About: Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing
Creator	Zemla, Adam Desautels, Thomas Faissol, Daniel Franco, Magdalena Lau, Edmond
Source	BioRxiv
abstract	Rapidly responding to novel pathogens, such as SARS-CoV-2, represents an extremely challenging and complex endeavor. Numerous promising therapeutic and vaccine research efforts to mitigate the catastrophic effects of COVID-19 pandemic are underway, yet an efficacious countermeasure is still not available. To support these global research efforts, we have used a novel computational pipeline combining machine learning, bioinformatics, and supercomputing to predict antibody structures capable of targeting the SARS-CoV-2 receptor binding domain (RBD). In 22 days, using just the SARS-CoV-2 sequence and previously published neutralizing antibody structures for SARS-CoV-1, we generated 20 initial antibody sequences predicted to target the SARS-CoV-2 RBD. As a first step in this process, we predicted (and publicly released) structures of the SARS-CoV-2 spike protein using homology-based structural modeling. The predicted structures proved to be accurate within the targeted RBD region when compared to experimentally derived structures published weeks later. Next we used our in silico design platform to iteratively propose mutations to SARS-CoV-1 neutralizing antibodies (known not to bind SARS-Cov-2) to enable and optimize binding within the RBD of SARS-CoV-2. Starting from a calculated baseline free energy of −48.1 kcal/mol (± 8.3), our 20 selected first round antibody structures are predicted to have improved interaction with the SARS-CoV-2 RBD with free energies as low as −82.0 kcal/mole. The baseline SARS-CoV-1 antibody in complex with the SARS-CoV-1 RBD has a calculated interaction energy of −52.2 kcal/mole and neutralizes the virus by preventing it from binding and entering the human ACE2 receptor. These results suggest that our predicted antibody mutants may bind the SARS-CoV-2 RBD and potentially neutralize the virus. Additionally, our selected antibody mutants score well according to multiple antibody developability metrics. These antibody designs are being expressed and experimentally tested for binding to COVID-19 viral proteins, which will provide invaluable feedback to further improve the machine learning–driven designs. This technical report is a high-level description of that effort; the Supplementary Materials includes the homology-based structural models we developed and 178,856 in silico free energy calculations for 89,263 mutant antibodies derived from known SARS-CoV-1 neutralizing antibodies.
has issue date	2020-04-10(xsd:dateTime)
bibo:doi	10.1101/2020.04.03.024885
has license	biorxiv
sha1sum (hex)	673a9d33f3d75fb307b6c3ea4381b6e4a676ddab
schema:url	https://doi.org/10.1101/2020.04.03.024885
resource representing a document's title	Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing
schema:publication	bioRxiv
resource representing a document's body	covid:673a9d33f3d75fb307b6c3ea4381b6e4a676ddab#body_text
is schema:about of	named entity 'pathogens' named entity 'neutralize' named entity 'published' named entity 'kcal' named entity 'initial' named entity 'supercomputing' named entity 'interaction energy' named entity 'compared' named entity 'interaction' named entity 'computational' named entity 'SARS-CoV-1' named entity 'RELEASED' named entity 'ACCORDING' named entity 'SPIKE PROTEIN' named entity 'PUBLISHED' named entity 'ENERGIES' named entity 'NUMEROUS' named entity 'INCLUDES' named entity 'COVID-19' named entity 'predicted' named entity 'targeting' named entity 'extremely' named entity 'complex' named entity 'SARS-CoV-1' named entity 'free energy' named entity 'free energy' named entity 'derived' named entity 'Next' named entity 'published' named entity 'RBD' named entity 'accurate' named entity 'preventing' named entity 'weeks' named entity 'SARS-CoV-2' named entity 'neutralizing antibody' named entity 'binding' named entity 'machine' named entity 'SARS-CoV-2' named entity 'promising' named entity 'feedback' named entity 'selected' named entity 'neutralizing antibodies' named entity 'free energies' named entity 'SARS-CoV-1' named entity 'antibody' named entity 'spike protein' named entity 'antibody' named entity 'kcal' named entity 'SARS-CoV-2' named entity 'virus' named entity 'COVID-19 pandemic' named entity 'neutralizing antibody' named entity 'machine learning' named entity 'neutralizing antibodies' named entity 'antibody' named entity 'SARS-CoV-1' named entity 'pathogens' named entity 'RBD' named entity 'mitigate' named entity 'optimize' named entity 'SARS-CoV-1' named entity 'free energy' named entity 'SARS-CoV-2' named entity 'heuristic' named entity 'GBSA' named entity 'FoldX' named entity 'antibody' named entity 'glycoprotein' named entity 'protein' named entity 'SARS-CoV-2' named entity 'neutralizing antibodies' named entity 'feedback loop' named entity 'SARS-CoV-2' named entity 'antigen' named entity 'SARS-CoV-2' named entity 'wild-type' named entity 'molecular dynamics' named entity 'Profiler' named entity 'antibody' named entity 'SARS-CoV-2'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software