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通过蛋白质内在无序预测理解病毒传播行为:冠状病毒

Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses.

作者信息

Goh Gerard Kian-Meng, Dunker A Keith, Uversky Vladimir N

机构信息

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA ; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228.

出版信息

J Pathog. 2012;2012:738590. doi: 10.1155/2012/738590. Epub 2012 Oct 14.

Abstract

Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior.

摘要

冠状病毒(CoV)除了是家畜和家禽的常见威胁外,还引发了2002年至2004年的人类严重急性呼吸综合征(SARS)疫情。然而,CoV行为的许多方面,包括其传播方式,仍有待充分了解。我们表明,病毒外壳中蛋白质内在无序的数量和分布特点可用于高效分析CoV的行为和传播模式。所提出的模型允许根据各种CoV在其膜(M)和核衣壳(N)中无序分布的特点进行分类。这种分类能够快速识别在传播中具有相似行为的病毒,而不论其基因亲缘关系如何。基于这一分析,开发了一种预测病毒传播行为的经验模型。该模型能够解释一些重要冠状病毒以前未被充分理解的行为方面。这种新的预测工具对于更好地从流行病学、临床和结构方面理解新出现病毒和已知已久病毒的行为可能是一个有用的工具。一种潜在的新疫苗策略可能涉及寻找那些在外壳无序分布特点与其行为之间存在进化不匹配特征的病毒株。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/088b/3477565/da7ac39c4c0c/JPATH2012-738590.001.jpg

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