deep learning framework Prosit that shows outstanding prediction accuracy of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved by 50-300% on average, that proteasomal HLA peptide splicing may not exist and that additional neo-epitopes that elicit an immune response can be identified from patient tumors. Together, the provided peptides, spectra and computational tools substantially expand the scope of immunopeptidomics workflows.

2021-04-26 | PXD021398 | Pride

ProteomeTools â?? Part II - Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

2020-07-21 | MSV000085805 | MassIVE

ProteomeTools Part IV - TMT labeled non-modified tryptic peptides

2021-01-11 | PXD023119 | Pride

ProteomeTools Part V - TMT labeled non-modified non-tryptic peptides

2021-04-26 | PXD023120 | Pride

ProteomeTools - Building ProteomeTools based on a complete synthetic human proteome

Project description:The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the generation and multimodal LC-MS/MS analysis of >350,000 synthetic tryptic peptides representing nearly all canonical human gene products. This resource will be extended to 1.4 million peptides within two years and all data will be made available to the public in ProteomicsDB.

2017-02-09 | PXD004732 | Pride

ProteomeTools – Part III - HLA Class I & II & non-tryptic peptides

Project description:The ProteomeTools project aims to derive molecular and digital tools from the human proteome to facilitate biomedical and life science research. Here, we describe the third iteration of the generation and multimodal LC-MS/MS analysis of >305,000 synthetic non-tryptic peptides representing HLA Class I & II ligands as well as peptides derived from the N-terminal proteases LysN and ApsN. This resource will be extended to 1.4 million peptides and all data will be made available to the public in ProteomicsDB.

2021-04-26 | PXD021013 | Pride

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities.

Project description:Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthermore, the implementation of Transformers within bioinformatics has become relatively convenient due to transfer learning, i.e., adapting a network trained for other tasks to new functionality. Transfer learning makes these relatively large networks more accessible as it generally requires less data, and the training time improves substantially. We implemented a Transformer based on the pretrained model TAPE to predict MS2 intensities. TAPE is a general model trained to predict missing residues from protein sequences. Despite being trained for a different task, we could modify its behavior by adding a prediction head at the end of the TAPE model and fine-tune it using the spectrum intensity from the training set to the well-known predictor Prosit. We demonstrate that the predictor, which we call Prosit Transformer, outperforms the recurrent neural-network-based predictor Prosit, increasing the median angular similarity on its hold-out set from 0.908 to 0.929. We believe that Transformers will significantly increase prediction accuracy for other types of predictions within MS-based proteomics.

| S-EPMC9087333 | biostudies-literature

PROCAL: A set of 40 peptide standards for retention time indexing, column performance monitoring and collision energy calibration

Project description:We describe PROCAL (ProteomeTools Standard), a set of 40 synthetic peptides that span the entire hydrophobicity range of tryptic digests, enabling not only accurate determination of retention time indices but also monitoring of chromatographic separation performance over time. The fragmentation characteristics of the peptides can also be used to calibrate and compare collision energies between mass spectrometers. The sequences of all selected peptides do not occur in any natural protein, thus eliminating the need for stable isotope labeling and allowing straightforward integration into standard peptide identification strategies employing database searching or spectral library matching.

2017-09-07 | PXD006832 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data