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