《BioRxiv,2月3日,(第2版更新)通过药物-靶标相互作用深度学习模型预测可能作用于新型冠状病毒(2019-ncov)的商用抗病毒药物》

  • 来源专题:COVID-19科研动态监测
  • 编译者: zhangmin
  • 发布时间:2020-02-04
  • Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig

    Changhai Lei, Wenyan Fu, Kewen Qian, Tian Li, Sheng Zhang, Min Ding, Shi Hu

    doi: https://doi.org/10.1101/2020.02.01.929976

    Abstract

    2019-nCoV, which is a novel coronavirus emerged in Wuhan, China, at the end of 2019, has caused at least infected 11,844 as of Feb 1, 2020. However, there is no specific antiviral treatment or vaccine currently. Very recently report had suggested that novel CoV would use the same cell entry receptor, ACE2, as the SARS-CoV. In this report, we generated a novel recombinant protein by connecting the extracellular domain of human ACE2 to the Fc region of the human immunoglobulin IgG1. An ACE2 mutant with low catalytic activity was also used in the study. The fusion proteins were then characterized. Both fusion proteins has high affinity binding to the receptor-binding domain (RBD) of SARS-CoV and 2019-nCoV and exerted desired pharmacological properties. Moreover, fusion proteins potently neutralized SARS-CoV and 2019-nCoV in vitro. As these fusion proteins exhibit cross-reactivity against coronaviruses, they could have potential applications for diagnosis, prophylaxis, and treatment of 2019-nCoV.

    *注,本文为预印本论文手稿,是未经同行评审的初步报告,其观点仅供科研同行交流,并不是结论性内容,请使用者谨慎使用.

  • 原文来源:https://www.biorxiv.org/content/10.1101/2020.02.01.929976v2
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  • 《BioRxiv,2月2日,通过药物-靶标相互作用的深度学习模型预测可能用于2019-nCoV的抗病毒药》

    • 来源专题:COVID-19科研动态监测
    • 编译者:zhangmin
    • 发布时间:2020-02-03
    • The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV. *注,本文为预印本论文手稿,是未经同行评审的初步报告,其观点仅供科研同行交流,并不是结论性内容,请使用者谨慎使用.
  • 《通过药物-靶标相互作用深度学习模型预测可能作用于新型冠状病毒(2019-nCoV)的市售抗病毒药物》

    • 来源专题:新发突发疾病(新型冠状病毒肺炎)
    • 编译者:蒋君
    • 发布时间:2020-02-09
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