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Results for “conserved moiety”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2003

Structure-Derived Proton-Transfer Mechanism of Action Human Pyruvate Dehydrogenase

The derivative of vitamin B1 thiamin pyrophosphate (TPP) is a cofactor of pyruvate dehydrogenase (E1p) that is involved in decarboxylation of pyruvate followed by reductive acetylation of lipoic acid covalently bound to a lysine residue of dihydrolipoamide acetyltransferase. The structure of E1p recently determined in our laboratory revealed patterns of association of foul subunits and specifics of two TPP binding sites. The mechanism of action in part includes a conserved hydrogen bond between the N1' atom of the aminopyrimidine ring of the cofactor and the carboxylate group of Glu59 from the beta subunits, and a V-conformation of the cofactor that brings the N4' atom of the aminopyrimidine ring to the distance of the intramolecular hydrogen bond formed with the C2-atom of the thiazolium moiety. The carboxylate group of Glu59 is the local proton acceptor that enables proton translocation within the aminopyrimidine ring and stabilization of the rare N4' - iminopyrimidine tautomer. Based on the analysis of E1p structure, we postulate that the protein environment drives N4' - amino/N4' - imino dynamics resulting in a concerted shuttle-like movement of the subunits. We also propose that this movement of the subunits is strictly coordinated with the two enzymatic reactions carried out in E1p by each of the two cofactor sites. It is proposed that these reactions are in alternating phases such that when one active site is involved in decarboxylation, the other is involved in acetylation of lipoyl noiety.

Ciszak, Ewa↗
NASA NTRS2021

Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks

Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.

Ashwin Ravichandran↗