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PROJECT #18106 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

We are simulating publicly available protein and small molecule structures of the currently very hot cancer target KRas, see https://www.fiercepharma.com/pharma/amgen-s-lumakras-becomes-first-fda-approved-kras-inhibitor-for-lung-cancer-patients for recent developments. Folding@home has previously looked at this protein (in project 10490), and the following part of the description is copied from there:

This project is "studying a small protein called KRAS, which forms a key link in growth signaling and cancer. This gene is something like a molecular switch with a timer. When it is bound to a molecule called GDP, it is off, and does not signal that the cell should grow. However, other proteins can cause it to swap its GDP for a GTP, turning KRAS on. In the on state, it signals that the cell should grow and divide. Normally, after some time, KRAS, with the aid of some partners, will chemically convert its GTP to GDP and return to its inactive state.

In many cancers, this protein becomes mutated, and cannot return to its off state. The result? The cells continue to divide without limit. What’s worse, cancers with this protein mutated tend to have much poorer prognoses. As a result, scientists have been trying to target this protein for decades."

We are investigating the dynamic behavior of KRas with these publicly disclosed inhibitors so that we can apply this knowledge to our own drug design. At the same time, we are further testing the adaptive sampling methodology. All data is being made publicly available, and insights from methodology developments will be shared. 

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/

PROJECT INFO

Manager(s): Rafal Wiewiora

Institution: Roivant Sciences (Silicon Therapeutics)

Project URL: roivant.com

PROJECT WORK UNIT SUMMARY

Atoms: 19,678

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Friday, 23 July 2021 12:13:38

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 3090
GA102 [GeForce RTX 3090]		 Nvidia GA102 3,002,499 127,812 23.49 1 hrs 1 mins
2 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448		 Nvidia TU102 2,991,841 126,355 23.68 1 hrs 1 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]		 Nvidia TU104 2,491,744 158,159 15.75 2 hrs 31 mins
4 TITAN Xp
GP102 [TITAN Xp] 12150		 Nvidia GP102 2,243,098 115,656 19.39 1 hrs 14 mins
5 GeForce RTX 3080
GA102 [GeForce RTX 3080]		 Nvidia GA102 2,152,057 114,490 18.80 1 hrs 17 mins
6 GeForce RTX 3070
GA104 [GeForce RTX 3070]		 Nvidia GA104 1,977,802 110,888 17.84 1 hrs 21 mins
7 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465		 Nvidia TU106 1,779,827 107,119 16.62 1 hrs 27 mins
8 GeForce RTX 2080
TU104 [GeForce RTX 2080]		 Nvidia TU104 1,480,044 100,488 14.73 2 hrs 38 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068		 Nvidia TU104 1,474,597 100,679 14.65 2 hrs 38 mins
10 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]		 Nvidia GA104 1,458,181 100,376 14.53 2 hrs 39 mins
11 GeForce GTX 960
GM206 [GeForce GTX 960] 2308		 Nvidia GM206 82,999 21,637 3.84 6 hrs 15 mins
12 GeForce GT 1030
GP108 [GeForce GT 1030] 1127		 Nvidia GP108 65,465 35,818 1.83 13 hrs 8 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Friday, 23 July 2021 12:13:38

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