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This article was corrected on 18 July 2022. See the end of the full text for details.
Basic Protocol 1: Labeling a protein of interest at its N‐terminus with NHS esters through stepwise reaction
Alternate Protocol: Labeling a protein of interest at its N‐terminus with NHS esters through a one‐pot reaction
Basic Protocol 2: Purifying the N‐terminal fluorescent‐labeled protein and determining its concentration and labeling efficiency
Basic Protocol 3: Using MST to determine the binding affinity of an N‐terminal fluorescent‐labeled protein to a binding partner.
Basic Protocol 4: NHS ester labeling of ubiquitin E3 ligase WWP2 and measurement of the binding affinity between WWP2 and an E2 conjugating enzyme by the MST binding assay
Gerhardt, Edileusa C.; Parize, Erick; Gravina, Fernanda; Pontes, Flávia L.; Santos, Adrian R.; Araújo, Gillize A.; Goedert, Ana C.; Urbanski, Alysson H.; Steffens, Maria B.; Chubatsu, Leda S.; et al(
, mSystems)
Porto, Carla
(Ed.)
ABSTRACT The PII family comprises a group of widely distributed signal transduction proteins ubiquitous in prokaryotes and in the chloroplasts of plants. PII proteins sense the levels of key metabolites ATP, ADP, and 2-oxoglutarate, which affect the PII protein structure and thereby the ability of PII to interact with a range of target proteins. Here, we performed multiple ligand fishing assays with the PII protein orthologue GlnZ from the plant growth-promoting nitrogen-fixing bacterium Azospirillum brasilense to identify 37 proteins that are likely to be part of the PII protein-protein interaction network. Among the PII targets identified were enzymes related to nitrogen and fatty acid metabolism, signaling, coenzyme synthesis, RNA catabolism, and transcription. Direct binary PII-target complex was confirmed for 15 protein complexes using pulldown assays with recombinant proteins. Untargeted metabolome analysis showed that PII is required for proper homeostasis of important metabolites. Two enzymes involved in c-di-GMP metabolism were among the identified PII targets. A PII-deficient strain showed reduced c-di-GMP levels and altered aerotaxis and flocculation behavior. These data support that PII acts as a major metabolic hub controlling important enzymes and the homeostasis of key metabolites such as c-di-GMP in response to the prevailing nutritional status. IMPORTANCE The PII proteins sense and integrate important metabolic signals which reflect the cellular nutrition and energy status. Such extraordinary ability was capitalized by nature in such a way that the various PII proteins regulate different facets of metabolism by controlling the activity of a range of target proteins by protein-protein interactions. Here, we determined the PII protein interaction network in the plant growth-promoting nitrogen-fixing bacterium Azospirillum brasilense . The interactome data along with metabolome analysis suggest that PII functions as a master metabolic regulator hub. We provide evidence that PII proteins act to regulate c-di-GMP levels in vivo and cell motility and adherence behaviors.
Sutera, Vincent A.; Weeks, Savannah J.; Dudenhausen, Elizabeth E.; Baggett, Helen B.; Shaw, McKay C.; Brand, Kirsten A.; Glass, David J.; Bloom, Linda B.; Lovett, Susan T.(
, DNA Repair)
Geng, Cunliang, Jung, Yong, Renaud, Nicolas, Honavar, Vasant, Bonvin, Alexandre M, Xue, Li C, and Valencia, Alfonso. iScore: A novel graph kernel-based function for scoring protein-protein docking models. Retrieved from https://par.nsf.gov/biblio/10112141. Bioinformatics . Web. doi:10.1093/bioinformatics/btz496.
Geng, Cunliang, Jung, Yong, Renaud, Nicolas, Honavar, Vasant, Bonvin, Alexandre M, Xue, Li C, and Valencia, Alfonso.
"iScore: A novel graph kernel-based function for scoring protein-protein docking models". Bioinformatics (). Country unknown/Code not available. https://doi.org/10.1093/bioinformatics/btz496.https://par.nsf.gov/biblio/10112141.
@article{osti_10112141,
place = {Country unknown/Code not available},
title = {iScore: A novel graph kernel-based function for scoring protein-protein docking models},
url = {https://par.nsf.gov/biblio/10112141},
DOI = {10.1093/bioinformatics/btz496},
abstractNote = {},
journal = {Bioinformatics},
author = {Geng, Cunliang and Jung, Yong and Renaud, Nicolas and Honavar, Vasant and Bonvin, Alexandre M and Xue, Li C and Valencia, Alfonso},
}
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