RESEARCH: COVID-19
FOLDING PROJECT #17332 PROFILE
PROJECT TEAM
Manager(s): Ivy ZhangInstitution: Memorial Sloan Kettering Cancer Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 257,388Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to studying how the SARS-CoV-2 virus attaches to human cells. Scientists are using computer simulations to understand how proteins on the virus and in our bodies interact. This research could help design new drugs to fight COVID-19.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
These projects involve the SARS-CoV-2 receptor binding domain (RBD) and its target receptor in humans, ACE2.
We are simulating these proteins alone and in complex with each other (and with and without glycosylation).
We will build Markov State Models using the Fah simulation data, which will help us identify the metastable states of each protein/protein complex.
Given these experiments, we hope to be able to explain the impact of glycosylation on RBD conformational dynamics as well as identify whether there are shifts in metastable states upon RBD:ACE2 binding.
Ultimately, the knowledge gained here will help infom drug design efforts. Note: 17313-6 replace 17307-9, as 17307-9 used a different, less stable integrator. 17329-331 replace 17316-8 (change from rectangular to truncated octahedron box) 17332-5 replace 17325-8 ((change from rectangular to truncated octahedron box).
RELATED TERMS GLOSSARY AI BETA
SARS-CoV-2
Severe Acute Respiratory Syndrome Coronavirus 2
SARS-CoV-2 is the virus responsible for causing COVID-19. It's a type of coronavirus that spreads through respiratory droplets and can lead to severe respiratory illness.
Receptor Binding Domain (RBD)
Receptor Binding Domain
The RBD is a part of the SARS-CoV-2 spike protein responsible for attaching to and entering human cells. It binds to the ACE2 receptor.
ACE2
Angiotensin-Converting Enzyme 2
ACE2 is a protein found on the surface of human cells. The SARS-CoV-2 RBD binds to ACE2, allowing the virus to enter and infect cells.
Markov State Models
Mathematical models used to simulate the dynamics of complex systems.
Markov State Models are used to predict the behavior of molecules over time. In this case, they will help understand how proteins interact and change shape.
Glycosylation
The addition of sugar molecules to proteins.
Glycosylation is a common modification of proteins that can affect their structure and function. This research will investigate how glycosylation impacts the interaction between SARS-CoV-2 proteins.
Metastable States
Stable but transient states of a system.
Metastable states are temporary configurations that molecules can exist in. Understanding these states can reveal how proteins change shape and function.
Drug Design
The process of identifying and developing new medications.
Drug design aims to create drugs that target specific molecules or pathways involved in diseases. The insights gained from this research can inform the development of antiviral therapies against SARS-CoV-2.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:39:50|
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 | 5,902,681 | 243,864 | 24.20 | 0 hrs 59 mins |
| 2 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 5,389,188 | 240,217 | 22.43 | 1 hrs 4 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,153,868 | 219,531 | 18.92 | 1 hrs 16 mins |
| 4 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 4,078,386 | 219,844 | 18.55 | 1 hrs 18 mins |
| 5 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 3,756,279 | 213,289 | 17.61 | 1 hrs 22 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,872,931 | 194,595 | 14.76 | 1 hrs 38 mins |
| 7 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,650,171 | 189,718 | 13.97 | 1 hrs 43 mins |
| 8 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,609,824 | 188,779 | 13.82 | 1 hrs 44 mins |
| 9 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,524,538 | 186,765 | 13.52 | 1 hrs 47 mins |
| 10 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,443,956 | 184,093 | 13.28 | 1 hrs 48 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,405,247 | 183,498 | 13.11 | 1 hrs 50 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,111,199 | 173,463 | 12.17 | 1 hrs 58 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,030,045 | 172,380 | 11.78 | 2 hrs 2 mins |
| 14 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,913,612 | 170,488 | 11.22 | 2 hrs 8 mins |
| 15 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,733,692 | 165,195 | 10.49 | 2 hrs 17 mins |
| 16 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,695,727 | 160,076 | 10.59 | 2 hrs 16 mins |
| 17 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,550,447 | 158,662 | 9.77 | 2 hrs 27 mins |
| 18 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,538,851 | 156,540 | 9.83 | 2 hrs 26 mins |
| 19 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,254,585 | 147,303 | 8.52 | 2 hrs 49 mins |
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|
|||||||
| 20 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,242,898 | 147,598 | 8.42 | 2 hrs 51 mins |
| 21 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,147,185 | 141,189 | 8.13 | 2 hrs 57 mins |
| 22 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,102,487 | 140,838 | 7.83 | 3 hrs 4 mins |
| 23 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,086,969 | 141,047 | 7.71 | 3 hrs 7 mins |
| 24 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,078,962 | 140,911 | 7.66 | 3 hrs 8 mins |
| 25 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] 4550 |
Nvidia | TU106M | 1,068,430 | 140,555 | 7.60 | 3 hrs 9 mins |
| 26 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 902,004 | 132,649 | 6.80 | 3 hrs 32 mins |
| 27 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 878,496 | 108,912 | 8.07 | 2 hrs 59 mins |
| 28 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 877,958 | 131,559 | 6.67 | 3 hrs 36 mins |
| 29 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 666,914 | 116,514 | 5.72 | 4 hrs 12 mins |
| 30 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 558,946 | 113,240 | 4.94 | 4 hrs 52 mins |
| 31 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 520,651 | 109,674 | 4.75 | 5 hrs 3 mins |
| 32 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 425,089 | 103,337 | 4.11 | 5 hrs 50 mins |
| 33 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 423,797 | 103,425 | 4.10 | 5 hrs 51 mins |
| 34 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 287,513 | 90,861 | 3.16 | 7 hrs 35 mins |
| 35 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 269,788 | 88,680 | 3.04 | 7 hrs 53 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:39:50|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|