论文标题
分数 - 易感感染模型：COVID-19主要蛋白酶研究的定义和应用
Fractional-order susceptible-infected model: definition and applications to the study of COVID-19 main protease
论文作者
论文摘要
我们提出了一个模型，用于在蛋白质的相互作用网络中传播扰动的蛋白质的氨基酸。该动力学由基于Caputo分数衍生物的易感感染（SI）模型组成。我们找到了该模型分析解的上限，该解析代表了跨蛋白质残基网络扰动传播的糟糕情况。该上限以氨基酸间相互作用网络的邻接矩阵的Mittag-Leffler函数表示。然后，我们将此模型应用于SARS COV-2主要蛋白酶抑制剂产生的扰动的传播。我们发现，蛋白酶强抑制剂产生的扰动远离结合位点，证实了蛋白质内通信的远距离性质。相反，最弱的抑制剂只会在结合位点周围的近距离环境中传递其扰动。这些发现可能有助于针对这种新的冠状病毒的候选药物设计。
We propose a model for the transmission of perturbations across the amino acids of a protein represented as an interaction network. The dynamics consists of a Susceptible-Infected (SI) model based on the Caputo fractional-order derivative. We find an upper bound to the analytical solution of this model which represents the worse-case scenario on the propagation of perturbations across a protein residue network. This upper bound is expressed in terms of Mittag-Leffler functions of the adjacency matrix of the network of inter-amino acids interactions. We then apply this model to the analysis of the propagation of perturbations produced by inhibitors of the main protease of SARS CoV-2. We find that the perturbations produced by strong inhibitors of the protease are propagated far away from the binding site, confirming the long-range nature of intra-protein communication. On the contrary, the weakest inhibitors only transmit their perturbations across a close environment around the binding site. These findings may help to the design of drug candidates against this new coronavirus.
