{"id":6667,"date":"2021-03-02T14:20:53","date_gmt":"2021-03-02T14:20:53","guid":{"rendered":"https:\/\/www.blopig.com\/blog\/?p=6667"},"modified":"2021-06-22T14:24:34","modified_gmt":"2021-06-22T13:24:34","slug":"the-coronavirus-antibody-database-10-months-on-10x-the-data","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2021\/03\/the-coronavirus-antibody-database-10-months-on-10x-the-data\/","title":{"rendered":"The Coronavirus Antibody Database: 10 months on, 10x the data!"},"content":{"rendered":"\n

Back in May 2020, we released the Coronavirus Antibody Database<\/a> (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…<\/p>\n\n\n\n

10 months later (2nd March 2021), we now have tracked 2,673<\/strong> coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.<\/p>\n\n\n\n

\"\"<\/a>
The entire contents CoV-AbDab database as of 2nd March 2021.<\/figcaption><\/figure>\n\n\n\n\n\n\n\n

The vast majority of these are complementary to SARS-CoV-2<\/strong>: 2,390\/2,673 (89.4%), and of these 773\/2,390 (32.3%) bind sufficiently strongly to a neutralising epitope to inhibit viral replication, primarily within the receptor-binding domain (RBD, 699\/773). The remaining 74 bind elsewhere on the spike protein, primarily to N-Terminal domain, but also to the S2 domain and the S1\/S2 furin cleavage site. <\/p>\n\n\n\n

This implies a broad range of neutralisation mechanisms, including direct competition for the ACE-2 binding site, physical inhibition of the transition from RBD-down to RBD-up, blocking of the transition of the spike protein to its membrane fusion state, and allosteric conformational modification. Capturing such a large spread of neutralising antibodies is necessary to rapidly identify which SARS-CoV-2 protective immunoglobulins are present in the deep-sequenced response\/memory repertoires of infected or vaccinated individuals.<\/p>\n\n\n\n

Many of the antibodies in CoV-AbDab are also cross-reactive with other epidemic\/seasonal coronaviruses. This knowledge could be key to prophylactic design, thinking forward to protective medicines that may help to contain future epidemic betacoronavirus outbreaks.<\/p>\n\n\n\n

\"\"<\/a>
All these antibodies are cross-reactive between SARS-CoV-1 and SARS-CoV-2, and have the ability to neutralise (to different extents) one or both viruses.<\/figcaption><\/figure>\n\n\n\n

Across binders to all coronaviruses, 1,636 antibodies have been found that engage the receptor-binding domain<\/strong> from a wide range of genetic origins. This is an unpredicented number of confirmed complementary molecules against a relatively small antigenic region. CoV-AbDab therefore represents an enormous opportunity for the antibody function prediction community, where often a limited diversity of “true binder” data precludes learning more general determinants of complementarity. Critical assessments to understand just how diverse a set of antibodies are required at training-time to learn generalisable features about binders to a particular epitope may be made possible with the scale of data now in CoV-AbDab.<\/p>\n\n\n\n

It is important to recognise that CoV-AbDab reflects the biases of the experiments currently performed into SARS-CoV-2. Binders to the spike protein, and in particular to neutralising epitopes in the RBD, are likely to be overrepresented relative to their expected proportion of a natural infection response. Most antibodies have been ‘baited’ from natural response repertoires (this is a positive bias from the perspective of the drug discovery community, as they are typically more developable than antibodies sourced from high-throughput in vitro assays). Finally, all studies referenced in CoV-AbDab use antigens from wildtype\/early variants of SARS-CoV-2 as baits. Binders to these viruses may differ from binders to the latest dominant strains. Future papers relevant to CoV-AbDab are likely to investigate the role of antigenic drift on immunogenicity, and so the database will probably have to adapt to communicate which strain of SARS-CoV-2 was tested in each study.<\/p>\n\n\n\n

Nonetheless, even with its current biases, the data in CoV-AbDab represents a huge opportunity and we expect it to grow still further in the months to come (we are tracking 60+ papers with expected data). We’re eager to see what follow-on investigations may result from this unique dataset.<\/p>\n","protected":false},"excerpt":{"rendered":"

Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, […]<\/p>\n","protected":false},"author":45,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[123,403,361,341,10,186,189,191,228,202,272],"tags":[141,315,323,352],"ppma_author":[532],"class_list":["post-6667","post","type-post","status-publish","format-standard","hentry","category-commentary","category-coronavirus","category-data-science","category-databases","category-groupmeetings","category-immunoinformatics","category-machine-learning","category-protein-engineering","category-protein-structure","category-proteins","category-software-services","tag-antibodies","tag-coronavirus","tag-covid-19","tag-sars-cov-2"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":532,"user_id":45,"is_guest":0,"slug":"matt","display_name":"Matthew Raybould","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/71f525eb0d7861fad46de36f6b8f3a4cfd89c5cc6b13e48c5dc6e79a82c5b591?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6667","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/45"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=6667"}],"version-history":[{"count":5,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6667\/revisions"}],"predecessor-version":[{"id":6989,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6667\/revisions\/6989"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=6667"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=6667"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=6667"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=6667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}