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Mol Cell ; 81(12): 2656-2668.e8, 2021 06 17.
Article in English | MEDLINE | ID: covidwho-1179919


A deficient interferon (IFN) response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been implicated as a determinant of severe coronavirus disease 2019 (COVID-19). To identify the molecular effectors that govern IFN control of SARS-CoV-2 infection, we conducted a large-scale gain-of-function analysis that evaluated the impact of human IFN-stimulated genes (ISGs) on viral replication. A limited subset of ISGs were found to control viral infection, including endosomal factors inhibiting viral entry, RNA binding proteins suppressing viral RNA synthesis, and a highly enriched cluster of endoplasmic reticulum (ER)/Golgi-resident ISGs inhibiting viral assembly/egress. These included broad-acting antiviral ISGs and eight ISGs that specifically inhibited SARS-CoV-2 and SARS-CoV-1 replication. Among the broad-acting ISGs was BST2/tetherin, which impeded viral release and is antagonized by SARS-CoV-2 Orf7a protein. Overall, these data illuminate a set of ISGs that underlie innate immune control of SARS-CoV-2/SARS-CoV-1 infection, which will facilitate the understanding of host determinants that impact disease severity and offer potential therapeutic strategies for COVID-19.

Antigens, CD/genetics , Host-Pathogen Interactions/genetics , Interferon Regulatory Factors/genetics , Interferon Type I/genetics , SARS-CoV-2/genetics , Viral Proteins/genetics , Animals , Antigens, CD/chemistry , Antigens, CD/immunology , Binding Sites , Cell Line, Tumor , Chlorocebus aethiops , Endoplasmic Reticulum/genetics , Endoplasmic Reticulum/immunology , Endoplasmic Reticulum/virology , GPI-Linked Proteins/chemistry , GPI-Linked Proteins/genetics , GPI-Linked Proteins/immunology , Gene Expression Regulation , Golgi Apparatus/genetics , Golgi Apparatus/immunology , Golgi Apparatus/virology , HEK293 Cells , Host-Pathogen Interactions/immunology , Humans , Immunity, Innate , Interferon Regulatory Factors/classification , Interferon Regulatory Factors/immunology , Interferon Type I/immunology , Molecular Docking Simulation , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , SARS-CoV-2/immunology , Signal Transduction , Vero Cells , Viral Proteins/chemistry , Viral Proteins/immunology , Virus Internalization , Virus Release/genetics , Virus Release/immunology , Virus Replication/genetics , Virus Replication/immunology
Proteins ; 89(4): 416-426, 2021 04.
Article in English | MEDLINE | ID: covidwho-947002


To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.

Binding Sites , Computational Biology/methods , Ligands , Machine Learning , Algorithms , Antigens, CD/chemistry , Antigens, Neoplasm/chemistry , Area Under Curve , Coronavirus 3C Proteases/chemistry , Coronavirus Papain-Like Proteases/chemistry , Elasticity , Hepacivirus , Humans , Interleukin-2/chemistry , Karyopherins/chemistry , Models, Statistical , Molecular Dynamics Simulation , Protein Conformation , Protein Tyrosine Phosphatase, Non-Receptor Type 1/chemistry , ROC Curve , Receptors, Cytoplasmic and Nuclear/chemistry , Regression Analysis , Reproducibility of Results , SARS-CoV-2