PROJECT #18712 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: 96,249

Core: 0x22

Status: Beta

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Wednesday, 07 December 2022 06:13:21

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 12,843,384 727,041 17.67 1 hrs 22 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 9,452,119 655,637 14.42 2 hrs 40 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,509,601 577,417 11.27 2 hrs 8 mins
4 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,614,979 519,374 8.89 3 hrs 42 mins
5 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 4,039,087 495,903 8.14 3 hrs 57 mins
6 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,059,012 420,259 7.28 3 hrs 18 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,655,633 423,432 6.27 4 hrs 50 mins
8 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,561,360 426,387 6.01 4 hrs 60 mins
9 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,377,508 645,212 3.68 7 hrs 31 mins
10 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,342,166 411,882 5.69 4 hrs 13 mins
11 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 915,183 329,985 2.77 9 hrs 39 mins
12 GeForce GTX Titan Black
GK110 [GeForce GTX Titan Black] 5121
Nvidia GK110 192,365 210,665 0.91 26 hrs 17 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Wednesday, 07 December 2022 06:13:21

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make