PDF(Pubmed)
Abstract：
When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and genome rearrangements. Attenuation typically involves the reduction in virus replication, due to direct effects on viral structural and replicative machinery or viral factors that antagonize host defense or cause disease. However, attenuation must balance reduced replication and immunogenic antigen expression. In the present study, we explored a new approach in order to discover attenuating mutations. Specifically, we used protein structure modeling and computational methods to identify amino acid substitutions in the RSV nonstructural protein 1 (NS1) predicted to cause various levels of structural perturbation. Twelve different mutations predicted to alter the NS1 protein structure were introduced into infectious virus and analyzed in cell culture for effects on viral mRNA and protein expression, interferon and cytokine expression, and caspase activation. We found the use of structure-based machine learning to predict amino acid substitutions that reduce the thermodynamic stability of NS1 resulted in various levels of loss of NS1 function, exemplified by effects including reduced multi-cycle viral replication in cells competent for type I interferon, reduced expression of viral mRNAs and proteins, and increased interferon and apoptosis responses.
摘要：
在设计呼吸道合胞病毒（RSV）减毒活疫苗时，减毒突变可以通过生物选择或反向遗传操作开发，可能包括点突变，密码子和基因缺失，和基因组重排。减毒通常涉及减少病毒复制，由于对病毒结构和复制机制或拮抗宿主防御或引起疾病的病毒因子的直接影响。然而,减毒必须平衡减少的复制和免疫原性抗原表达。在本研究中,我们探索了一种新的方法来发现减毒突变。具体来说,我们使用蛋白质结构建模和计算方法来鉴定RSV非结构蛋白1(NS1)中的氨基酸取代,预测这些取代会导致不同水平的结构扰动.将预测会改变NS1蛋白结构的12种不同突变引入感染性病毒中，并在细胞培养物中分析对病毒mRNA和蛋白表达的影响。干扰素和细胞因子表达，和半胱天冬酶激活。我们发现使用基于结构的机器学习来预测降低NS1热力学稳定性的氨基酸取代会导致NS1功能的不同程度的损失。例如，包括减少多周期病毒复制的细胞有能力为I型干扰素，降低病毒mRNA和蛋白质的表达，和增加干扰素和细胞凋亡反应。
