Phosphorylation is an essential protein modification and is most commonly associated with hydroxyl-containing amino acids via an adenosine triphosphate (ATP) substrate. The last decades have brought greater appreciation to the roles that phosphorylation of myriad amino acids plays in biological signaling, metabolism, and gene transcription. Histidine phosphorylation occurs in both eukaryotes and prokaryotes but has been shown to dominate signaling networks in the latter due to its role in microbial two-component systems. Methods to investigate histidine phosphorylation have lagged behind those to study serine, threonine, and tyrosine modifications due to its inherent instability and the historical view that this protein modification was rare. An important strategy to overcome the reactivity of phosphohistidine is the development of substrate-based probes with altered chemical properties that improve modification longevity but that do not suffer from poor recognition or transfer by the protein. Here, we present combined experimental and computational studies to better understand the molecular requirements for efficient histidine phosphorylation by comparison of the native kinase substrate, ATP, and alkylated ATP derivatives. While recognition of the substrates by the histidine kinases is an important parameter for the formation of phosphohistidine derivatives, reaction sterics also affect the outcome. In addition, we found that stability of the resulting phosphohistidine moieties correlates with the stability of their hydrolysis products, specifically with their free energy in solution. Interestingly, alkylation dramatically affects the stability of the phosphohistidine derivatives at very acidic pH values. These results provide critical mechanistic insights into histidine phosphorylation and will facilitate the design of future probes to study enzymatic histidine phosphorylation.
Bibliographical noteFunding Information:
We thank K. Suazo at the University of Minnesota for helpful CuAAC discussions. This work was supported by the University of Minnesota, the UMN NIH Biotechnology Training Grant (5T32GM008347 to A.E.), NIH DP2OD008592. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota and the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231, for providing resources that contributed to the results reported within this paper.