Research section Research projects and references

Research: CANCER Folding Project #18724

Project #18724 overview

Project Summary AI Beta

This project explores how a protein called TNF-alpha works in both fighting and fueling cancer. Researchers are studying how its shape changes when it binds to another molecule, which can either help or harm cancer cells. They're using powerful computers to simulate how this protein behaves and hope to learn more about how to use it for cancer treatment.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Rafal Wiewiora
Institution
Roivant Sciences (Silicon Therapeutics)

Work unit

Atoms
77,809
Core
0x22
Status
Public
Source material

Official Project Description

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.

Performance data

Hardware Performance for Project 18724

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:50:19

GPU PPD Averages

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 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 8,412,102 796,152 10.57 2 hrs 16 mins
2 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 6,712,461 721,111 9.31 2 hrs 35 mins
3 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,479,506 645,549 6.94 3 hrs 28 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,746,144 558,277 4.92 4 hrs 53 mins