No abstract

A functional association is uncovered between the ribosome-associated trigger factor (TF) chaperone and the ClpXP degradation complex. Bioinformatic analyses demonstrate conservation of the close proximity of tig, the gene coding for TF, and genes coding for ClpXP, suggesting a functional interaction. The effect of TF on ClpXP-dependent degradation varies based on the nature of substrate. While degradation of some substrates are slowed down or are unaffected by TF, surprisingly, TF increases the degradation rate of a third class of substrates. These include λ phage replication protein λO, master regulator of stationary phase RpoS, and SsrA-tagged proteins. Globally, TF acts to enhance the degradation of about 2% of newly synthesized proteins. TF is found to interact through multiple sites with ClpX in a highly dynamic fashion to promote protein degradation. This chaperone–protease cooperation constitutes a unique and likely ancestral aspect of cellular protein homeostasis in which TF acts as an adaptor for ClpXP.

Thermus thermophilus trigger factor (TtTF) is a zinc-dependent molecular chaperone whose folding-arrest activity is regulated by Zn2+. However, little is known about the mechanism of zinc-dependent regulation of the TtTF activity. Here we exploit in vitro biophysical experiments to investigate zinc-binding, the oligomeric state, the secondary structure, and the thermal stability of TtTF in the absence and presence of Zn2+. The data show that full-length TtTF binds Zn2+, but the isolated domains and tandem domains of TtTF do not bind to Zn2+. Furthermore, circular dichroism (CD) and nuclear magnetic resonance (NMR) spectra suggested that Zn2+-binding induces the partial structural changes of TtTF, and size exclusion chromatography-multi-angle light scattering (SEC-MALS) showed that Zn2+ promotes TtTF oligomerization. Given the previous work showing that the activity regulation of E. coli trigger factor is accompanied by oligomerization, the data suggest that TtTF exploits zinc ions to induce the structural change coupled with the oligomerization to assemble the client-binding site, thereby effectively preventing proteins from misfolding in the thermal environment.

For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.