FLC | LLNL antibody, anti-viral research aids COVID-19 response

LLNL antibody, anti-viral research aids COVID-19 response

March 24, 2020

Scientists at Lawrence Livermore National Laboratory (LLNL) are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs to combat the disease.

A COVID-19 response team of LLNL researchers from various disciplines has used modeling simulation, along with machine learning, to identify about 20 initial, yet promising, antibody designs from a nearly infinite set of potentials and to examine millions of small molecules that could have anti-viral properties. The candidates will need to be synthesized and experimentally tested, but progress is being made.

When the COVID-19 outbreak began, LLNL’s Adam Zemla developed and published a predicted 3D protein structure of the virus, which was downloaded and used by more than a dozen outside research groups. Since then, the actual crystal structure of the key protein from SARS-CoV-2, the virus that causes COVID-19, has been determined, which closely matched the team’s predictions, researchers said.

Armed with the virus’ predicted 3D structure and a few antibodies known to bind and neutralize SARS, an LLNL team led by Daniel Faissol and Thomas Desautels used two HPC clusters to perform AI-driven virtual screening of antibodies capable of binding to SARS-CoV-2, generating high-fidelity simulations to test the molecular interactions for efficacy. The modeling platform, supported by the Defense Advanced Research Projects Agency (DARPA) and internal Laboratory Directed Research and Development (LDRD) funding, is the first of its kind in integrating experimental data, structural biology, bioinformatic modeling and molecular simulations — driven by a machine learning algorithm — to design antibody candidates, This platform was used to identify potential high value modifications to the SARS antibodies so that it binds to SARS-CoV-2.

The approach has not only sped up the process considerably over selection guided solely by human intuition — narrowing down the number of antibody candidates from 1039 possibilities to a handful in a matter of weeks — but has focused on areas where scientists may not have otherwise looked.

Another component of the multi-pronged response involves antiviral drug design. A group of Lab scientists led by Felice Lightstone and Jonathan Allen recently used dedicated access time on the entire Quartz supercomputing cluster to perform virtual screening of small molecules against two COVID-19 proteins. Using LLNL-customized software, created by Lab scientist Xiaohua Zhang, the LLNL team has performed a large-scale computational run to screen 26 million molecules against four protein sites (totaling more than 100 million docking calculations) to identify compounds that possibly could prevent infection or treat COVID-19.

LLNL also is adapting its portable, rapid PCR-based molecular diagnostics platform (Bio ID) developed by LLNL biomedical scientist Larry Dugan, as a potential tool to quickly diagnose COVID-19.