基于共振识别模型的SARS-CoV-2 δ(印度)变体传染性分析

I. Cosic, D. Cosic, I. Loncarevic
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引用次数: 1

摘要

这篇论文是我们之前工作的延续,我们使用共振识别模型(RRM)分析了SARS-CoV-2病毒的不同变体(英国、南非、巴西和印度(Kappa)),这是一种能够分析蛋白质功能和相互作用的生物物理模型。我们之前已经确定了这些SARS-CoV-2病毒变体的传染性与每种变体在RRM特征频率上的信号强度之间的相关性。在这里,我们将这一分析扩展到Delta(印度)SARS-CoV-2病毒变体,这种病毒具有极强的传染性,正在世界各地迅速传播。我们对Delta(印度)变体的研究结果与我们之前的RRM命题完全一致,即病毒传染性与RRM特征频率上的信号强度成正比。这些结果可以解释为什么三角洲(印度)变异体更具传染性。所有这些例子都有很强的相关性,因此我们可以提出RRM模型可以作为分析变异病毒传染性的通用工具。
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Analysis of Delta (Indian) Variant of SARS-CoV-2 Infectivity using Resonant Recognition Model
This manuscript is continuation of our previous work, where we have analyzed different variants of SARS-CoV-2 virus (UK, South African, Brazilian, and Indian (Kappa)) using Resonant Recognition Model (RRM), which is biophysical model capable to analyze protein function and interaction. We have previously identified correlation between infectivity of these SARS-CoV-2 virus variants with strength of signal at RRM characteristic frequencies for each variant. Here, we have extended this analysis for Delta (Indian) SARS-CoV-2 virus variant, which is extremely infectious and is rapidly spreading around the World. Our results with Delta (Indian) variant are in complete agreement with our previous RRM proposition that viral infectivity is proportional to strength of signal at RRM characteristic frequency. These results can explain why Delta (Indian) variant is more infectious. With strong correlation obtained in all these examples, we can propose here that RRM model can be used as general tool to analyze infectivity of mutated virus variants.
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