Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

Abstract

MS²PIP is a data-driven tool that accurately predicts peak intensities for a given peptide's fragmentation mass spectrum. Since the release of the MS²PIP web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run MS²PIP, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS²PIP web server is freely available at https://iomics.ugent.be/ms2pip/.

The use of hybrid data-dependent and -independent acquisition spectral libraries empower dual-proteome profiling

“…Besides the use of DDA-based spectral libraries, an alternative way of creating spectral libraries is by using algorithms that accurately predict MS2 fragmentation spectra such as MS 2 PIP [13] and Prosit [14]. MS 2 PIP (MS2 peak intensity prediction) is a data-driven tool [13] that is trained on different types of public DDA data to predict MS2 peak intensities [15]. Prosit [14] on the other hand is trained on MS2 spectra of synthetic peptides and mass spectrometry data generated in the context of the ProteomeTools project, which has the overall aim to provide a high-quality reference MS2 data of synthetic peptides [16].…”

Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra

“…Thus, the targeted search for DDA precursor fragments does not take full advantage of resulting digital records of all ions in scans generated in a data-independent manner (Gillet et al , 2012) . Recent approaches based on large synthetic peptide libraries enable accurate prediction of peptide spectra directly from sequence data (Gessulat et al , 2019;Gabriels et al , 2019) . Computational approaches that utilize MS1 -MS2 co-elution information to generate pseudo-spectra do not require creating experimental libraries (Tsou et al , 2015;Wang et al , 2015;Li et al , 2015) .…”

Lost and found: re-searching and re-scoring proteomics data aids the discovery of bacterial proteins and improves proteome coverage

“…In addition, the deviation of the predicted peptide retention time (RT) serves as a useful scoring feature ). Next to predicting the RT for a given peptide, algorithms were recently introduced that predict the intensity of peptide fragment ions with unprecedented accuracy (Degroeve and Martens 2013;Gabriels et al 2019;Gessulat et al 2019;Tiwary et al 2019).…”