PROJECT #18716 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: 81,870
Core: 0x22
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Monday, 20 March 2023 06:14:51
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 | 3 hrs 38 mins |
10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,436,637 | 523,486 | 8.48 | 3 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 | 3 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 | 3 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 | 4 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 | 4 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 | 4 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 | 5 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 | 6 hrs 51 mins |
21 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,165,281 | 368,424 | 3.16 | 8 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 | 8 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] |
Ellesmere XT | 310,256 | 164,120 | 1.89 | 13 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 | 20 hrs 51 mins |
29 | Tesla K40m GK110 [Tesla K40m] 5046 |
Nvidia | GK110 | 241,838 | 216,821 | 1.12 | 22 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
PPDDB data as of Monday, 20 March 2023 06:14:51
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|