MS2PIP: a tool for MS/MS peak intensity prediction

Abstract

Motivation: Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research.

Results: We present a new tool called MS(2)PIP for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. MS(2)PIP pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of MS(2)PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool.

Availability: MS(2)PIP code is available for both training and predicting at http://compomics.com/.

Fig. 1.

The distribution of the oob R² prediction performance results for the regression tasks D_clf, with f ∈{b,y}

Fig. 2.

The distribution of the PCC values computed from the b and y ion types (set1) for the evaluation datasets lab1, lab2, lab3, sample1 and sample2