Comprehensive Spectral Library from the Pathogenic Bacterium Streptococcus pneumoniae with Focus on Phosphoproteins.
Christian HentschkerSandra MaaßSabryna JunkerMichael HeckerSven HammerschmidtAndreas OttoDörte BecherPublished in: Journal of proteome research (2020)
To understand bacterial reactions to environmental stress or infection-related processes, it is necessary to identify the involved proteins. In mass spectrometry-based proteomics, the method of choice for spectra-to-peptide-match is database search, but in recent times, spectral libraries have come into focus. Here, we built a mass spectral library from Streptococcus pneumoniae D39, reflecting 76% of the theoretical proteome of the organism. Besides the proteins themselves, posttranslational protein modifications especially reveal central hubs of regulation in bacterial pathogenesis. Here, for example, phosphorylation events are involved in the signal transduction and regulation of virulence. Although there have been major advances in phosphoproteomics, identification of this modification is still challenging. To enhance the number of phosphorylated peptides, which can be reproducibly detected, a comprehensive mass spectral library of the S. pneumoniae D39 phosphoproteome has been compiled in addition to the comprehensive total proteome mass spectral library. The phosphopeptide library was manually validated, and the data quality was additionally proven by analyses of synthetic phosphorylated peptides. In total, 128 phosphorylated proteins were revealed, of which many are involved in glycolysis, purine metabolism, protein biosynthesis, and virulence. The publicly available, thoroughly validated spectral libraries are an excellent resource to improve and speed up future investigations on the proteome and phosphoproteome of pneumococci.
Keyphrases
- optical coherence tomography
- mass spectrometry
- dual energy
- escherichia coli
- pseudomonas aeruginosa
- staphylococcus aureus
- amino acid
- computed tomography
- antimicrobial resistance
- big data
- current status
- magnetic resonance imaging
- machine learning
- risk assessment
- cystic fibrosis
- protein kinase
- gene expression
- ms ms
- deep learning
- adverse drug
- artificial intelligence
- genome wide
- stress induced
- dna methylation
- high performance liquid chromatography
- decision making
- life cycle