UniSpec: A Deep Learning Approach for Predicting Energy-Sensitive Peptide Tandem Mass Spectra and Generating Proteomics-Wide In-Silico Spectral Libraries

Abstract

In this report, we present UniSpec, an attention-based deep neural network designed to predict complete collision-induced fragmentation of tryptic peptides, aimed at enhancing peptide and protein identification in shotgun proteomics studies. We preprocessed spectral data from peptide tandem mass spectral libraries, compiled by the National Institute of Standards and Technology (NIST), utilizing a data approach tailored for model development, resulting in high-quality, energy-consistent spectral datasets. By analyzing all the annotated fragment ions present in these libraries, we constructed an extensive peptide fragment dictionary containing 7919 isotopic ions from sequence ions, neutral loss, internal, iminium, and amino acid fragment ions. The streamlined dictionary-based spectral training data enables UniSpec to efficiently learn the complex intensity patterns of various product ions, resulting in reliable spectral predictions for a wide range of unmodified and modified peptides. We evaluated the models accuracy by comparing its performance across training and testing data, considering diverse peptide characteristics like peptide classes, charge states, and sequence lengths. Our model attained a median cosine similarity score of 0.951 and 0.923 on the training and test data respectively. Contrary to existing deep learning models that often overlook a substantial part of peptide tandem mass spectra beyond the sequence b and y ion series, UniSpec can predict up to 75% of all measured fragment intensities (including unknown signals) in the raw experimental spectra. This represents a marked advancement from the 43.5% coverage achieved solely by b and y sequence ions in the NIST library spectra. For the evaluation of our models practical utility in predicting proteome-wide in-silico spectral libraries, we executed a benchmark test using a dataset of HeLa cells. UniSpec displayed a significant overlap of peptide identifications with the widely used search engine MS-GF+ and the NIST experimental spectral library, demonstrating its robust performance as a standalone peptide identification tool.