Modeling Effects of RNA on Capsid Assembly Pathways via Coarse-Grained Stochastic Simulation.
Modeling Effects of RNA on Capsid Assembly Pathways via Coarse-Grained Stochastic Simulation.
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DOI：
10.1371/journal.pone.0156547
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Schwartz R
中科院分区：
文献类型：
--
作者：
Smith GR;Xie L;Schwartz R
活细胞的环境与体外反应系统的环境大不相同，这一问题对使用体外模型或基于它们的计算机模拟来理解体内生物化学提出了巨大挑战。病毒衣壳为此类问题提供了一个很好的模型系统，因为它们通常具有很少的不同组分，使其适合体外和建模研究，但它们的组装可能涉及复杂的可能反应网络，这些反应无法通过任何现有的实验技术详细解决。我们以前适合动力学模拟参数散装在体外组装数据，以产生模拟和真实的数据之间的密切匹配，然后使用模拟来研究功能的组装，不能通过实验监测。目前的工作旨在项目如何组装在这些模拟适合在体外数据将被改变计算添加到系统的细胞环境的功能，特别是核酸的存在下，许多衣壳组装。这项工作的主要挑战是计算：在真实的病毒的尺度和参数域中模拟精细尺度的组装途径，计算成本太高，无法建立核酸相互作用的明确模型。我们通过应用核酸效应的分析模型来调整从体外数据中学习到的动力学速率参数，以了解这些调整（单独或组合）如何影响精细规模组装过程，从而绕过了这一限制。由此产生的模拟表现出令人惊讶的行为复杂性，具有不同的效果，通常协同作用，以驱动有效的组装和改变相对于体外模型的途径。这项工作展示了计算机模拟如何帮助我们了解体外和体内环境之间的组装可能存在哪些差异，以及细胞环境的哪些特征导致了这些差异。
The environment of a living cell is vastly different from that of an in vitro reaction system, an issue that presents great challenges to the use of in vitro models, or computer simulations based on them, for understanding biochemistry in vivo. Virus capsids make an excellent model system for such questions because they typically have few distinct components, making them amenable to in vitro and modeling studies, yet their assembly can involve complex networks of possible reactions that cannot be resolved in detail by any current experimental technology. We previously fit kinetic simulation parameters to bulk in vitro assembly data to yield a close match between simulated and real data, and then used the simulations to study features of assembly that cannot be monitored experimentally. The present work seeks to project how assembly in these simulations fit to in vitro data would be altered by computationally adding features of the cellular environment to the system, specifically the presence of nucleic acid about which many capsids assemble. The major challenge of such work is computational: simulating fine-scale assembly pathways on the scale and in the parameter domains of real viruses is far too computationally costly to allow for explicit models of nucleic acid interaction. We bypass that limitation by applying analytical models of nucleic acid effects to adjust kinetic rate parameters learned from in vitro data to see how these adjustments, singly or in combination, might affect fine-scale assembly progress. The resulting simulations exhibit surprising behavioral complexity, with distinct effects often acting synergistically to drive efficient assembly and alter pathways relative to the in vitro model. The work demonstrates how computer simulations can help us understand how assembly might differ between the in vitro and in vivo environments and what features of the cellular environment account for these differences.