TY - JOUR
T1 - Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT
AU - Gabriel, Wassim
AU - Giurcoiu, Victor
AU - Lautenbacher, Ludwig
AU - Wilhelm, Mathias
N1 - Publisher Copyright:
© 2022 The Authors. Proteomics published by Wiley-VCH GmbH.
PY - 2022/10
Y1 - 2022/10
N2 - Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3% - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins.
AB - Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3% - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins.
KW - Fragment intensity prediction
KW - Prosit
KW - Retention time prediction
KW - TMTPro
KW - iTRAQ
UR - http://www.scopus.com/inward/record.url?scp=85130482216&partnerID=8YFLogxK
U2 - 10.1002/pmic.202100257
DO - 10.1002/pmic.202100257
M3 - Article
C2 - 35578405
AN - SCOPUS:85130482216
SN - 1615-9853
VL - 22
JO - Proteomics
JF - Proteomics
IS - 19-20
M1 - 2100257
ER -