Mycobacterial Ser/Thr protein kinases (STPKs) play a critical role in signal transduction pathways that ultimately determine mycobacterial growth and metabolic adaptation. Identification of key physiological substrates of these protein kinases is, therefore, crucial to better understand how Ser/Thr phosphorylation contributes to mycobacterial environmental adaptation, including response to stress, cell division, and host-pathogen interactions. Various substrate detection methods have been employed with limited success, with direct targets of STPKs remaining elusive. Recently developed mass spectrometry (MS)-based phosphoproteomic approaches have expanded the list of potential STPK substrate identifications, yet further investigation is required to define the most functionally significant phosphosites and their physiological importance. Prior to the application of MS workflows, for instance, GarA was the only known and validated physiological substrate for protein kinase G (PknG) from pathogenic mycobacteria. A subsequent list of at least 28 candidate PknG substrates has since been reported with the use of MS-based analyses. Herein, we integrate and critically review MS-generated datasets available on novel STPK substrates and report new functional and subcellular localization enrichment analyses on novel candidate protein kinase A (PknA), protein kinase B (PknB) and PknG substrates to deduce the possible physiological roles of these kinases. In addition, we assess substrate specificity patterns across different mycobacterial STPKs by analyzing reported sets of phosphopeptides, in order to determine whether novel motifs or consensus regions exist for mycobacterial Ser/Thr phosphorylation sites. This review focuses on MS-based techniques employed for STPK substrate identification in mycobacteria, while highlighting the advantages and challenges of the various applications.
Keywords: Mass Spectrometry; Mycobacterium tuberculosis; Phosphoproteins; Phosphoproteomics; Phosphorylation; Serine/Threonine Kinases; Substrate Identification.
© 2020 Baros et al.