Division of Infectious Diseases, Stanford University School of Medicine, Stanford, CA, United States of America.
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States of America.
PLoS One. 2022 Mar 9;17(3):e0261045. doi: 10.1371/journal.pone.0261045. eCollection 2022.
As novel SARS-CoV-2 variants with different patterns of spike protein mutations have emerged, the susceptibility of these variants to neutralization by antibodies has been rapidly assessed. However, neutralization data are generated using different approaches and are scattered across different publications making it difficult for these data to be located and synthesized. The Stanford Coronavirus Resistance Database (CoV-RDB; https://covdb.stanford.edu) is designed to house comprehensively curated published data on the neutralizing susceptibility of SARS-CoV-2 variants and spike mutations to monoclonal antibodies (mAbs), convalescent plasma (CP), and vaccinee plasma (VP). As of December 31, 2021, CoV-RDB encompassed 257 publications including 91 (35%) containing 9,070 neutralizing mAb susceptibility results, 131 (51%) containing 16,773 neutralizing CP susceptibility results, and 178 (69%) containing 33,540 neutralizing VP results. The database also records which spike mutations are selected during in vitro passage of SARS-CoV-2 in the presence of mAbs and which emerge in persons receiving mAbs as treatment. The CoV-RDB interface interactively displays neutralizing susceptibility data at different levels of granularity by filtering and/or aggregating query results according to one or more experimental conditions. The CoV-RDB website provides a companion sequence analysis program that outputs information about mutations present in a submitted sequence and that also assists users in determining the appropriate mutation-detection thresholds for identifying non-consensus amino acids. The most recent data underlying the CoV-RDB can be downloaded in its entirety from a GitHub repository in a documented machine-readable format.
随着具有不同刺突蛋白突变模式的新型 SARS-CoV-2 变体的出现,这些变体对抗体中和的敏感性已被迅速评估。然而,中和数据是使用不同的方法生成的,并且分散在不同的出版物中,使得这些数据难以定位和综合。斯坦福冠状病毒抗性数据库 (CoV-RDB;https://covdb.stanford.edu) 旨在全面收录有关 SARS-CoV-2 变体和刺突突变对单克隆抗体 (mAb)、恢复期血浆 (CP) 和疫苗接种者血浆 (VP) 的中和敏感性的已发表数据。截至 2021 年 12 月 31 日,CoV-RDB 包含 257 篇出版物,其中 91 篇 (35%) 包含 9070 种中和 mAb 敏感性结果,131 篇 (51%) 包含 16773 种中和 CP 敏感性结果,178 篇 (69%) 包含 33540 种中和 VP 结果。该数据库还记录了 SARS-CoV-2 在 mAb 存在下体外传代过程中选择的哪些刺突突变以及在接受 mAb 作为治疗的人中出现的哪些突变。CoV-RDB 界面通过根据一个或多个实验条件过滤和/或聚合查询结果,以不同的粒度交互显示中和敏感性数据。CoV-RDB 网站提供了一个伴随的序列分析程序,该程序输出提交序列中存在的突变信息,还可以帮助用户确定用于识别非共识氨基酸的适当突变检测阈值。CoV-RDB 背后的最新数据可以从 GitHub 存储库以记录在案的机器可读格式完整下载。