Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

ABSTRACT MotivationIdentification of peptides in data-independent acquisition (DIA) mass spectrometry (MS) typically relies on the scoring for the peak groups upon extracted chromatograms of fragment ions. Expanding fragment scoring features closer to the genuine experimental spectra can improve DIA identification. Deep learning is able to predict fragment presence without understanding the fragmentation mechanism that can enrich the scoring features in DIA identification. ResultsIn this work, we developed a deep neural network-based model, Alpha-Frag, to predict the fragment ions that should be present for a given peptide by reporting their probabilities of existence. The prediction performance was evaluated in terms of intersection over union (IoU), and Alpha-Frag achieved an average of >0.7 and outperformed substantially the benchmarks across the validation datasets. Furthermore, qualitative scores based on Alpha-Frag were designed and incorporated into the peptide statistical validation tools as auxiliary scores. Our preliminary experiments show that the qualitative scores by Alpha-Frag are profitable for DIA identification, especially in the case of short gradient, and yielded an increase of 10.1%-29.3% improvements for the test dataset compared to the same scoring strategy but using Prosit. Availability and ImplementationSource code and the trained model are available at www.github.com/YuAirLab/Alpha-Frag.