RESEARCH: CANCER
FOLDING PROJECT #18716 PROFILE
PROJECT TEAM
Manager(s): Rafal WiewioraInstitution: Roivant Sciences (Silicon Therapeutics)
Project URL: View Project Website
WORK UNIT INFO
Atoms: 81,870Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how a protein called TNF-alpha works. TNF-alpha affects the immune system and can both help and hurt cancer cells. Scientists want to understand how TNF-alpha changes shape when it binds to other proteins and if these shapes affect its ability to fight or promote cancer. They're using computer simulations to figure this out.
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 regulates inflammation.
Tumor necrosis factor (TNF) is a protein involved in the body's immune response. It plays a crucial role in fighting infections and regulating inflammation. However, too much TNF can contribute to autoimmune diseases like arthritis and Crohn's disease.
Cytokine
A type of protein that helps regulate immune responses.
Cytokines are small proteins that act as messengers between cells in the immune system. They play a vital role in coordinating immune responses to infections, injuries, and other threats.
Autoimmune disease
A condition where the immune system attacks the body's own tissues.
Autoimmune diseases occur when the body's immune system mistakenly attacks its own cells and tissues. This can lead to inflammation and damage in various organs.
TNF receptor
A protein that binds to TNF and triggers a signaling cascade.
TNF receptors are proteins found on the surface of cells. When TNF binds to these receptors, it sets off a series of signals within the cell that can influence various cellular processes.
Cancer
Uncontrolled growth of abnormal cells.
Cancer is a disease characterized by the uncontrolled growth and spread of abnormal cells. These cells can invade surrounding tissues and organs, disrupting normal body functions.
Angiogenesis
The formation of new blood vessels.
Angiogenesis is the process by which new blood vessels are formed. It is essential for tissue growth and repair but can also contribute to the development of tumors.
Apo state
The inactive or unbound form of a molecule.
Apo state refers to the inactive or unbound form of a biological molecule, such as a protein. In contrast, the active or bound form is often referred to as the holo state.
Monomers
Individual units that make up a larger molecule.
Monomers are the individual building blocks of larger molecules, such as proteins and polymers. They can join together to form chains or complex structures.
HREMD
High-Resolution Enhanced Molecular Dynamics.
HREMD is a computational method used to simulate the movement and interactions of molecules at a high resolution. It provides insights into protein structure and dynamics.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:27:30|
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 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 12,307,806 | 744,398 | 16.53 | 1 hrs 27 mins |
| 2 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 12,184,764 | 657,926 | 18.52 | 1 hrs 18 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 11,978,150 | 724,089 | 16.54 | 1 hrs 27 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,841,288 | 610,683 | 11.20 | 2 hrs 9 mins |
| 5 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,651,272 | 605,775 | 10.98 | 2 hrs 11 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,918,495 | 582,335 | 10.16 | 2 hrs 22 mins |
| 7 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 5,775,164 | 576,457 | 10.02 | 2 hrs 24 mins |
| 8 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 5,646,400 | 575,164 | 9.82 | 2 hrs 27 mins |
| 9 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,052,096 | 553,861 | 9.12 | 2 hrs 38 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,436,637 | 523,486 | 8.48 | 2 hrs 50 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,420,715 | 530,700 | 8.33 | 2 hrs 53 mins |
| 12 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,119,188 | 496,181 | 8.30 | 2 hrs 53 mins |
| 13 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,632,309 | 494,564 | 7.34 | 3 hrs 16 mins |
| 14 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,107,471 | 475,394 | 6.54 | 3 hrs 40 mins |
| 15 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,784,057 | 461,227 | 6.04 | 3 hrs 59 mins |
| 16 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,701,086 | 432,239 | 6.25 | 3 hrs 50 mins |
| 17 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,369,465 | 430,785 | 5.50 | 4 hrs 22 mins |
| 18 | RTX A4000 GA104GL [RTX A4000] |
Nvidia | GA104GL | 2,345,512 | 428,360 | 5.48 | 4 hrs 23 mins |
| 19 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,215,460 | 418,620 | 5.29 | 4 hrs 32 mins |
|
|
|||||||
| 20 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,561,023 | 380,387 | 4.10 | 5 hrs 51 mins |
| 21 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,165,281 | 368,424 | 3.16 | 7 hrs 35 mins |
| 22 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,146,019 | 337,326 | 3.40 | 7 hrs 4 mins |
| 23 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 973,387 | 320,972 | 3.03 | 7 hrs 55 mins |
| 24 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 500,469 | 275,849 | 1.81 | 13 hrs 14 mins |
| 25 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 311,719 | 208,165 | 1.50 | 16 hrs 2 mins |
| 26 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 310,256 | 164,120 | 1.89 | 12 hrs 42 mins |
| 27 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 305,641 | 234,157 | 1.31 | 18 hrs 23 mins |
| 28 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 242,297 | 200,439 | 1.21 | 19 hrs 51 mins |
| 29 | Tesla K40m GK110 [Tesla K40m] 5046 |
Nvidia | GK110 | 241,838 | 216,821 | 1.12 | 21 hrs 31 mins |
| 30 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 208,444 | 191,475 | 1.09 | 22 hrs 3 mins |
| 31 | R7 370/R9 270X/370X Curacao XT/Trinidad XT [R7 370/R9 270X/370X] |
AMD | Curacao XT/Trinidad XT | 137,302 | 166,658 | 0.82 | 29 hrs 8 mins |
| 32 | Radeon 540/540X/550/550X/RX 540X/550/550X Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X] |
AMD | Lexa PRO | 81,543 | 150,000 | 0.54 | 44 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:27:30|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|