Coronavirus Resistance Database (CoV-RDB): SARS-CoV-2 susceptibility to monoclonal antibodies, convalescent plasma, and plasma from vaccinated persons

Abstract

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.

Antiviral and clinical activity of bamlanivimab in a randomized trial of non-hospitalized adults with COVID-19

“…Illumina library construction was performed using the Nextera XT Library Prep Kit (Illumina). Sequencing was performed on the Illumina MiSeq platform and deep sequencing data analysis was carried out using the Stanford Coronavirus Antiviral & Resistance Database (CoVDB) platform ( https://covdb.stanford.edu/sierra/sars2/by-reads/?cutoff=0.01&mixrate=0.01 ) 26 . Input FASTQ sequence alignment with Wuhan-Hu-1 reference was done using MiniMap2 version 2.22 in CodFreq pipeline ( https://github.com/hivdb/codfreq ).…”

“…The output of MiniMap2, an aligned SAM file, is converted to a CodFreq file by an in-house written Python script using a PySam library (version: 0.18.0) and further analyzed with the CoVDB. SARS-COV-2 variant calling was done using 3 different variant calling platforms, namely, CoVDB 26 , Scorpio call v1.2.123 ( https://pangolin.cog-uk.io/ ) 27 , and Nextclade v.1.13.2 ( https://clades.nextstrain.org/ ) 28 . PCR and sequencing runs were performed once with the appropriate positive and negative controls.…”

Potential intervariant and intravariant recombination of Delta and Omicron variants

“…The structural illustration was generated using PDB: Mol Viewer. The linear mapping of amino acid mutations and labeling of SARS‐CoV‐2 susceptibility were obtained from Coronavirus Resistance Database (CoV‐RDB) 14 …”

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network

“…Therefore, the network approach led to a 2.3-fold improvement in success rate, compared to the 7.94% success rate of the NCATS compound list. We further cross-referenced our compounds with the antivirals reported through various publications catalogued in the Coronavirus resistance database CoV-RDB database ( Supplementary Table S4 ) (50).…”