Lend your CPU and GPU to Crowdsource Computer Networks for the fight against COVID-19

Around the world countries are taking drastic measures to protect their citizens against a rare form of flu pandemic known as COVID-19 or (Corona Virus Disease 2019) [1]. While governments, researchers and health professionals working tirelessly to contain the pandemic and find cure against this rapidly spreading disease, you too can help by allocating your computer resources to towards advances in research about this rapidly spreading disease from the comfort of your home.

Simply start “Folding” by downloading an application from Folding@home (FAH or F@h) at https://foldingathome.org/start-folding/. It will allow you to lend the power of your computer (GPU and CPU) toward advancing the research of coronavirus. Majority of today’s laptops and desktops have built-in GPU and multi-core CPU: just let the application tap into your unused clock cycles. Just connect to internet, download the application, install the FAH application and stay connected (figure 1).

Folding@Home or FAH has been around since October, 2000. It was developed by the Pande Laboratory at Stanford University, under the direction of Prof. Vijay Pande, who lead the project until 2019. Since 2019, Folding@home has been led by Dr. Greg Bowman, a former student of Dr. Pande [2]. The FAH is a distributed computing project [3] for research that simulates protein folding [4], computational drug design, and other types of molecular dynamics [5]. Till date, FAH helped Pande Lab to produce 223 scientific research papers [6]. While FAH is well known distributed computing project, there are other initiatives that uses idle computer power to carry out various research in the field of astronomy, chemistry, biology, climatology, mathematics, and physics, e.g. Berkeley Open Infrastructure for Network Computing (BOINC) [7]. Unlike FAH that focuses purpose-built for protein folding, BOINC supports 30 other science projects such as Einstein@Home, IBM World Community Grid, and SETI@home. Now that you know, you can lend your computing power to do some great goods for some of the world’s pressing problems either through FAH for COVID-19 research or through BIONC supported projects.

The distributed computing is a concept of using multiple computers to solve a common problem by computation distributed among connected computers unlike parallel computing systems that uses common memory pool.

There are different messaging mechanisms for distributed computing which may includes http, RPC and message queues. Implementation architecture also varies and falls into one of the following architectures:

· Client-server: It is the simplest form of architecture in which client request and server executing or some way of fulfilling the request. FAH implementation is a good example of client server architecture that uses RPC connectors for communications. A client server architecture could be two tier or three-tier depending upon the implementation.

· N-tier: This architecture also known as multi-tier mainly comprises of applications servers for which processing is performed through different computing systems as depicted in figure 2 (above).

· Peer-to-Peer (P2P): The P2P is a form of client-server distributed system is which every node can be either server or client depending upon the task. Filesharing and blockchain implementations are good examples of P2P deployments. SETI@home project uses P2P communication and computation.

· Cluster Computing: It is a “High Performance Distributed Computing” (HPDC) model in which distributed computing techniques are applied to the solution of computationally intensive applications across networks of computers. Cluster computing can be either distributed or parallel in implementation.

Simply put FAH is assisting researchers through simulating of druggable design of protein target to combat COVID-19. As of today, FAH has released initial wave of projects simulating potentially druggable protein targets from SARS-CoV-2 (the virus that causes COVID-19) and the related SARS-CoV virus [8]. These projects help scientist better understand how coronaviruses interact with human ACE2 receptor (also known as angiotensin converting enzyme 2). According to studies of patients with severe acute respiratory syndrome (SARS) demonstrated that the respiratory tract is a major site of SARS-coronavirus (CoV) infection and disease morbidity for which ACE2 is the viral entry point to human cell [8; 9]. Similar observations are also made for the virus infections of COVID-19. Hence, work at FAH is of great importance because FAH initiative helps researchers better understand how COVID19 interact with ACE2 and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

With collaboration and partnership of various labs, researchers and some of the new structural biology and biochemical data available at bioRxiv and chemRxiv, Folding@home hopes to help researchers better understand COVID-19 and how it interact with ACE2 receptor in a process to fight the virus. For further details, please read https://foldingathome.org/2020/03/10/covid19-update/ .

As discussed, COVID-19 has much similarities with SARS coronavirus from 2003. The infection resulting from both viruses occurs at lung when a virus protein (spike protein) binds with a lung cell receptor protein (ACE2) [8;9]. An antibody can prevent the spread of the infection by blocking the spike protein from binding with the receptor, in other words, blocking the COVID-19 virus to bind ACE2. To develop such antibody, researchers need to study the structure of COVID-19 spike protein, various shapes it takes and how it binds to ACE2 receptor. This requires lots of protein folding simulations unique to COVID-19 and that where FAH comes in. Protein folding is constantly ongoing and a sorts of biological opera with a huge cast of performers, an intricate plot, and dramatic denouements when things go awry [10]. Understanding these intricacies help scientist create druggable protein design to fight disease among other things. Accurate simulation of protein folding is thus holy grail of computational biology. In a human body, protein takes milliseconds to seconds to fold completely. For a computer to simulate this protein folding with all changes at atomic level, it has to perform it has to perform trillions of quadrillions of steps. If we were to simulate this using single high end personal computer, it will take centuries to render. Please view the following video on millisecond protein folding to better understand how things work.

To render similar tasks of a millisecond-level simulation of what you are viewing in this video above, may take took months of CPU time for a typical “Supercomputer”. That’s where distributed computing model such as Folding@home (FAH) is useful. The FAH computing model behaves as “Supercomputer” by using power of computers from volunteers around the world and performance is also great with more than 100 Peta FLOPS (floating point operation per second) in comparison to IBM supercomputer with 150 PetaFLOPS: FLOPS is a unit to measure the performance of “Supercomputer”. Additionally, FAH deployment is much cheaper than using IBM’s OLCF-4 type supercomputer. More importantly, according to the researchers in the Pande Lab, “Protein folding dynamics is statistical in nature, so a single long simulation from a supercomputer would not be sufficient to fully understand the folding process” [11].

1. CDC, 2020. Key facts: Know the facts about coronavirus disease 2019 (COVID-19) and help stop the spread of rumors. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases. Available online at https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/share-facts.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-ncov%2Fabout%2Fshare-facts.html

2. Wikipedia, 2020. Folding@home. Wikipedia. Available at https://en.wikipedia.org/wiki/Folding@home.

3. ScienceDirect, 2020. Distributed Computing. Science Direct. Available at https://www.sciencedirect.com/topics/computer-science/distributed-computing

4. Wikipedia, 2020. Protein Folding. Wikipedia. https://en.wikipedia.org/wiki/Protein_folding .

5. Meller, J. 2001. Molecular Dynamics. Encyclopedia of Life Sciences: Nature Publishing Group. Available https://dasher.wustl.edu/chem430/readings/md-intro-1.pdf .

6. FAH, 2020. Papers and Results. Folding@Home. Available online at https://foldingathome.org/papers-results/

7. BOINC, 2020. Compute for Science. BOINC. Available at https://boinc.berkeley.edu/ .

8. FAH, 2020. FOLDING@HOME UPDATE ON SARS-COV-2 (10 MAR 2020). Folding@Home. Available https://foldingathome.org/2020/03/10/covid19-update/ .

9. Jia et al, 2005. Jia, P.H., Look, C.D., Shi, L., Hickey, M., Pewe, L., Netland, J., Farzan, M., Wohlford-Lenane, C., Perlman, S. & McCray, B. P., 2005. ACE2 Receptor Expression and Severe Acute Respiratory Syndrome Coronavirus Infection Depend on Differentiation of Human Airway Epithelia. Journal of Virology, American Society for Microbiology (ASM).

10. Everts, S., 2017. Protein folding: Much more intricate than we thought. C & EN. Available at https://cen.acs.org/articles/95/i31/Protein-folding-Much-intricate-thought.html .

11. Mathi, S. 2020. You Can Help Fight Coronavirus by Giving Scientists Access to Your Computer. Available at https://onezero.medium.com/you-can-help-fight-coronavirus-by-giving-scientists-access-to-your-computer-16c39c2e7164 .