Suppressor of IKKepsilon (SIKE), a protein first identified in the antiviral innate immune response associated with TANK‐binding kinase 1 (TBK1), is associated with multiple, distinct protein complexes including TBK1, STRIPAK (striatin interacting phosphatase and kinase) and cytoskeletal proteins, tubulin and actinin. Although SIKE's function is not fully defined in these complexes, SIKE does function as a high‐affinity substrate of TBK1 with phosphorylation occurring at up to six SIKE serine residues, S133, S185, S187, S188, S190, and S198. The accompanying figure shows a cartoon diagram of SIKE dimer model with the phosphorylation sites shown in spheres (red 187, 190; orange 133; 187 green; 188 and 190 cyan). Using a phosphomimetic mutant (S133/185/187/188/190/198E) of SIKE, size exclusion chromatography and chemical crosslinking studies showed primarily a monomeric species, whereas unmodified SIKE separated as a dimeric species. These observations suggested that SIKE could undergo a phosphorylation‐induced change in quaternary structure, but how many of the six phosphorylation sites were necessary? To explore this idea, conservation of the phosphorylation sites was explored. 133 SIKE sequences (Animalia, Fungi) from OrthoDB and Protein Blast were compiled and aligned using Clustal Omega. Positions 187 and 190 were conserved as serine in >90% of sequences whereas position 133 was ~60% serine and 25% aspartate or glutamate with a 9:10 bias towards aspartate. The remaining positions had 75% or less retaining serine with position 188 registering ~32% lysine. Using a model of the SIKE dimer where the phosphorylation sites line part of the dimer interface, phosphorylated S187/190 in each subunit would be positioned to repulse one another. To gain further insight into the role of individual phosphorylation sites on the theoretical dimer interface, phosphomimetic mutants at each phosphorylation site were computationally created (PyMOL), energy minimized (GalaxyRefineComplex) to yield 10 models, and the interface stability of each model for each mutant assessed on a per residue basis (HawkDock MM/GBSA). These mutant data were then compared to a similar analysis of the wild type SIKE model to identify residues with significant changes in total free energy of binding (dimer interface). S133E showed no significant changes from WT whereas S185/187/188/190/198E individually revealed a similar pattern of residues with altered total free energy of binding adjacent to the phosphorylation sites that differed in the number (19‐30) of residues affected in the region. The sequence conservation suggested that positions 187 and 190 could serve as key phosphorylation sites to trigger a dimer to monomer transition. Although the computational analyses has not resolved the effect of multiple phosphorylation events, the primary alteration to dimer stability is localized to the regions adjacent to the phosphorylation sites.

Enzymes complexes (metabolons) support the metabolic reactions through substrate channeling. Several enzymes in the Krebs cycle are found in protein‐protein metabolons including mitochondrial malate dehydrogenase (MDH2) and citric synthase (CS). While the weak transitory interaction between these enzymes has been demonstrated, the key residues of human MDH2 involved in the interface with CS has not been identified. The purpose of this study is to probe possible residues of MDH responsible for binding to CS. Because cytosolic MDH (MDH1) poorly binds to CS, we initially identified unique sequences that were involved in or adjacent to reported or predicted binding sites of MDH2 and CS. Four regions were selected and residues corresponding to the primary sequence of cytosolic MDH1 were substituted in place of mitochondrial MDH2. Computational models of all the monomeric constructs were made using phyre2 homology modeling, subsequent refinement of the monomers using Galaxy Refine and construction of the dimer forms using the appropriate template in PyMol. Dimers were then refined using Galaxy Refine Complex to give replicate structures of the final models. Potential metabolon formation was explored using HawkDock with the dimer versions of MDH and Citrate Synthase either with or without restraints imposed by published metabolon models. Wild‐type human genes of MDH1, MDH2, and CS were codon optimized for bacterial expression and cloned into the C‐terminus of each gene in a pET28a expression vector. Each MDH1‐MDH2 substitution (DS1‐DS4) were constructed by Gibson cloning. Resulting structure and functional impacts were determined by melting points and kinetic parameters (specific activity, Km, Vmax and Kcat) in purified proteins. Interactions between wild‐type MDH1/2 and CS were compared to MDH2 DS mutants. To qualitatively demonstrate interactions, pull‐down assays were performed in the absence and presence of crowding agents, glycerol, PEG or Ficoll 70. Finally wild‐type MDH 1 and 2 vs MDH2 mutants were subjected to competitive pull‐down assays to identify regions responsible for isozyme specific interactions. This work will show four of the potential interacting regions between MDH and CS and lead to a better understanding of dynamics of this metabolic pair.

Aminoacyl‐tRNA synthetases (aaRSs) are a well‐known class of enzymes that act as sentinels of genome. During protein translation, aaRSs ensure that L‐amino acids are charged or acylated onto their cognate tRNA isoacceptor(s). Hence, aaRSs help ensure that ribosomally translated proteins contain the proper sequence of amino acids. aaRS catalyze tRNA charging in a two‐step process. In step one, aaRSs selectively form a high energy amino acid adenylate. This step, refereed to amino acid activation, is the first level of selectivity in protein translation. Here, aaRSs effectively discriminate non‐cognate L‐amino acids. In step two, aaRSs transfer adenylated amino acids onto the cognate tRNA isoacceptor. This step is referred to as aminoacylation or tRNA charging. A great deal is known about aaRS reaction mechanisms but a number of confounding idiosyncrasies remain. The focus of this work aims to elucidate idiosyncrasies associated with amino acid activation. Here, a 32P‐based assay is used to measure the level to which E. coliaaRSs activate non‐cognate L‐amino acids. The purpose of these assays is to measure the plasticity of aaRS active sites to define additional underlying binding interactions that modulate amino acid recognition. This information could also be used to develop small molecule inhibitors of aaRS. Moreover, these data could reveal underlying clues to the potential origins of aaRSs. Here, three aaRSs are investigated: ArgRS, IleRS and LysRS. The activation data shows that each aaRS perhaps somewhat surprisingly activates more than five non‐cognate AAs to varying levels. Next, computer docking experiments are used to visualize the binding interaction of synthetase active site residues to each non‐cognate amino acid. These data are currently being used to construct protein‐ligand interaction maps of each aaRS active site. The resulting interactions maps can be used to more clearly elucidate idiosyncrasies associated with amino activation.