FastSpel: A Method for Fast Spectral Library Generation

Recently, several methods have been proposed for predicting peptide MS/MS fragment intensity profiles. These predicted profiles may be used for generating spectral libraries for data-independent acquisition analysis, or for improving peptide identification by rescoring the peptide-spectra matches identified by search engines such as MaxQuant. Although some of the proposed intensity prediction methods generate high quality spectral libraries and significantly improve peptide identification, they are computationally expensive and their parameters are difficult to interpret. In this paper, we introduce FastSpel (fast spectral library), a fast and interpretable fragment intensity prediction method for tryptic peptides. Testing FastSpel on 23 independent data sets, we show that its performance, in terms of improving peptide identification via rescoring and spectral library generation, is comparable with those of the existing state-of-the-art methods, while being over 2 orders of magnitude less computationally expensive than these methods. Moreover, analysis of parameters of the model corroborates known fragmentation rules, such as the “proline effect”, and suggests novel patterns. In addition to FastSpel, we propose a simple scoring function that achieves rescoring/identification performance close to that of Percolator, a widely used program for this purpose, without requiring model training as Percolator does.