Validating the Distinctiveness of the Omicron Lineage within the SARSCov-2 based on Protein Language Models

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS Current Bioinformatics Pub Date : 2024-04-30 DOI:10.2174/0115748936291075240409080924
Ke Dong, Jingyang Gao
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Abstract

Introduction: Variants of concern were identified in severe acute respiratory syndrome coronavirus 2, namely Alpha, Beta, Gamma, Delta, and Omicron. This study explores the mutations of the Omicron lineage and its differences from other lineages through a protein language model. Methods: By inputting the severe acute respiratory syndrome coronavirus 2 wild-type sequence into the protein language model evolving pre-trained models-1v, this study obtained the score for each position mutating to other amino acids and calculated the overall trend of a new variant of concern mutation scores. objective: Analyze the differences in the number of Omicron amino acid mutations compared to the other four VOC mutations using statistical methods, and use the protein language model esm-1v to analyze the specificity of Omicron amino acid mutations. Results: It is found that when the proportion of unobserved mutations to observed mutations is 4:15, Omicron still generates a large number of newly emerging mutations. It was found that the overall score for the Omicron family is low, and the overall ranking for the Omicron family is low. Conclusion: Mutations in the Omicron lineage are different from amino acid mutations in other lineages. The findings of this paper deepen the understanding of the spatial distribution of spike protein amino acid mutations and overall trends of newly emerging mutations corresponding to different variants of concern. This also provides insights into simulating the evolution of the Omicron lineage.
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基于蛋白质语言模型验证 SARSCov-2 中 Omicron 系的独特性
导言:在严重急性呼吸系统综合征冠状病毒 2 中发现了令人担忧的变异,即 Alpha、Beta、Gamma、Delta 和 Omicron。本研究通过蛋白质语言模型探索 Omicron 系的变异及其与其他系的差异。研究方法通过将严重急性呼吸道综合征冠状病毒 2 野生型序列输入进化预训练模型-1v 的蛋白质语言模型,本研究获得了变异为其他氨基酸的每个位置的得分,并计算了关注变异得分的新变体的整体趋势。 目标:分析变异为其他氨基酸的新变体的数量差异:利用统计学方法分析与其他四种 VOC 突变相比,Omicron 氨基酸突变数量的差异,并利用蛋白质语言模型 esm-1v 分析 Omicron 氨基酸突变的特异性。结果发现发现当未观察到的突变与观察到的突变的比例为 4:15 时,Omicron 仍会产生大量新出现的突变。研究发现,Omicron 家族的总体得分较低,Omicron 家族的总体排名也较低。结论Omicron 系的突变不同于其他系的氨基酸突变。本文的研究结果加深了人们对尖峰蛋白氨基酸突变的空间分布和新出现的突变的总体趋势的理解,这些突变对应于不同的关注变体。这也为模拟 Omicron 品系的进化提供了启示。
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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