RESEARCH: CANCER
FOLDING PROJECT #18715 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 114,420Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how a protein called TNF-alpha works in both fighting and promoting cancer. Scientists are using computer simulations to study how TNF-alpha changes shape when it binds to other proteins, and how this affects its ability to stimulate or inhibit tumor growth. This research could lead to new treatments for cancer.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Tumor necrosis factor α (TNFα) is a cytokine that belongs to a superfamily of trimeric proteins.
This protein has been shown to be important in regulating autoimmune diseases such as arthritis and Crohn’s disease through interactions with the TNF receptor.
In regard to cancer, TNF is a double-dealer.
On one hand, TNF could be an endogenous tumor promoter, because TNF stimulates cancer cells’ growth, proliferation, invasion and metastasis, and tumor angiogenesis.
On the other hand, TNF could be a cancer killer.
In it’s apo state TNFα has shown to be symmetrical, but small ligand inhibitors bind the TNFα disrupt this symmetry by forcing one of the monomers to be below the other two, which disrupts the binding interface to form the TNFα-receptor complex.
In this project, we want to determine the stability of the trimer and get a sense of the free energy landscape.
We also want to determine if the asymmetry found in the inhibited conformation requires the presence of an inhibitor or if the apo trimer can visit inhibited states in the absence of the ligand.
In particular we are interested in learning about the effect the volume of the binding pocket has on forming the asymmetrical TNFα complex.
The initial starting structures are 50 diverse seeds from HREMD simulations started from a crystal structure.
This is a project run by Roivant Sciences (formerly Silicon Therapeutics) as was officially announced in this press release: https://foldingathome.org/2021/04/20/maximizing-the-impact-of-foldinghome-by-engaging-industry-collaborators/ All data is being made publicly available as soon as it is received at https://console.cloud.google.com/storage/browser/stxfah-bucket.
RELATED TERMS GLOSSARY AI BETA
Tumor necrosis factor
A cytokine that plays a role in inflammation and immune responses.
Tumor necrosis factor (TNF) is a protein involved in regulating the body's immune system. It can trigger inflammation and help fight infections but also contribute to autoimmune diseases like arthritis.
Cytokine
A type of signaling molecule produced by cells of the immune system.
Cytokines are small proteins that act as messengers between cells of the immune system. They help coordinate the body's response to infections and other threats.
Autoimmune diseases
Conditions in which the immune system attacks healthy tissues.
Autoimmune diseases occur when the body's immune system mistakenly identifies its own cells and tissues as foreign invaders. This leads to an attack on healthy cells and organs.
Arthritis
A group of conditions causing inflammation and pain in the joints.
Arthritis is a common condition that affects the joints, causing pain, stiffness, and swelling. There are many types of arthritis, each with different causes and symptoms.
Crohn's disease
An inflammatory bowel disease that affects the digestive tract.
Crohn's disease is a chronic condition that causes inflammation of the digestive tract. It can affect any part of the gut, from the mouth to the anus.
Cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a group of diseases where abnormal cells grow and divide uncontrollably. This can lead to the formation of tumors and the spread of cancer to other parts of the body.
Tumor angiogenesis
The formation of new blood vessels that supply a tumor.
Tumor angiogenesis is the process by which tumors develop their own blood supply. This allows tumors to grow and spread.
TNF receptor
A protein that binds to TNF and mediates its effects.
TNF receptors are proteins on the surface of cells that bind to TNF. This binding triggers a variety of cellular responses.
HREMD
High-resolution Enhanced Molecular Dynamics simulations
HREMD is a type of computer simulation used to study the movement and interactions of molecules. It provides detailed information about the structure and dynamics of biomolecules.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:27:31|
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 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 16,094,258 | 789,015 | 20.40 | 1 hrs 11 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 10,972,263 | 855,987 | 12.82 | 1 hrs 52 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 10,595,533 | 857,784 | 12.35 | 1 hrs 57 mins |
| 4 | GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 6,113,530 | 690,374 | 8.86 | 2 hrs 43 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,700,979 | 698,438 | 8.16 | 2 hrs 56 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,530,584 | 691,098 | 8.00 | 2 hrs 60 mins |
| 7 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,436,569 | 680,575 | 7.99 | 3 hrs 0 mins |
| 8 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 5,184,316 | 673,596 | 7.70 | 3 hrs 7 mins |
| 9 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,484,123 | 644,841 | 6.95 | 3 hrs 27 mins |
| 10 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,483,479 | 648,637 | 6.91 | 3 hrs 28 mins |
| 11 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,919,555 | 615,773 | 6.37 | 3 hrs 46 mins |
| 12 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,910,125 | 613,738 | 6.37 | 3 hrs 46 mins |
| 13 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,576,084 | 604,679 | 5.91 | 4 hrs 3 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,879,720 | 557,063 | 5.17 | 4 hrs 39 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,302,752 | 500,817 | 4.60 | 5 hrs 13 mins |
| 16 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,189,783 | 524,033 | 4.18 | 5 hrs 45 mins |
| 17 | Geforce RTX 3070 Ti Laptop GPU GA104 [Geforce RTX 3070 Ti Laptop GPU] |
Nvidia | GA104 | 2,056,203 | 505,225 | 4.07 | 5 hrs 54 mins |
| 18 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,025,565 | 498,533 | 4.06 | 5 hrs 54 mins |
| 19 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,942,042 | 489,188 | 3.97 | 6 hrs 3 mins |
|
|
|||||||
| 20 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,906,997 | 485,242 | 3.93 | 6 hrs 6 mins |
| 21 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,888,065 | 482,560 | 3.91 | 6 hrs 8 mins |
| 22 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,583,918 | 463,075 | 3.42 | 7 hrs 1 mins |
| 23 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,560,177 | 453,565 | 3.44 | 6 hrs 59 mins |
| 24 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,491,148 | 447,268 | 3.33 | 7 hrs 12 mins |
| 25 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,083,544 | 404,074 | 2.68 | 8 hrs 57 mins |
| 26 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,078,394 | 415,711 | 2.59 | 9 hrs 15 mins |
| 27 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,050,942 | 391,508 | 2.68 | 8 hrs 56 mins |
| 28 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 992,284 | 398,834 | 2.49 | 9 hrs 39 mins |
| 29 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 991,229 | 389,254 | 2.55 | 9 hrs 25 mins |
| 30 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 918,770 | 379,012 | 2.42 | 9 hrs 54 mins |
| 31 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 784,036 | 395,089 | 1.98 | 12 hrs 6 mins |
| 32 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 523,560 | 316,020 | 1.66 | 14 hrs 29 mins |
| 33 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 519,652 | 343,962 | 1.51 | 15 hrs 53 mins |
| 34 | GeForce GTX 1060 Mobile GP106M [GeForce GTX 1060 Mobile] |
Nvidia | GP106M | 448,706 | 299,199 | 1.50 | 16 hrs 0 mins |
| 35 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 434,437 | 318,988 | 1.36 | 17 hrs 37 mins |
| 36 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 426,595 | 296,584 | 1.44 | 16 hrs 41 mins |
| 37 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 253,303 | 250,054 | 1.01 | 23 hrs 42 mins |
| 38 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 222,596 | 236,499 | 0.94 | 25 hrs 30 mins |
| 39 | RX Vega M GL Polaris 22 XL [RX Vega M GL] |
AMD | Polaris 22 XL | 183,734 | 222,289 | 0.83 | 29 hrs 2 mins |
|
|
|||||||
| 40 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 180,395 | 264,186 | 0.68 | 35 hrs 9 mins |
| 41 | R7 370/R9 270X/370X Curacao XT/Trinidad XT [R7 370/R9 270X/370X] |
AMD | Curacao XT/Trinidad XT | 120,783 | 200,000 | 0.60 | 39 hrs 44 mins |
| 42 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 105,858 | 200,000 | 0.53 | 45 hrs 21 mins |
| 43 | Radeon 540/540X/550/550X/RX 540X/550/550X Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X] |
AMD | Lexa PRO | 81,867 | 200,000 | 0.41 | 58 hrs 38 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:27:31|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|