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病毒进化及病毒和宿主作物之间相互作用的研究
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  • 英文题名:Mechanism of Virus Evolution and Virus-Crop Interaction
  • 作者:夏真
  • 论文级别:博士
  • 学科专业名称:作物学
  • 学位年度:2011
  • 导师:朱军 ; Ruhong Zhou
  • 学科代码:0901
  • 学位授予单位:浙江大学
  • 论文提交日期:2011-12-23
  • 答辩委员会主席:蒋立希
摘要
病毒进化及突变模式,病毒和作物之间相互作用的机理越来越受到作物学家们的关注。在这篇论文中,作者尝试在微观尺度下,利用生物信息学和分子力学工具揭示蛋白质的动态结构及相关重要的生物功能。本研究探讨了在RNA干涉中Argonaute蛋白识别小RNA和分解mRNA的机理,计算了病毒蛋白和宿主RNA的相互结合能。同时利用交互信息模型,发展了基于位点转换的流感病毒进化网络。整个工作主要分为下面三个部分。
     在第一部分,作者利用分子动力学模拟(MD)来研究p19蛋白和siRNA的识别机制及其突变体的效应。研究的对象包括野生型和W39G&W42G突变体。常规MD和拉伸分子动力学(SMD)都表明当p19与小RNA结合时,突变型要比野生型不稳定。这些结果和实验的结果是一致的。与此同时,作者用自由能微扰(FEP)成功预测了p19和RNA结合自由能的变化,相对于野生型,单氨基酸突变体(W39G)损失了6.98±0.95kcal/mol的结合自由能,双氨基酸突变W39/42G损失了12.8±1.0kcal/mol的结合自由能,并且得出90%的贡献来自范德华力相互作用。这些结果表明W39/42G突变打断了RNA碱基和色氨酸(tryptophan)之间π-π键相互作用。这些大规模的模拟提供了RNA病毒蛋白和其宿主键协同进化关系的新见解。
     在第二部分,作者揭示了Argonaute二聚体识别和降解mRNA的机制并建立了两种最近报道的靶链RNA (11-nt和15-nt)和Argonaute三聚体的三维结构模型。对于两个不同长度的靶RNA链,我们得出了相似的模拟结果:当碱基错配个数在引导链(guide) RNA和靶链(target) RNA的"seed"位置之间增加时,A-form结构的双螺旋会逐渐变形,当碱基错配个数超过两个时,Argonaute三聚体将不能稳定存在。在极端的4个碱基错配情况下,核苷酸双链与Argonaute的Ll和L2片段的氢键将全部断掉,由此诱发了整个PAZ功能域在空间上沿着Ll和L2片段的相对旋转,因而导致Argonaute蛋白整个核酸结合区被打开。这种在核酸和PAZ功能域之间的长距离的相互作用,揭示了"seed"区域在引导链-靶链结合中的重要作用:它不仅决定了引导链-靶链成核和延伸,而且控制了整个PAZ功能域的构象变化。除此以外,我们把引导链由DNA换成了更加普遍的RNA,并得出了相似的结论。这些结果支持了RNA也可能在Thermus thermophilus昀RNA干涉中作为引导链,并且对碱基错配不像DNA作为引导链那样敏感。
     在最后一节,作者通过交互信息预型对流感病毒抗原性及其遗传进化途径进行了分析。交互信息模型作用于每一个hemagglutinin (HA)蛋白位点,从而生成位点转移网络(STN)。STN网络表明大部分的动态相互作用位置在抗原表位周围和受体结合域(RBD)地区,并且突变位点对将要突变成的氨基酸的种类有强烈的偏好。STN网络还表明,随着时间的推移,抗原的变化也在积累并当在多个抗原位点同时突变时,整个病毒的表型会发生较大的变化。通过聚类分析把整个STN互做网络分成若干个小的子网络揭示了更多具体的关于流感抗原的特征:子网路内部的位点和子网路之间连接的位点对于流感的抗原漂移是一样重要的。最后作者成功提出了基于STN网络的预测HA蛋白突变的5步模型。在对2003-2004季度突变位点预测中,该模型获得了70%的准确性。该模型还预测了最近的下一个可能的抗原漂移。同样,STN网络的结果和进化树分析以及实验结果均相吻合。
Capturing the evolution trend of virus and understanding the interaction between virus and its host plants are of great concern in crop science. In this thesis, the author tries to explain the fundamental biological processes in micro-scale by investigating corresponding protein dynamics structures using bioinformatics tools and molecular mechanics. The mechanism of how Argonaute protein recognize small RNAs and cleave target mRNA in RNA interference, as well as the binding affinity between virus protein p19and its host RNA are explored. The genetic evolution of influenza virus is mapped using the mutual information based site transition network. The entire work is divided by the following three sections.
     In the first section, the p19-siRNA recognition mechanism and mutation effects were studied by molecular dynamics simulations of the wild-type and mutant p19protein (W39G&W42G) binding with a21-nt siRNA duplex. Simulations with standard molecular dynamics (MD) and steered molecular dynamics have shown that the double mutant structure is indeed much less stable than the wild-type, consistent with the recent experimental findings. The free energy perturbation successfully predicted a binding affinity loss of6.98±0.95kcal/mol for the single mutation W39G, and12.8±1.0kcal/mol loss for the double mutation W39/42G. with the van der Waals interactions dominating the contribution (-90%). These results indicate that the W39/42G mutations essentially destroy the important p19-siRNA recognition by breaking the strong stacking interaction between Cyt1and Gua'19with end-capping tryptophans. These large scale simulations might provide new insights into the interactions and co-evolution relationship between RNA virus proteins and their hosts.
     The second section examined the recognition mechanism and cleavage activity of target mRNA by Argonaute silencing complexes. Both the recently reported11-nt and15-nt guide-target strands-Ago complexes are modeled. Our simulations show comparable results for both the11-and15-nt nucleic-acid strands, whose A-form-like helix duplex gradually distorts as the number of mismatches at the seed region increases and the complex can survive no more than two mismatches. In the extreme four-mismatch mutant complex, the hydrogen bonds between the nucleic-acid duplex and L1/L2segments of Ago protein are broken, which introduce a bending motion of the PAZ domain along the L1/L2"hinge-like" connection region and result in the opening of the nucleic-acid-binding channel. These long-range interactions between the seed region and PAZ domain, mediated by the L1/L2segments, reveals the central role of the seed region in the guide-target recognition-it not only determines the guide-target duplex's nucleation, and propagation, but also controls the dynamics of the large conformational changes in the PAZ domain. The catalytic activities of RNA guided mRNA cleavage are also investigated in our simulation by replacing the guide DNA by corresponding RNA strand in the Ago-DNA-mRNA complexes. Similar behaviors are found for the Ago-RNA-mRNA complexes, which support the hypothesis that small RNAs might also be used as the guide strand for Thermus thermophilus Ago-like proteins and may also have larger tolerance for the mismatches in the seed region.
     In the final section, the antigenic and genetic evolution pathways of influenza virus is predicted by mutual information model. The mutual information method was used to design a site transition network (STN) for each amino acid site in the hemagglutinin (HA) sequence. The STN network indicates that most of the dynamic interactions are positioned around the epitopes and the RBD regions, with strong preferences in both the mutation sites and amino acid types being mutated to. The network also shows that antigenic changes accumulate over time, with occasional large changes due to multiple co-occurring mutations at antigenic sites. The cluster analysis by subdividing the STN into several subnetworks reveals a more detailed view about the features of the antigenic change:The characteristic inner sites and the connecting inter-subnetwork sites are both responsible for the drifts. A novel5-step prediction algorithm based on the STN shows a reasonable accuracy in reproducing historical HA mutations. For example, our method can reproduce the2003-2004A/H3N2mutations with~70%accuracy. The method also predicts seven possible mutations for the next antigenic drift in the coming season. The site transition network approach also agrees well with the phylogenetic tree and antigenic maps based on HA inhibition assays.
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