Control of Hsp90 chaperone and its clients by N-terminal acetylation and the N-end rule pathway

We found that the heat shock protein 90 (Hsp90) chaperone system of the yeast Saccharomyces cerevisiae is greatly impaired in naa10Δ cells, which lack the NatA N^α-terminal acetylase (Nt-acetylase) and therefore cannot N-terminally acetylate a majority of normally N-terminally acetylated proteins, including Hsp90 and most of its cochaperones. Chk1, a mitotic checkpoint kinase and a client of Hsp90, was degraded relatively slowly in wild-type cells but was rapidly destroyed in naa10Δ cells by the Arg/N-end rule pathway, which recognized a C terminus-proximal degron of Chk1. Diverse proteins (in addition to Chk1) that are shown here to be targeted for degradation by the Arg/N-end rule pathway in naa10Δ cells include Kar4, Tup1, Gpd1, Ste11, and also, remarkably, the main Hsp90 chaperone (Hsc82) itself. Protection of Chk1 by Hsp90 could be overridden not only by ablation of the NatA Nt-acetylase but also by overexpression of the Arg/N-end rule pathway in wild-type cells. Split ubiquitin-binding assays detected interactions between Hsp90 and Chk1 in wild-type cells but not in naa10Δ cells. These and related results revealed a major role of Nt-acetylation in the Hsp90-mediated protein homeostasis, a strong up-regulation of the Arg/N-end rule pathway in the absence of NatA, and showed that a number of Hsp90 clients are previously unknown substrates of the Arg/N-end rule pathway

Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data

Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is challenging due to the presence of complex issues such as label errors, under-representation, and outliers. In this paper, we propose a unified approach for identifying the problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data. We further introduce a visualization tool that provides contextual information of a data point in the feature-embedded space, serving as an effective tool for interactively diagnosing data. We evaluate the label error and outlier/out-of-distribution (OOD) detection performances of our approach on the large-scale image, speech, and language domain tasks, including ImageNet, ESC-50, and MNLI. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in debugging large-scale real-world datasets across various domains.Comment: preprin

Five enzymes of the Arg/N-degron pathway form a targeting complex: The concept of superchanneling

The Arg/N-degron pathway targets proteins for degradation by recognizing their N-terminal (Nt) residues. If a substrate bears, for example, Nt-Asn, its targeting involves deamidation of Nt-Asn, arginylation of resulting Nt-Asp, binding of resulting (conjugated) Nt-Arg to the UBR1-RAD6 E3-E2 ubiquitin ligase, ligase-mediated synthesis of a substrate-linked polyubiquitin chain, its capture by the proteasome, and substrate’s degradation. We discovered that the human Nt-Asn–specific Nt-amidase NTAN1, Nt-Gln–specific Nt-amidase NTAQ1, arginyltransferase ATE1, and the ubiquitin ligase UBR1-UBE2A/B (or UBR2-UBE2A/B) form a complex in which NTAN1 Nt-amidase binds to NTAQ1, ATE1, and UBR1/UBR2. In addition, NTAQ1 Nt-amidase and ATE1 arginyltransferase also bind to UBR1/UBR2. In the yeast Saccharomyces cerevisiae, the Nt-amidase, arginyltransferase, and the double-E3 ubiquitin ligase UBR1-RAD6/UFD4-UBC4/5 are shown to form an analogous targeting complex. These complexes may enable substrate channeling, in which a substrate bearing, for example, Nt-Asn, would be captured by a complex-bound Nt-amidase, followed by sequential Nt modifications of the substrate and its polyubiquitylation at an internal Lys residue without substrate’s dissociation into the bulk solution. At least in yeast, the UBR1/UFD4 ubiquitin ligase interacts with the 26S proteasome, suggesting an even larger Arg/N-degron–targeting complex that contains the proteasome as well. In addition, specific features of protein-sized Arg/N-degron substrates, including their partly sequential and partly nonsequential enzymatic modifications, led us to a verifiable concept termed “superchanneling.” In superchanneling, the synthesis of a substrate-linked poly-Ub chain can occur not only after a substrate’s sequential Nt modifications, but also before them, through a skipping of either some or all of these modifications within a targeting complex

Thermoelectric properties of nanoporous three-dimensional graphene networks

We propose three dimensional-graphene nanonetworks (3D-GN) with pores in the range of 10 similar to 20 nm as a potential candidate for thermoelectric materials. The 3D-GN has a low thermal conductivity of 0.90 W/mK @773 K and a maximum electrical conductivity of 6660 S/m @773 K. Our results suggest a straightforward way to individually control two interdependent parameters, sigma and kappa, in the nanoporous graphene structures to ultimately improve the figure of merit value.open