A Fluorescence Polarization Assay for Macrodomains Facilitates the Identification of Potent Inhibitors of the SARS-CoV-2 Macrodomain.

Viral macrodomains, which can bind to and/or hydrolyze adenine diphosphate ribose (ADP-ribose or ADPr) from proteins, have been suggested to counteract host immune response and be viable targets for the development of antiviral drugs. Therefore, developing high-throughput screening (HTS) techniques for macrodomain inhibitors is of great interest. Herein, using a novel tracer TAMRA-ADPr, an ADP-ribose compound conjugated with tetramethylrhodamine, we developed a robust fluorescence polarization assay for various viral and human macrodomains including SARS-CoV-2 Macro1, VEEV Macro, CHIKV Macro, human MacroD1, MacroD2, and PARP9 Macro2. Using this assay, we validated Z8539 (IC50 6.4 µM) and GS441524 (IC50 15.2 µM), two literature-reported small-molecule inhibitors of SARS-CoV-2 Macro1. Our data suggest that GS441524 is highly selective for SARS-CoV-2 Macro1 over other human and viral macrodomains. Furthermore, using this assay, we identified pNP-ADPr (ADP-ribosylated p-nitrophenol, IC50 370 nM) and TFMU-ADPr (ADP-ribosylated trifluoromethyl umbelliferone, IC50 590 nM) as the most potent SARS-CoV-2 Macro1 binders reported to date. An X-ray crystal structure of SARS-CoV-2 Macro1 in complex with TFMU-ADPr revealed how the TFMU moiety contributes to the binding affinity. Our data demonstrate that this fluorescence polarization assay is a useful addition to the HTS methods for the identification of macrodomain inhibitors.

Self-Supervised Model Based on Masked Autoencoders Advance CT Scans Classification

The coronavirus pandemic has been going on since the year 2019, and the trend is still not abating. Therefore, it is particularly important to classify medical CT scans to assist in medical diagnosis. At present, Supervised Deep Learning algorithms have made a great success in the classification task of medical CT scans, but medical image datasets often require professional image annotation, and many research datasets are not publicly available. To solve this problem, this paper is inspired by the self-supervised learning algorithm MAE and uses the MAE model pre-trained on ImageNet to perform transfer learning on CT Scans dataset. This method improves the generalization performance of the model and avoids the risk of overfitting on small datasets. Through extensive experiments on the COVID-CT dataset and the SARS-CoV-2 dataset, we compare the SSL-based method in this paper with other state-of-the-art supervised learning-based pretraining methods. Experimental results show that our method improves the generalization performance of the model more effectively and avoids the risk of overfitting on small datasets. The model achieved almost the same accuracy as supervised learning on both test datasets. Finally, ablation experiments aim to fully demonstrate the effectiveness of our method and how it works.

Dynamic Continuum of Nanoscale Peptide Assemblies Facilitates Endocytosis and Endosomal Escape.

Alkaline phosphatase (ALP) enables intracellular targeting by peptide assemblies, but how the ALP substrates enter cells remains elusive. Here we show that nanoscale phosphopeptide assemblies cluster ALP to enable caveolae-mediated endocytosis (CME) and endosomal escape. Specifically, fluorescent phosphopeptides undergo enzyme-catalyzed self-assembly to form nanofibers. Live cell imaging unveils that phosphopeptides nanoparticles, coincubated with HEK293 cells overexpressing red fluorescent protein-tagged tissue-nonspecific ALP (TNAP-RFP), cluster TNAP-RFP in lipid rafts to enable CME. Further dephosphorylation of the phosphopeptides produces peptidic nanofibers for endosomal escape. Inhibiting TNAP, cleaving the membrane anchored TNAP, or disrupting lipid rafts abolishes the endocytosis. Decreasing the transformation to nanofibers prevents the endosomal escape. As the first study establishing a dynamic continuum of nanoscale assemblies for cellular uptake, this work illustrates an effective design for enzyme-responsive supramolecular therapeutics and provides mechanism insights for understanding the dynamics of cellular uptake of proteins or exogenous peptide aggregates.