Clement Nathan, Rasheed Muhibur, Bajaj Chandrajit Lal
Department of Computer Science, The University of Texas at Austin , Austin, Texas.
J Comput Biol. 2018 Jan;25(1):51-71. doi: 10.1089/cmb.2017.0218.
Most of the existing research in assembly pathway prediction/analysis of viral capsids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assumption does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continue throughout the assembly process until stabilization. This article presents a statistical-ensemble-based approach that samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Further, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible subassemblies to bind in all possible combinations. We represent the resulting assembly graph in two different ways: First, we use the Wilcoxon signed-rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. We applied these protocols on the feline panleukopenia virus and the Nudaurelia capensis virus. Results from these experiments showed a significant departure from those that one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and we provide a statistical framework as an important first step toward assembly pathway prediction with high statistical confidence.
现有的大多数关于病毒衣壳组装途径预测/分析的研究都做了一个简化假设，即中间状态的构型可以直接从整个衣壳的最终构型中提取。这个假设没有考虑组成蛋白的构象变化以及在整个组装过程中持续到稳定之前结合界面的微小变化。本文提出了一种基于统计系综的方法，该方法通过单体之间的相对局部取向对每个单体的构型空间进行采样，以捕捉结合和构象中的不确定性。此外，我们不是使用更大的衣壳粒（三聚体、五聚体）作为构建块，而是允许所有可能的亚组装体以所有可能的组合方式结合。我们用两种不同的方式表示由此产生的组装图：第一，我们使用威尔科克森符号秩检验来比较在采样构象上计算的结合自由能分布，以预测可能的途径。第二，我们将转变的化学平衡方面表示为一个贝叶斯因子图，其中结合和解离都是基于浓度和结合自由能进行建模的。我们将这些方案应用于猫泛白细胞减少症病毒和卡普斯线虫病毒。这些实验的结果表明，与仅考虑蛋白质的静态构型时所得到的结果有显著差异。因此，我们确立了用于途径分析的不确定性感知方案的重要性，并提供了一个统计框架，作为朝着具有高统计置信度的组装途径预测迈出的重要第一步。
