PROJECT #18711 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: 67,342

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 11,270,896 485,568 23.21 1 hrs 2 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,123,562 393,863 15.55 2 hrs 33 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 4,982,049 778,764 6.40 4 hrs 45 mins
4 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,059,361 316,545 9.66 2 hrs 29 mins
5 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,048,722 317,733 9.60 3 hrs 30 mins
6 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,642,562 256,123 6.41 4 hrs 45 mins
7 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,219,507 231,193 5.27 5 hrs 33 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