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 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 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 6,426,861 586,982 10.95 2 hrs 12 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 3,275,431 476,634 6.87 3 hrs 30 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 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