PROJECT #18126 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 at https://console.cloud.google.com/storage/browser/stxfah-bucket, 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/ 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: 36,000

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Tuesday, 16 August 2022 22:52:19

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 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 3,989,608 186,732 21.37 1 hrs 7 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,979,835 187,663 21.21 1 hrs 8 mins
3 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 3,701,223 186,479 19.85 1 hrs 13 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 3,367,177 177,952 18.92 1 hrs 16 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,244,629 173,752 18.67 1 hrs 17 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 2,976,708 171,956 17.31 1 hrs 23 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 2,921,226 171,520 17.03 1 hrs 25 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,732,143 167,595 16.30 1 hrs 28 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 2,671,700 167,150 15.98 2 hrs 30 mins
10 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,535,583 163,137 15.54 2 hrs 33 mins
11 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 2,493,500 162,614 15.33 2 hrs 34 mins
12 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 2,408,252 158,189 15.22 2 hrs 35 mins
13 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,351,463 158,939 14.79 2 hrs 37 mins
14 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 1,967,011 150,192 13.10 2 hrs 50 mins
15 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,903,747 148,566 12.81 2 hrs 52 mins
16 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 1,719,457 142,679 12.05 2 hrs 59 mins
17 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 1,706,190 144,323 11.82 2 hrs 2 mins
18 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,587,733 140,193 11.33 2 hrs 7 mins
19 Tesla P100 16GB
GP100GL [Tesla P100 16GB] 9340
Nvidia GP100GL 1,539,128 138,742 11.09 2 hrs 10 mins
20 GeForce RTX 3060
GA104 [GeForce RTX 3060]
Nvidia GA104 1,489,146 137,607 10.82 2 hrs 13 mins
21 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,169,976 123,906 9.44 3 hrs 33 mins
22 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,123,940 124,219 9.05 3 hrs 39 mins
23 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,089,132 123,201 8.84 3 hrs 43 mins
24 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 993,118 118,085 8.41 3 hrs 51 mins
25 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 979,482 118,246 8.28 3 hrs 54 mins
26 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 854,178 111,737 7.64 3 hrs 8 mins
27 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 841,971 113,016 7.45 3 hrs 13 mins
28 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 835,166 105,475 7.92 3 hrs 2 mins
29 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 683,403 106,128 6.44 4 hrs 44 mins
30 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 655,859 103,504 6.34 4 hrs 47 mins
31 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 564,128 99,509 5.67 4 hrs 14 mins
32 Quadro M4000
GM204GL [Quadro M4000]
Nvidia GM204GL 309,302 81,092 3.81 6 hrs 18 mins
33 P106-090
GP106 [P106-090]
Nvidia GP106 304,455 80,612 3.78 6 hrs 21 mins
34 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 252,264 70,210 3.59 7 hrs 41 mins
35 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 250,016 69,268 3.61 7 hrs 39 mins
36 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 239,725 76,048 3.15 8 hrs 37 mins
37 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 220,870 72,126 3.06 8 hrs 50 mins
38 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 168,385 62,022 2.71 9 hrs 50 mins
39 GeForce GTX 660
GK106 [GeForce GTX 660] 1981
Nvidia GK106 52,344 51,847 1.01 24 hrs 46 mins
40 Quadro K600
GK107 [Quadro K600]
Nvidia GK107 34,323 34,323 1.00 24 hrs 0 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Tuesday, 16 August 2022 22:52:19

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