PROJECT #18713 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE
PROJECT SUMMARY
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.
PROJECT INFO
Manager(s): Rafal Wiewiora
Institution: Roivant Sciences (Silicon Therapeutics)
Project URL: roivant.com
PROJECT WORK UNIT SUMMARY
Atoms: 98,085
Core: 0x22
Status: Beta
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Saturday, 01 April 2023 12:14:45
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 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 11,784,480 | 722,481 | 16.31 | 1 hrs 28 mins |
2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 9,378,430 | 669,711 | 14.00 | 2 hrs 43 mins |
3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,418,365 | 585,863 | 10.96 | 2 hrs 11 mins |
4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,685,031 | 490,273 | 7.52 | 3 hrs 12 mins |
5 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,205,130 | 472,349 | 6.79 | 4 hrs 32 mins |
6 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,245,856 | 628,049 | 3.58 | 7 hrs 43 mins |
7 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,087,950 | 326,398 | 3.33 | 7 hrs 12 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Saturday, 01 April 2023 12:14:45
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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