Perovic Vladimir, Glisic Sanja, Veljkovic Milena, Paessler Slobodan, Veljkovic Veljko
Biomed Protection, Galveston, TX 77550, USA.
Galveston National Laboratory, Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA.
Entropy (Basel). 2023 Oct 19;25(10):1463. doi: 10.3390/e25101463.
The SARS-CoV-2 virus, the causative agent of COVID-19, is known for its genetic diversity. Virus variants of concern (VOCs) as well as variants of interest (VOIs) are classified by the World Health Organization (WHO) according to their potential risk to global health. This study seeks to enhance the identification and classification of such variants by developing a novel bioinformatics criterion centered on the virus's spike protein (SP1), a key player in host cell entry, immune response, and a mutational hotspot. To achieve this, we pioneered a unique phylogenetic algorithm which calculates EIIP-entropy as a distance measure based on the distribution of the electron-ion interaction potential (EIIP) of amino acids in SP1. This method offers a comprehensive, scalable, and rapid approach to analyze large genomic data sets and predict the impact of specific mutations. This innovative approach provides a robust tool for classifying emergent SARS-CoV-2 variants into potential VOCs or VOIs. It could significantly augment surveillance efforts and understanding of variant characteristics, while also offering potential applicability to the analysis and classification of other emerging viral pathogens and enhancing global readiness against emerging and re-emerging viral pathogens.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）是冠状病毒病（COVID-19）的病原体，以其基因多样性而闻名。世界卫生组织（WHO）根据关注的病毒变体（VOCs）以及感兴趣的变体（VOIs）对全球健康的潜在风险对其进行分类。本研究旨在通过开发一种以病毒刺突蛋白（SP1）为核心的新型生物信息学标准，加强对此类变体的识别和分类，SP1是宿主细胞进入、免疫反应中的关键因子，也是一个突变热点。为实现这一目标，我们首创了一种独特的系统发育算法，该算法基于SP1中氨基酸的电子-离子相互作用势（EIIP）分布，将EIIP熵计算为一种距离度量。该方法为分析大型基因组数据集和预测特定突变的影响提供了一种全面、可扩展且快速的方法。这种创新方法为将新出现的SARS-CoV-2变体分类为潜在的VOCs或VOIs提供了一个强大的工具。它可以显著加强监测工作并增进对变体特征的了解，同时还可能适用于其他新兴病毒病原体的分析和分类，并提高全球对新兴和再发病毒病原体的防范能力。
