COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning.
COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning.
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DOI：
10.3389/fimmu.2020.01581
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发表时间：
2020-03-22
期刊：
影响因子：
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通讯作者：
He, Yongqun
中科院分区：
文献类型：
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作者：
Ong, Edison;Wong, Mei U;He, Yongqun
为了最终对抗新出现的COVID-19大流行，期望开发针对由SARS-CoV-2冠状病毒引起的这种高度传染性疾病的有效且安全的疫苗。我们的文献和临床试验调查表明，整个病毒，以及刺突（S）蛋白，核衣壳（N）蛋白和膜（M）蛋白，已被测试用于针对SARS和MERS的疫苗开发。然而，这些候选疫苗可能缺乏完全保护的诱导，并存在安全性问题。然后，我们应用Vaxign和新开发的基于机器学习的Vaxign-ML反向疫苗学工具来预测COVID-19候选疫苗。我们的Vaxign分析发现，SARS-CoV-2 N蛋白序列与SARS-CoV和MERS-CoV保守，但与其他四种引起轻微症状的人类冠状病毒不同。通过对SARS-CoV-2蛋白质组的研究，发现包括S蛋白在内的6个蛋白质和5个非结构蛋白（nsp 3、3CL-pro和nsp 8 -10）是SARS-CoV-2的粘附素，它们在病毒粘附和入侵宿主中起着重要作用。Vaxign-ML还预测S、nsp 3和nsp 8蛋白诱导高保护性抗原性。除了常用的S蛋白外，nsp 3蛋白尚未在任何冠状病毒疫苗研究中进行测试，并被选择用于进一步研究。nsp 3在SARS-CoV-2、SARS-CoV和MERS-CoV中比感染人类和其他动物的15种冠状病毒更保守。该蛋白质还被预测含有混杂的MHC-I和MHC-II T细胞表位，并且预测的线性B细胞表位被发现定位于该蛋白质的表面上。我们预测的疫苗靶点有潜力开发有效和安全的COVID-19疫苗。我们还提出，含有结构蛋白（Sp）和非结构蛋白（Nsp）的“Sp/Nsp鸡尾酒疫苗”将刺激有效的互补免疫应答。为了最终对抗新出现的COVID-19大流行，期望开发针对这种由SARS-CoV-2冠状病毒引起的高度传染性疾病的有效且安全的疫苗。我们的文献和临床试验调查表明，整个病毒，以及刺突（S）蛋白，核衣壳（N）蛋白和膜（M）蛋白，已被测试用于针对SARS和MERS的疫苗开发。然而，这些候选疫苗可能缺乏完全保护的诱导，并存在安全性问题。然后，我们应用Vaxign反向疫苗学工具和新开发的Vaxign-ML机器学习工具来预测COVID-19候选疫苗。通过对SARS-CoV-2蛋白质组的研究，发现包括S蛋白在内的6个蛋白质和5个非结构蛋白（nsp 3、3CL-pro和nsp 8 -10）是SARS-CoV-2的粘附素，它们在病毒粘附和入侵宿主中起着重要作用。Vaxign-ML还预测S、nsp 3和nsp 8蛋白诱导高保护性抗原性。除了常用的S蛋白外，nsp 3蛋白尚未在任何冠状病毒疫苗研究中进行测试，并被选择用于进一步研究。nsp 3在SARS-CoV-2、SARS-CoV和MERS-CoV中比感染人类和其他动物的15种冠状病毒更保守。该蛋白质还被预测含有混杂的MHC-I和MHC-II T细胞表位，以及定位于蛋白质的特定位置和功能结构域的线性B细胞表位。通过应用反向疫苗学和机器学习，我们预测了有效和安全的COVID-19疫苗开发的潜在疫苗靶点。然后，我们提出含有结构蛋白（Sp）和非结构蛋白（Nsp）的“Sp/Nsp鸡尾酒疫苗”将刺激有效的互补免疫应答。
To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and linear B-cell epitopes localized in specific locations and functional domains of the protein. By applying reverse vaccinology and machine learning, we predicted potential vaccine targets for effective and safe COVID-19 vaccine development. We then propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.