Functional interaction between bases C1049 in domain II and G2751 in domain VI of 23S rRNA in Escherichia coli ribosomes

The factor-binding center within the Escherichia coli ribosome is comprised of two discrete domains of 23S rRNA: the GTPase-associated region (GAR) in domain II and the sarcin–ricin loop in domain VI. These two regions appear to collaborate in the factor-dependent events that occur during protein synthesis. Current X-ray crystallography of the ribosome shows an interaction between C1049 in the GAR and G2751 in domain VI. We have confirmed this interaction by site-directed mutagenesis and chemical probing. Disruption of this base pair affected not only the chemical modification of some bases in domains II and VI and in helix H89 of domain V, but also ribosome function dependent on both EF-G and EF-Tu. Mutant ribosomes carrying the C1049 to G substitution, which show enhancement of chemical modification at G2751, were used to probe the interactions between the regions around 1049 and 2751. Binding of EF-G-GDP-fusidic acid, but not EF-G-GMP-PNP, to the ribosome protected G2751 from modification. The G2751 protection was also observed after tRNA binding to the ribosomal P and E sites. The results suggest that the interactions between the bases around 1049 and 2751 alter during different stages of the translation process

Engineered Toxins “Zymoxins” Are Activated by the HCV NS3 Protease by Removal of an Inhibitory Protein Domain

The synthesis of inactive enzyme precursors, also known as “zymogens,” serves as a mechanism for regulating the execution of selected catalytic activities in a desirable time and/or site. Zymogens are usually activated by proteolytic cleavage. Many viruses encode proteases that execute key proteolytic steps of the viral life cycle. Here, we describe a proof of concept for a therapeutic approach to fighting viral infections through eradication of virally infected cells exclusively, thus limiting virus production and spread. Using the hepatitis C virus (HCV) as a model, we designed two HCV NS3 protease-activated “zymogenized” chimeric toxins (which we denote “zymoxins”). In these recombinant constructs, the bacterial and plant toxins diphtheria toxin A (DTA) and Ricin A chain (RTA), respectively, were fused to rationally designed inhibitor peptides/domains via an HCV NS3 protease-cleavable linker. The above toxins were then fused to the binding and translocation domains of Pseudomonas exotoxin A in order to enable translocation into the mammalian cells cytoplasm. We show that these toxins exhibit NS3 cleavage dependent increase in enzymatic activity upon NS3 protease cleavage in vitro. Moreover, a higher level of cytotoxicity was observed when zymoxins were applied to NS3 expressing cells or to HCV infected cells, demonstrating a potential therapeutic window. The increase in toxin activity correlated with NS3 protease activity in the treated cells, thus the therapeutic window was larger in cells expressing recombinant NS3 than in HCV infected cells. This suggests that the “zymoxin” approach may be most appropriate for application to life-threatening acute infections where much higher levels of the activating protease would be expected

Scalable Approaches to Dubins Vehicle Navigation Problems Under Uncertainty

The environment around an autonomously navigated vehicle can have an unpredictablenumber of other vehicles and stationary or moving obstacles that may or may not have harmful intentions. The safe navigation of the autonomous vehicle in the presence of other vehicles and obstacles can be formulated as a stochastic optimal control problem. While in theory one can write down the corresponding Hamilton-Jacobi-Bellman (HJB) equation for any state space control problem, practically solving the equation is computationally infeasible when the state space is large. Moreover, once it is accounted for a time varying number of obstacles and other vehicles, and the associated time varying dimension of the state space, it is clear that new approaches to the design of vehicle navigation have to be considered. This work addresses the problem of autonomous navigation by a scalable integration of stochastic optimal control solutions to problems such as vehicle-to-vehicle, vehicle-to-obstacle, or vehicle-to-goal problems. The scalable navigation means that the autonomous vehicle or team of vehicles can navigate toward their goals while coping with a large number of other vehicles, or obstacles in their proximity. The work is based on the Dubins nonholonomic vehicle model and is illustrated by multiple scenarios in simulations and with real robots

Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies. © Copyright © 2021 Huang, Kleindessner, Munishkin, Varshney, Guo and Wang.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]