The zinc finger ubiquitin ligase RNF6 has been proposed as a potential therapeutic target in several cancers, but understanding its molecular mechanism of degradation has been elusive. In the present study, we find that RNF6 is degraded via auto-ubiquitination in a manner dependent on its Really Interesting New Gene (RING) domain. We determine that when the RING domain is deleted (ΔRING) or the core cysteine residues in the zinc finger are mutated (C632S/C635S), the WT protein, but not the ΔRING or mutant RNF6 protein, undergoes polyubiquitination. We also identify USP7 as a deubiquitinase of RNF6 by tandem mass spectrometry. We show that USP7 interacts with RNF6 and abolishes its K48-linked polyubiquitination, thereby preventing its degradation. In contrast, we found a USP7-specific inhibitor promotes RNF6 polyubiquitination, degradation, and cell death. Furthermore, we demonstrate the anti-leukemic drug Nilotinib and anti-myeloma drug Panobinostat (LBH589) induce RNF6 K48-linked polyubiquitination and degradation in both multiple myeloma (MM) and leukemia cells. In agreement with our hypothesis on the mode of RNF6 degradation, we show these drugs promote RNF6 auto-ubiquitination in an in vitro ubiquitination system without other E3 ligases. Consistently, reexpression of RNF6 ablates drug-induced MM and leukemia cell apoptosis. Therefore, our results reveal that RNF6 is a RING E3 ligase that undergoes auto-ubiquitination, which could be abolished by USP7 and induced by anti-cancer drugs. We propose that chemical induction of RNF6 auto-ubiquitination and degradation could be a novel strategy for the treatment of hematological malignancies including MM and leukemia.

There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods to clarify their biological activities. Using artificial intelligence instead is a more energy-efficient and faster approach. MuCoCP aims to build a complete pre-trained model for extracting potential features of cyclic peptides, which can be fine-tuned to accurately predict cyclic peptide bioactivity on various downstream tasks. To maximize its effectiveness, we use a novel data augmentation method based on a priori chemical knowledge and multiple unsupervised training objective functions to greatly improve the information-grabbing ability of the model. To assay the efficacy of the model, we conducted validation on the membrane-permeability of cyclic peptides which achieved an accuracy of 0.87 and R-squared of 0.503 on CycPeptMPDB using semi-supervised training and obtained an accuracy of 0.84 and R-squared of 0.384 using a model with frozen parameters on an external dataset. This result has achieved state-of-the-art, which substantiates the stability and generalization capability of MuCoCP. It means that MuCoCP can fully explore the high-dimensional information of cyclic peptides and make accurate predictions on downstream bioactivity tasks, which will serve as a guide for the future de novo design of cyclic peptide drugs and promote the development of cyclic peptide drugs. All code used in our proposed method can be found at https://github.com/lennonyu11234/MuCoCP.

An organocatalytic enantio- and diastereoselective synthesis of trifluoro-ethylamine allenoate derivatives containing axial and central chiralities has been achieved.

In the original version of this article the author name Xiaolei Chen was published incorrectly. This has been corrected to Xiao Lei Chen.