Open modification searching of SARS-CoV-2–human protein interaction data reveals novel viral modification sites

Abstract

The outbreak of the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions (PPIs) through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications (PTMs). We have reanalyzed public affinity purification mass spectrometry data using open modification searching to investigate the presence of PTMs in the context of the SARS-CoV-2 virus-host PPI network. Based on an over two-fold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. Additionally, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

“…From this aspect, the rescoring system for PSM using artificial intelligence (AI) prediction nodes has become a more standard technique. In addition to the previously used metrics, mass to charge ration (m/z), retention time (RT) and IM, the additional metrics, such as predicted spectral angle (SA) and the predicted RT, have been introduced to enhance the (immune)peptide identification 57‐59 . These synergic progresses both in MS hardware and the supportive search systems make robust identification possible from the growing large‐scale MS raw data.…”

Machine learning‐based peptide‐spectrum match rescoring opens up the immunopeptidome

“…In this way, model performance can be optimized for specific datasets, even when only a limited amount of training data is available. Transfer learning is used by several deep learning tools [34, 44, 57] and may help to create models better suited for different scenarios, such as finetuning them for different fragmentation mechanisms, instrument platforms, and even lab‐specific data properties [58].…”

“…When new experimental setups are being implemented, prediction models and feature sets will need to be updated and developed. For example, while the initial Prosit fragment ion intensity prediction model was trained on data from Thermo Scientific Orbitrap instruments [43], it was recently optimized to accurately predict fragment ion intensities for timsTOF data as well by finetuning the original model [58]. Similarly, as new instrument platforms are introduced, such as electron‐activated dissociation provided by Sciex ZenoTOF instruments or data from Thermo Scientific Astral instruments, new prediction models need to be developed.…”

“…A challenge with developing prediction models for new experimental settings is that initially only a limited amount of training data might be available. Therefore, if there is still some similarity between the new experimental set‐up and previous models, such as similar fragmentation methods on related instrument platforms, finetuning can be used instead [58]. In contrast, when the two settings are sufficiently different, retraining new prediction models from scratch might be necessary.…”