LRZ to Help COVID-19 Researchers Access HPC Resources and supports first projects
Scientists pursuing research aimed at prevention, containment, remediation, or cures related to the coronavirus pandemic are giving expedited access to HPC resources at LRZ as part of GCS, Germany’s three national supercomputing centres as well as part of PRACE, the Partnership for Advanced Computing in Europe.
Effective immediately (March 17, 2020), LRZ helps researchers working on COVID-19 to gain expedited access to computing resources. This includes access to SuperMUC-NG, our leadership class supercomputer. Scientists can also use the LRZ Compute Cloud and the LRZ Linux Cluster (CoolMUC-2 and CoolMUC-3) amongst others.
Recognizing the urgent need for new strategies to contain the global pandemic, LRZ has committed to fast-tracking applications for COVID-19 related computing time and minimizing hurdles during the application process. The first focus is research at the molecular level to understand the virus and develop vaccines and therapeutics. The second focus is epidemiological research to understand and forecast disease spread, and other related approaches aimed at understanding and halting the pandemic.
"Computational approaches can provide unique and critical insights into the biology and transmission of disease, " said Dr. Dieter Kranzlmüller, Director of the Leibniz Supercomputing Centre and GCS Chair. "LRZ and GCS are committed to doing everything we can to support the fight against the coronavirus, providing computing time as well as technical support to investigators working to contain its effects."
If you are a scientist interested in using supercomputing resources at the LRZ, please contact Dieter Kranzlmüller via leitung@lrz.de
Accepted COVID-19 research projects
To date (June 19, 2020), LRZ has awarded nearly 60 Mio core hours of computing time to four projects in the areas of biophysics, biochemistry, pharmacology and virology. Using different methods, including e.g. big data analytics methods, machine learning or molecular dynamics simulations, all projects support the search for drug discovery and active components as well as the discovery of antibodies.
“Our long-term joint efforts with some of the projects as well as our continued collaboration with scientists performing basic research in relevant areas allowed us to react so fast and help get projects going on our machines quickly. I’d like to take the opportunity to thank our colleagues internally and the LRZ Lenkungsausschuss, our partners at GCS and PRACE as well as at the Federal Ministry of Education and Research and the Bavarian State Ministry of Science and the Arts. Without the support of all sides involved we would not have been able to make this happen so quickly,” comments Kranzlmüller.
Title: Deployment of INCITE software environment on SuperMUC-NG for COVID-19 research
Project area: Biochemical simulations
Principle Investigator: Prof. Dr. Peter Coveney, University College London (UCL)
System: SuperMUC-NG
Amount: 69 Mio Core Hours (as of spring 2021)
Application: GCS Fast Track
Abstract
Coveney and his collaborators model the binding affinities of drug compounds and pathogens. A drug’s binding affinity essentially means the strength of the interaction between, for instance, a protein in the life cyle of a virus and active compounds in a medication—the stronger the binding affinity, the more effective the drug. Supercomputers allow researchers to run large numbers of binding affinity simulations in parallel. Here, they compare information about the structure of the virus with a database containing information about known drug compounds to identify those with a high likelihood of binding. This computational approach enables researchers to investigate large numbers of potential drugs much more quickly than would be possible if they had to mix individual drug samples with actual viruses in a lab.
Coveney has been performing his work as part of the Consortium on Coronavirus, an international effort involving researchers and resources from 9 universities, 5 United States Department of Energy national laboratories, and some of the world’s fastest supercomputers, including SuperMUC-NG (currently number 9 in the Top500 list) and Summit at Oak Ridge National Laboratory in the United States (currently the world’s fastest machine for open science). According to Coveney this consortium is a vast effort, involving many people, supercomputers, synchrotron sources for experimental structural biology and protein structure determination, wet labs for assays, and synthetic chemists who can make new compounds.
Further Information: https://www.compbiomed.eu/compbiomed-and-coronavirus/
Title: Antibody epitopes and inhibitor binding sites from structural dynamics of SARS-CoV-2 spike protein clusters
Project area: Antibody design
Principal Investigator: Prof. Dr. Gerhard Hummer, Max-Planck-Institute for Biophysics
Amount: 20 Mio Core Hours
Application: GCS Fast Track
Abstract:
The exposure on the viral surface and the critical role in infection make the spike protein a highly attractive target both for antibodies and for viral-fusion inhibitors. Hence, the team aims at identifying druggable sites in the SARS-CoV-2 S glycoprotein and conserved epitopes as targets of neutralizing antibodies. The ultimate goal is to aid the development of antiviral drugs and vaccines against COVID-19. To reach their goal, the team will perform molecular dynamics simulation of a patch of SARS-CoV-2 native-like viral membrane with spike (S) glycoproteins embedded.
Title: Polypharmacology-based antiviral design
Project area: Biochemistry
Principle Investigator: Daniel Soler Viladrich, University of Barcelona
System: SuperMUC-NG
Amount: 4 Mio Core Hours
Application: PRACE Fast Track
Abstract
The team is looking for substances that prevent the virus from attacking cells. Viladrich and his team combine laboratory experiments with supercomputing and have developed a scoring system based on a wide range of criteria. SuperMUC-NG uses this scoring system to filter out substances that potentially bind to and block the proteins of various corona strains. The effectiveness of these compounds is then tested experimentally.
Title: High-throughput analysis of proteomic data for innate immunity
Project area: Virology
Principle Investigator: Prof. Dr. Andreas Pichlmair & Dr. Alexey Stukalov, TUM School of Medicine at the Technical University of Munich
System: CoolMUC-2
Application: LRZ Linux-cluster project
Abstract
The team of TUM takes the most experimental approach and aims to find out how the entirety of both the proteins (proteome) and the molecular interactions (interactome) in the cell are altered (up- or downregulation) upon an infection with the coronavirus. The analyses of their findings are supposed to help identify potential drug candidates for the treatment of Covid-19, which in turn shall then be examined in high-safety laboratories. The idea of this approach is to detect an inhibitory effect on the coronavirus, so that it is no longer able to infect human cells – or only to a very limited extent.
Further Information: https://innatelab.virologie.med.tum.de
Publication
By spring 2021, the team has published their results in Nature magazine: https://www.nature.com/articles/s41586-021-03493-4
For an overview on all GCS projects, please see here: https://www.gauss-centre.eu/news/research-highlights/article/gcs-centres-support-research-to-mitigate-impact-of-covid-19-pandemic/
More information on PRACE supported projects is available here: https://prace-ri.eu/prace-awards-195-million-core-hours-to-trap-and-defeat-covid-19-and-more-to-come/