SARS-CoV-2: Fluid Response
At Fluid Numerics we value community and view assistance that benefits the health and wellbeing of cohabitants on our planet as an opportunity to grow holistically. Instead of retreating from the compounding issues that are directly affecting our world we are leaning in to understand how we can support and are proud to be able to assist in accelerating time to analysis, research and synthesis of current coronavirus study as it relates to computational science.
Fluid Numerics has been affiliate member of the Covid-19 HPC Consortium since early 2020. In late 2020 we started assisting Dr. Suchetana Gupta with her compute resources at the Indian Institute for the Cultivation of Science resulting in the submission of findings in "D155Y Substitution of SARS-CoV-2 ORF3a Weakens Binding with Caveolin-1: An in silico Study" available for your review here.
In early 2020 we assisted a Googler integrating a part of their workflow to accelerate drug discovery on Google Cloud using a software named VirtualFlow.
Later in 2020, Google published research around VirtualFlow and how it is being utilized to aid in Covid-19 research.
Our team realized a need for teams to rapidly scale access to research computing resources and released multi-project and multi-region functionality mid-year 2020. We also started exposing tools for research to the public in 2020 and continue to contribute to and maintain these resources. The applications included in a functional research environment now called the Research Computing Cloud includes:
Fluid Numerics assists teams as they apply publicly available research data like the National Institute of Health dataset readily available through a SARS-CoV2 datahub and other datasets for Covid-19 research in order to complete their research and science. Our products and services are beneficial to teams and we realized this early on in the pandemic. Fluid Numerics decided to begin organizing and structuring in order to contribute a large percentage of it's capacity to community and socially benefiting projects.
As we continued into 2020, we submitted our Letter of Intent as a service provider to The Extreme Science and Engineering Discovery Environment (XSEDE) and in August of 2021, we received our acceptance into the environment as a provider. As a contributor of Cyber Infrastructure we are able to assist academics and organizations with their projects and enable them further.
Our team is available to help organizations and their team members use GCP to accelerate coronavirus research or share computer assisted efforts where it may be relevant to understanding this virus and it's potential impacts.
Let us know how we can help. Contact Us.
What we are doing:
In 2020 we built and deployed a partition for Fluid-Slurm-GCP that can host Folding@home Work Units for teams looking to provide more compute resources to F@H.
Repository publicly available here: https://bitbucket.org/fluidnumerics/folding-at-home-slurm_gcp/src/master/
Throughout 2021 we developed and deployed the Research Computing Cloud(RCC) and it's resources like rapidly deployable and scalable bare-metal style Research Computing Clusters which can expand and contract with the necessity of research and science to decrease time to science and resource needs for workloads that are critical to the health and safety of our community.
D155Y Substitution of SARS-CoV-2 ORF3a Weakens Binding with Caveolin-1: An in silico Study - Suchetana Gupta, Ditipriya Mallick, Kumarjeet Banerjee, Soumyadev Sarkar, Sonny T M Lee, Partha Basuchowdhuri, Siddhartha Sankar Jana - bioRxiv 2021.03.26.437194; doi: https://doi.org/10.1101/2021.03.26.437194
Gorgulla, C., Boeszoermenyi, A., Wang, Z. et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature (2020). https://doi.org/10.1038/s41586-020-2117-z
Christoper Wertz - Part V: Accelerating Drug Discovery with VirtualFlow on Google Cloud. Medium (8/26/2020). https://medium.com/google-ai-platform-for-predicting-peptide-for/part-v-accelerating-drug-discovery-with-virtualflow-on-google-cloud-87cd13fcbcba
D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, R.E. Duke, G. Giambasu, M.K. Gilson, H. Gohlke, A.W. Goetz, R. Harris, S. Izadi, S.A. Izmailov, C. Jin, K. Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O’Hearn, A. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C.L. Simmerling, N.R. Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, H. Wei, R.M. Wolf, X. Wu, Y. Xue, D.M. York, S. Zhao, and P.A. Kollman (2021), Amber 2021, University of California, San Francisco. The Molecular Dynamics Package. Amber20. https://ambermd.org/CiteAmber.php
Ahrens, James, Geveci, Berk, Law, Charles, ParaView: An End-User Tool for Large Data Visualization, Visualization Handbook, Elsevier, 2005, ISBN-13: 978-0123875822
Ayachit, Utkarsh, The ParaView Guide: A Parallel Visualization Application, Kitware, 2015, ISBN 978-1930934306
Berendsen, et al. (1995) Comp. Phys. Comm. 91: 43-56.
Lindahl, et al. (2001) J. Mol. Model. 7: 306-317.
van der Spoel, et al. (2005) J. Comput. Chem. 26: 1701-1718.
Hess, et al. (2008) J. Chem. Theory Comput. 4: 435-447.
Pronk, et al. (2013) Bioinformatics 29 845-854.
Páll, et al. (2015) Proc. of EASC 2015 LNCS, 8759 3-27.
Abraham, et al. (2015) SoftwareX 1-2 19-25.
The Lustre® file system is an open source parallel file system licensed under the GPL 2.0 license for use with Linux and is open to community involvement and code development. https://wiki.lustre.org/Development
Additional Resources we have referenced within our research:
China National Center for Bioinformation: 2019 Novel Coronavirus Resource (2019nCoVR) - https://bigd.big.ac.cn/ncov/?lang=en
SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) Sequences - https://www.ncbi.nlm.nih.gov/genbank/sars-cov-2-seqs/
Get Rapid Access to Novel Coronavirus (2019-nCoV) Sequence Data from NLM’s GenBank® - https://www.nlm.nih.gov/news/coronavirus_genbank.html
COVID-19 Open Research Dataset (CORD-19) on semanticscholar.org - https://pages.semanticscholar.org/coronavirus-research
COVID-19 Open Research Dataset Challenge (CORD-19) - https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
VirusSeeker, a computational pipeline for virus discovery and virome composition analysis. - https://www.ncbi.nlm.nih.gov/pubmed/28110145
VirusSeeker-Virome - https://github.com/guoyanzhao/VirusSeeker-Virome
Vipie: web pipeline for parallel characterization of viral populations from multiple NGS samples - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430618/
Department of Pathology & Immunology : Washington University School of Medicine - https://wupathlabs.wustl.edu/virusseeker/
Folding @ Home : COVID-19 Update - https://foldingathome.org/2020/03/10/covid19-update/
XSEDE COVID-19 HPC Consortium Request Information - https://www.xsede.org/c/journal/view_article_content?cmd=view&groupId=1477968&articleId=2421324&version=6.0