Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Phys Biol. 2011 Oct;8(5):055010. doi: 10.1088/1478-3975/8/5/055010. Epub 2011 Aug 10.
Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.
尽管蛋白质的多尺度时空组织具有公认的重要性，但大多数计算工具只能访问有限的时间和空间尺度，从而忽略了不同尺度之间复杂耦合对蛋白质行为的影响。我们从编码蛋白质结构的物理化学原子相互作用网络出发，引入了一种基于多尺度图划分的方法，该方法可以揭示跨越整个尺度范围的蛋白质的分区和组织层次，揭示在不同组织层次上发生的生物学特征，并跟踪它们在不同尺度上的影响。此外，我们引入了一种稳健性度量标准，通过生成基于生物化学的替代随机图模型来量化分区的相关性。我们将该方法应用于疟疾寄生虫分子马达中的肌球蛋白尾相互作用蛋白的四个不同构象，并研究了已通过实验解决的特性，如闭合机制、保守簇的存在，以及通过计算突变分析确定关键残基结合。
