Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Unambiguous Phosphosite Localization through the Combination of Trypsin and LysargiNase Mirror Spectra in a Large-Scale Phosphoproteome Study.

Understanding of the kinase-guided signaling pathways requires the identification and analysis of phosphosites. Mass spectrometry (MS)-based phosphoproteomics is a rapid and highly sensitive approach for high-throughput identification of phosphosites. However, phosphosite determination from MS data with a single protease is more likely to be ambiguous, regardless of the strategy used for phosphopeptide detection. Here, we explored the application of LysargiNase, which was recently reported to mirror trypsin in specificity to cleave arginine and lysine residues exclusively at the N-terminal side. We found that the combination of trypsin and LysargiNase mirror spectra resulted in higher ion coverage in MS2 spectra. The median ion coverage values of b ions in tryptic spectra, LysargiNase spectra, and combined spectra are 8.3, 20.5, and 25.0%, respectively. As for the median ion coverage of y ions, these values are 27.8, 10.0, and 32.3%. Higher ion coverage was helpful to pinpoint the precise phosphosites. Compared to trypsin alone, the combined use of trypsin and LysargiNase mirror spectra enabled 67.1% of mirror spectra with unreliable scores (confidence score <0.75) to become reliable (confidence score ≥ 0.75). Meanwhile, all of the mirror peptide-spectrum matches (PSMs) with multiple potential phosphosites from trypsin and LysargiNase digests could be assigned one precise phosphosite after applying the combination strategy. Besides, the combination strategy could identify more novel phosphosites than the union strategy did. We synthesized three phosphopeptides corresponding to the three novel phosphosites and validated the reliability of the identification. Taken together, our data demonstrated the distinctive potential of the combination strategy presented here for unambiguous phosphosite localization (Project accession PXD011178).

Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer.

Confident identification of sites of protein phosphorylation by mass spectrometry (MS) is essential to advance understanding of phosphorylation-mediated signaling events. However, the development of novel instrumentation requires that methods for MS data acquisition and its interrogation be evaluated and optimized for high-throughput phosphoproteomics. Here we compare and contrast eight MS acquisition methods on the novel tribrid Orbitrap Fusion MS platform using both a synthetic phosphopeptide library and a complex phosphopeptide-enriched cell lysate. In addition to evaluating multiple fragmentation regimes (HCD, EThcD, and neutral-loss-triggered ET(ca/hc)D) and analyzers for MS/MS (orbitrap (OT) versus ion trap (IT)), we also compare two commonly used bioinformatics platforms, Andromeda with PTM-score, and MASCOT with ptmRS for confident phosphopeptide identification and, crucially, phosphosite localization. Our findings demonstrate that optimal phosphosite identification is achieved using HCD fragmentation and high-resolution orbitrap-based MS/MS analysis, employing MASCOT/ptmRS for data interrogation. Although EThcD is optimal for confident site localization for a given PSM, the increased duty cycle compared with HCD compromises the numbers of phosphosites identified. Finally, our data highlight that a charge-state-dependent fragmentation regime and a multiple algorithm search strategy are likely to be of benefit for confident large-scale phosphosite localization.