Comprehensive Surveillance of Pemivibart (VYD2311) Escape Mutations in 9,398,268 SARS-CoV-2 Spike Protein Sequences 2020 up to 2025 Q3

This dataset presents a large-scale genomic surveillance analysis of selected spike mutations which may or may not disrupt pemivibart (VYD2311) monoclonal antibody binding. We analyzed 9,398,268 SARS-CoV-2 spike protein sequences. Pemivibart is a next-generation monoclonal antibody therapy for COVID-19, and understanding potential escape mutations is crucial for monitoring therapeutic efficacy.<b>Methods:</b>Analyzed 9,398,268 SARS-CoV-2 spike protein sequences from global databases.We employed position-based mutation detection against Wuhan-Hu-1 reference (YP_009724390.1) We systematically scanned for 11 known pemivibart escape mutations: R346T, K444T, F456L, F486P, S371F, N460K, S417T, S501T, S446A, S444T, S500TIdentified both individual mutations and mutation constellations (combinations)Data provided in compressed TSV format with comprehensive metadata<b>Results:</b>8,913,005 sequences (94.84%) contained at least one pemivibart escape mutationIdentified 116 distinct mutation constellationsMutation distribution: single mutations to 8-mutation combinationsMost prevalent mutations: <b>S500T, R346T, F486P, F456L</b>Constellation analysis reveals complex mutation patterns that may disrupt pemivibart binding epitopes.<b>Data Contents:</b>Individual mutation files for all 11 target mutations116 constellation files showing mutation combinationsFiles provided in both Zstandard (.zst) and Gzip (.gz) formatsComplete summary statistics and metadataSequence accessions, collection years, and geographic origins<b>Potential Impact:</b>The identified single and multiple mutation constellations may disrupt pemivibart epitopes and impact therapeutic efficacy. This comprehensive dataset enables researchers to monitor resistance emergence, inform treatment guidelines, and support public health surveillance.<b>Usage Notes:</b>Files are provided in tab-separated format with sequence headers, accession numbers, collection metadata, and mutation information. Compressed formats ensure efficient storage while maintaining data accessibility.<b>Study by</b>: [e-mail]

本数据集针对可能影响或不影响**培米维单抗（pemivibart，VYD2311）**单克隆抗体结合活性的选定SARS-CoV-2刺突突变，开展了大规模基因组监测分析。本研究共分析9398268条SARS-CoV-2刺突蛋白序列。培米维单抗是一款针对COVID-19的下一代单克隆抗体治疗药物，明确其潜在逃逸突变对监测治疗效果至关重要。

**方法：**
从全球公共数据库中检索获取上述9398268条SARS-CoV-2刺突蛋白序列；以Wuhan-Hu-1参考株（YP_009724390.1）为参照，采用基于位点的突变检测方法；系统筛查了11种已报道的培米维单抗逃逸突变：R346T、K444T、F456L、F486P、S371F、N460K、S417T、S501T、S446A、S444T、S500T；同时鉴定了单突变以及突变组合（共突变模式）；数据集以压缩TSV格式存储，并附带完整元数据。

**结果：**
共有8913005条序列（占比94.84%）携带至少1种培米维单抗逃逸突变；共鉴定得到116种不同的突变组合体；突变分布涵盖单突变至8突变组合的各类模式；最常见的逃逸突变为：<b>S500T、R346T、F486P、F456L</b>；共突变分析揭示了可破坏培米维单抗结合表位的复杂突变模式。

**数据内容：**
包含针对11种目标突变的单突变文件各1份；116份突变组合文件，展示各类共突变模式；数据文件同时提供Zstandard（.zst）与Gzip（.gz）两种压缩格式；附带完整的统计汇总数据与元数据；包含序列登录号、采集年份以及地理来源信息。

**潜在影响：**
本次鉴定的单突变及多突变组合体可能破坏培米维单抗结合表位，进而影响治疗效果。本综合性数据集可为研究人员监测耐药性出现、优化治疗指南以及支持公共卫生监测工作提供有力支撑。

**使用说明：**
数据文件采用制表符分隔格式，包含序列头部信息、登录号、采集元数据以及突变相关信息。采用压缩格式可实现高效存储，同时保障数据的可访问性。

本研究作者：[e-mail]