论文标题
用于比较SARS COVID（COV-2，COV）和MERS COVID的可传播性和发病机理的非结构蛋白的细胞自动机模型
Cellular Automata Model for Non-Structural Proteins Comparing Transmissibility and Pathogenesis of SARS Covid (CoV-2, CoV) and MERS Covid
论文作者
论文摘要
与SARS COV（2003）相比，SARS COV-2（2019）的可传递性明显更高，这可以归因于结构蛋白的突变（Spike S，Spike S，Nucleocapsid N，Membrane M和Invelope E）以及非结构蛋白（NSP）（NSPS）和附属蛋白（ORFS（ORFS）的非结构性蛋白质（ORFS）的作用，以进行病毒复制，并分配。非结构性蛋白质（NSP）可利用宿主蛋白质合成机制来启动病毒复制，以及宿主免疫防御的中和。 16 NSP中的关键蛋白质是非结构蛋白NSP1，也称为铅蛋白。 NSP1通过阻止主机翻译来领导劫持主机资源的过程。本文集中于基于细胞自动机的增强机器学习（CAML）模型的SARS COVID（COV-2，COV）和MERS COVID的NSP分析，用于生物串。该计算模型比较了结构 - COV -2与COV的功能的偏差，该功能采用了NSP的氨基酸链的CA演化而得出的CAML模型参数。该比较分析表明 - （i）与COV相比，与COV相比，主要NSP的可传播性更高，以及（ii）在毒力和发病机理方面，MERS与SARS COV的偏离。已经设计了一个机器学习（ML）框架，以将CAML模型参数映射到体外/体内/硅内实验研究中报告的物理领域特征。 ML框架使我们能够学习从三种病毒的16个NSP的突变研究中得出的允许模型参数范围。
Significantly higher transmissibility of SARS CoV-2 (2019) compared to SARS CoV (2003) can be attributed to mutations of structural proteins (Spike S, Nucleocapsid N, Membrane M, and Envelope E) and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly and shedding. The non-structural proteins (nsps) avail host protein synthesis machinery to initiate viral replication, along with neutralization of host immune defense. The key protein out of the 16 nsps, is the non-structural protein nsp1, also known as the leader protein. Nsp1 leads the process of hijacking host resources by blocking host translation. This paper concentrates on the analysis of nsps of SARS covid (CoV-2, CoV) and MERS covid based on Cellular Automata enhanced Machine Learning (CAML) model developed for study of biological strings. This computational model compares deviation of structure - function of CoV-2 from that of CoV employing CAML model parameters derived out of CA evolution of amino acid chains of nsps. This comparative analysis points to - (i) higher transmissibility of CoV-2 compared to CoV for major nsps, and (ii) deviation of MERS covid from SARS CoV in respect of virulence and pathogenesis. A Machine Learning (ML) framework has been designed to map the CAML model parameters to the physical domain features reported in in-vitro/in-vivo/in-silico experimental studies. The ML framework enables us to learn the permissible range of model parameters derived out of mutational study of sixteen nsps of three viruses.
