nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands.

The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn.

In vitro testing and computational analysis of specific phytochemicals with antiviral activities considering their possible applications against COVID-19.

The purpose of this study was to investigate the reservoir of natural products against the SARS-CoV-2 virus and to identify suitable candidates in order to recommend appropriate phytotherapy. Adequately prepared 65 molecules from traditional Chinese medicine with proven antiviral properties were subjected to docking analysis using AutoDock Vina 4 software with the aim to investigate binding affinity and interactions of compounds with Mpro from the SARS-CoV-2 virus. Biflavonoids and tannins show best docking scores with -9,80kcal/mol for biflavonoids and -9,00 kcal/mol for tannins. Biflavonoids: amentoflavone, agathistaflavone, robustaflavone, hinokiflavone and rhusflavanone were tested for their radical scavenging activity. Partition coefficients were examined by RP-HPLC. Evaluation of drug-likeness properties of investigated biflavonoids suggested rhusflavanone as a molecule with the best ADMET characteristics. Anti-inflammatory activity of rhusflavanone was investigated in LPS stimulated RAW264.7 macrophages. Tested biflavonoids exibit beneficial effects against inflammation by scavenging free radicals and by suppressing the production of proinflammatory mediators by macrophages. Both predictions of affinity spectra for substances (PASS) and in vitro testing showed promising biological activity of investigated biflavonoids. A Quantum chemical study was performed in order to calculate the thermodynamic, molecular orbital, and electrostatic potential of selected molecules and to compare their biological and chemical features. Our results highlighted antioxidant, anti-inflammatory and antiviral properties of investigated compounds, emphasizing the significance of biflavonoid moiety to selected characteristics, which encourage further investigational strategies against COVID-19.

Ruxolitinib attenuates secondary injury after traumatic spinal cord injury.

Excessive inflammation post-traumatic spinal cord injury (SCI) induces microglial activation, which leads to prolonged neurological dysfunction. However, the mechanism underlying microglial activation-induced neuroinflammation remains poorly understood. Ruxolitinib (RUX), a selective inhibitor of JAK1/2, was recently reported to inhibit inflammatory storms caused by SARS-CoV-2 in the lung. However, its role in disrupting inflammation post-SCI has not been confirmed. In this study, microglia were treated with RUX for 24 hours and then activated with interferon-γ for 6 hours. The results showed that interferon-γ-induced phosphorylation of JAK and STAT in microglia was inhibited, and the mRNA expression levels of pro-inflammatory cytokines tumor necrosis factor-α, interleukin-1ß, interleukin-6, and cell proliferation marker Ki67 were reduced. In further in vivo experiments, a mouse model of spinal cord injury was treated intragastrically with RUX for 3 successive days, and the findings suggest that RUX can inhibit microglial proliferation by inhibiting the interferon-γ/JAK/STAT pathway. Moreover, microglia treated with RUX centripetally migrated toward injured foci, remaining limited and compacted within the glial scar, which resulted in axon preservation and less demyelination. Moreover, the protein expression levels of tumor necrosis factor-α, interleukin-1ß, and interleukin-6 were reduced. The neuromotor function of SCI mice also recovered. These findings suggest that RUX can inhibit neuroinflammation through inhibiting the interferon-γ/JAK/STAT pathway, thereby reducing secondary injury after SCI and producing neuroprotective effects.

COVID-19 Imaging-based AI Research - A Literature Review.

BACKGROUND: The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial Intelligence (AI) assisted identification and detection of diseases is an effective method of medical diagnosis. OBJECTIVES: To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19. METHODS: In this paper, we firstly cover the latest collection and processing methods of datasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantification and severity assessment of infection in COVID-19 patients based on image segmentation and automatic screening. Finally, we analyze and point out the current AI-assisted diagnosis of COVID-19 problems, which may provide useful clues for future work. CONCLUSION: AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.

COVID-19 Docking Server: a meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19.

MOTIVATION: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019. Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target-ligand interactions represents a key challenge. Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and antibodies. RESULTS: Structures of proteins involved in the virus life cycle were collected or constructed based on the homologs of coronavirus, and prepared ready for docking. The meta-platform provides a free and interactive tool for the prediction of COVID-19 target-ligand interactions and following drug discovery for COVID-19. AVAILABILITY AND IMPLEMENTATION: http://ncov.schanglab.org.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.