All organisms have evolved mechanisms to manage the stalling of ribosomes upon translation of aberrant mRNA. In eukaryotes, the large ribosomal subunit-associated quality control complex (RQC), composed of the listerin/Ltn1 E3 ubiquitin ligase and cofactors, mediates the ubiquitylation and extraction of ribosome-stalled nascent polypeptide chains for proteasomal degradation. How RQC recognizes stalled ribosomes and performs its functions has not been understood. Using single-particle cryoelectron microscopy, we have determined the structure of the RQC complex bound to stalled 60S ribosomal subunits. The structure establishes how Ltn1 associates with the large ribosomal subunit and properly positions its E3-catalytic RING domain to mediate nascent chain ubiquitylation. The structure also reveals that a distinguishing feature of stalled 60S particles is an exposed, nascent chainconjugated tRNA, and that the Tae2 subunit of RQC, which facilitates Ltn1 binding, is responsible for selective recognition of stalled 60S subunits. RQC components are engaged in interactions across a large span of the 60S subunit surface, connecting the tRNA in the peptidyl transferase center to the distally located nascent chain tunnel exit. This work provides insights into a mechanism linking translation and protein degradation that targets defective proteins immediately after synthesis, while ignoring nascent chains in normally translating ribosomes.Tae2/Nemf | translational surveillance | protein quality control | cryo-EM | listerin/Ltn1 E3 ubiquitin ligase D uring the canonical termination and recycling steps of translation, stop codon recognition triggers factor-mediated hydrolysis of the nascent peptidyl-tRNA conjugate, nascent chain release, and ribosome splitting (1-3). Conversely, translation of aberrant mRNA, such as mRNA lacking stop codons ("nonstop mRNA"), renders 80S ribosomes stalled with nascent polypeptides (1-3). Furthermore, "nonstop proteins" cannot be corrected by quality control chaperones and have the potential to interfere with cellular function (3, 4). Not surprisingly, defective termination and recycling are under surveillance by a variety of mechanisms (1-3). In eukaryotes, "rescue factors" homologous to termination factors promote dissociation of translationally halted ribosomes in a stop codon-independent manner (5). However, because rescue factors lack peptidyl-tRNA hydrolase activity, their action results in nascent chains remaining stalled on the released 60S subunit.Ltn1 is the critical E3 ligase mediating ubiquitylation of aberrant proteins that become stalled on ribosomes during translation (4). Mutation of the Ltn1 mouse ortholog, listerin, causes neurodegeneration (6), suggesting an important function for this process. Ltn1 works together with several cofactors as part of the ribosome-associated quality control complex (RQC) (7-9) and appears to first associate with nascent chain-stalled 60S subunits together with two proteins of unknown function, Tae2 and Rqc1 (7, 9). Ltn1-mediated ubiquitylation of t...

We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.