Certain RING ubiquitin ligases (E3s) dimerize to facilitate ubiquitin (Ub) transfer from ubiquitin-conjugating enzyme (E2) to substrate, but structural evidence on how this process promotes Ub transfer is lacking. Here we report the structure of the human dimeric RING domain from BIRC7 in complex with the E2 UbcH5B covalently linked to Ub (UbcH5B∼Ub). The structure reveals extensive noncovalent donor Ub interactions with UbcH5B and both subunits of the RING domain dimer that stabilize the globular body and C-terminal tail of Ub. Mutations that disrupt these noncovalent interactions or RING dimerization reduce UbcH5B∼Ub binding affinity and ubiquitination activity. Moreover, NMR analyses demonstrate that BIRC7 binding to UbcH5B∼Ub induces peak-shift perturbations in the donor Ub consistent with the crystallographically-observed Ub interactions. Our results provide structural insights into how dimeric RING E3s recruit E2∼Ub and optimize the donor Ub configuration for transfer.
Repair of oxidatively damaged bases in the genome via the base excision repair pathway is initiated with excision of these lesions by DNA glycosylases with broad substrate range. The newly discovered human DNA glycosylases, NEIL1 and NEIL2, are distinct in structural features and reaction mechanism from the previously characterized NTH1 and OGG1 but act on many of the same substrates. However, NEIL2 shows a unique preference for excising lesions from a DNA bubble, whereas NTH1 and OGG1 are only active with duplex DNA. NEIL1 also excises efficiently 5-hydroxyuracil, an oxidation product of cytosine, from the bubble and singlestranded DNA but does not have strong activity toward 8-oxoguanine in the bubble. The dichotomy in the activity of NEILs versus NTH1/OGG1 for bubble versus duplex DNA substrates is consistent with higher affinity of the NEILs for the bubble structures of both damaged and undamaged DNA relative to duplex structure. These observations suggest that the NEILs are functionally distinct from OGG1/NTH1 in vivo. OGG1/NTH1-independent repair of oxidized bases in the transcribed sequences supports the possibility that NEILs are preferentially involved in repair of lesions in DNA bubbles generated during transcription and/or replication.
Cbls are RING ubiquitin ligases that attenuate receptor tyrosine kinase (RTK) signal transduction. Cbl ubiquitination activity is stimulated by phosphorylation of a linker helix region (LHR) tyrosine residue. To elucidate the mechanism of activation, we determined the structures of human CBL, a CBL-substrate peptide complex and a phosphorylated-Tyr371-CBL-E2-substrate peptide complex, and we compared them with the known structure of a CBL-E2-substrate peptide complex. Structural and biochemical analyses show that CBL adopts an autoinhibited RING conformation, where the RING's E2-binding surface associates with CBL to reduce E2 affinity. Tyr371 phosphorylation activates CBL by inducing LHR conformational changes that eliminate autoinhibition, flip the RING domain and E2 into proximity of the substrate-binding site and transform the RING domain into an enhanced E2-binding module. This activation is required for RTK ubiquitination. Our results present a mechanism for regulation of c-Cbl's activity by autoinhibition and phosphorylation-induced activation.
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter-and intra-observer variability, and erroneous predictions may influence decisions that directly impact human health. In this paper, we first review the state-of-the-art in handling label noise in deep learning. Then, we review studies that have dealt with label noise in deep learning for medical image analysis. Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community. To help achieve a better understanding of the extent of the problem and its potential remedies, we conducted experiments with three medical imaging datasets with different types of label noise, where we investigated several existing strategies and developed new methods to combat the negative effect of label noise. Based on the results of these experiments and our review of the literature, we have made recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis. We hope that this article helps the medical image analysis researchers and developers in choosing and devising new techniques that effectively handle label noise in deep learning.
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