RESEARCH: COVID-19
FOLDING PROJECT #14903 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 169,725Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how the SARS-CoV-2 virus attaches to human cells. Researchers at the University of Illinois are creating a computer simulation of the virus's spike protein binding to human ACE2 receptors. This will help us understand how the virus works and potentially develop new treatments.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
COVID-19 PROJECT This system is the solvated human ACE2 (Angiotensin-converting enzyme 2) and RBD (receptor-binding domain) complex involved in SARS-CoV-2 transfection.
This simulation will help us to understand the major interactions responsible for binding of the RBD domain of S1 protein to human ACE2. This project is managed by Matthew Chan at the Univerisity of Illinois at Urbana-Champaign. Matthew is a Ph.D.
Candidate in Prof.
Diwakar Shukla's group.
The long-term research goal of Shukla group is to combine theory, computation, and experiments to develop quantitative models of biological phenomena relevant for health, energy and climate change.
Under the broad umbrella of molecular engineering and sciences, our research program is focused on developing a platform for understanding regulation of protein function such as elucidating mechanistic insights to regulate plant growth and development in context of global climate change.
We integrate ideas from a wide range of disciplines tied together by a vision of “dynamic” biology and its role in engineering products for human health, energy, and climate change.
RELATED TERMS GLOSSARY AI BETA
COVID-19
A contagious respiratory illness caused by the SARS-CoV-2 virus.
COVID-19 is a highly contagious respiratory illness caused by the SARS-CoV-2 virus. It can cause a wide range of symptoms, from mild to severe, including fever, cough, shortness of breath, and loss of taste or smell. In some cases, it can lead to pneumonia, acute respiratory distress syndrome (ARDS), and death.
ACE2
Angiotensin-converting enzyme 2
ACE2 is a protein found on the surface of many cells in the body, including those in the lungs and heart. It plays a role in regulating blood pressure and other bodily functions. The SARS-CoV-2 virus uses ACE2 as a receptor to enter and infect cells.
RBD
Receptor-binding domain
The RBD is a specific region on the spike protein of the SARS-CoV-2 virus that binds to the ACE2 receptor on human cells. This binding allows the virus to enter and infect cells.
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
SARS-CoV-2 is the virus that causes COVID-19. It is a type of coronavirus that first emerged in Wuhan, China, in late 2019.
Transfection
The process of introducing foreign DNA into a cell.
Transfection is a laboratory technique used to introduce genetic material (DNA or RNA) into cells. This can be used for various research purposes, such as studying gene function or developing new therapies.
Protein
A large biomolecule composed of chains of amino acids.
Proteins are essential molecules that perform a wide range of functions in living organisms. They are involved in processes such as catalyzing reactions, transporting molecules, providing structural support, and regulating cellular activities.
Simulation
A computer-based model of a real-world system.
Simulations are used to study complex systems and predict their behavior. In the context of biological research, simulations can be used to model molecular interactions, cellular processes, or entire organisms.
Ph.D.Candidate
A person who is working towards a doctoral degree.
A Ph.D. Candidate is a graduate student pursuing a doctorate-level degree. They conduct in-depth research, write a dissertation, and defend their work before a committee of experts.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:32:56|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,087,248 | 375,032 | 18.90 | 1 hrs 16 mins |
| 2 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,496,012 | 282,988 | 12.35 | 1 hrs 57 mins |
| 3 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,173,040 | 287,171 | 11.05 | 2 hrs 10 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,001,741 | 282,424 | 10.63 | 2 hrs 15 mins |
| 5 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 2,954,438 | 265,238 | 11.14 | 2 hrs 9 mins |
| 6 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,757,974 | 274,520 | 10.05 | 2 hrs 23 mins |
| 7 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,603,683 | 269,932 | 9.65 | 2 hrs 29 mins |
| 8 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,558,693 | 265,269 | 9.65 | 2 hrs 29 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,418,676 | 262,237 | 9.22 | 2 hrs 36 mins |
| 10 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,876,128 | 230,453 | 8.14 | 2 hrs 57 mins |
| 11 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 1,719,592 | 234,906 | 7.32 | 3 hrs 17 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,600,121 | 228,534 | 7.00 | 3 hrs 26 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,590,482 | 227,177 | 7.00 | 3 hrs 26 mins |
| 14 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,493,158 | 223,920 | 6.67 | 3 hrs 36 mins |
| 15 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,279,324 | 199,573 | 6.41 | 3 hrs 45 mins |
| 16 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,197,299 | 197,102 | 6.07 | 3 hrs 57 mins |
| 17 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,045,975 | 198,633 | 5.27 | 4 hrs 33 mins |
| 18 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,021,915 | 196,696 | 5.20 | 4 hrs 37 mins |
| 19 | Radeon RX 5600/5600 XT - 5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT / 5700/5700 XT] |
AMD | Navi 10 | 1,015,596 | 196,085 | 5.18 | 4 hrs 38 mins |
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|||||||
| 20 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 979,483 | 193,860 | 5.05 | 4 hrs 45 mins |
| 21 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 955,540 | 191,411 | 4.99 | 4 hrs 48 mins |
| 22 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 841,299 | 172,713 | 4.87 | 4 hrs 56 mins |
| 23 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 801,118 | 172,680 | 4.64 | 5 hrs 10 mins |
| 24 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 753,311 | 178,497 | 4.22 | 5 hrs 41 mins |
| 25 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 710,454 | 174,856 | 4.06 | 5 hrs 54 mins |
| 26 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 645,405 | 168,690 | 3.83 | 6 hrs 16 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] |
Nvidia | GP104 | 639,446 | 168,014 | 3.81 | 6 hrs 18 mins |
| 28 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 542,828 | 160,012 | 3.39 | 7 hrs 4 mins |
| 29 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 476,401 | 150,947 | 3.16 | 7 hrs 36 mins |
| 30 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 405,426 | 145,183 | 2.79 | 8 hrs 36 mins |
| 31 | Radeon R9 280/HD 7900/8950 Tahiti PRO [Radeon R9 280/HD 7900/8950] |
AMD | Tahiti PRO | 300,861 | 131,221 | 2.29 | 10 hrs 28 mins |
| 32 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 207,000 | 115,961 | 1.79 | 13 hrs 27 mins |
| 33 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 124,875 | 92,881 | 1.34 | 17 hrs 51 mins |
| 34 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 114,777 | 95,055 | 1.21 | 19 hrs 53 mins |
| 35 | Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series] |
AMD | Hawaii | 107,894 | 93,645 | 1.15 | 20 hrs 50 mins |
| 36 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 97,081 | 83,103 | 1.17 | 20 hrs 33 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:32:56|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|