PROJECT #18127 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: 49,000
Core: OPENMM_22
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Saturday, 01 April 2023 12:14:47
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 Ti GA102 [GeForce RTX 3090 Ti] |
Nvidia | GA102 | 4,769,212 | 263,532 | 18.10 | 1 hrs 20 mins |
2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 4,766,486 | 257,353 | 18.52 | 1 hrs 18 mins |
3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,321,834 | 251,238 | 17.20 | 1 hrs 24 mins |
4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 4,026,350 | 244,597 | 16.46 | 1 hrs 27 mins |
5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,844,435 | 243,946 | 15.76 | 2 hrs 31 mins |
6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,471,256 | 236,953 | 14.65 | 2 hrs 38 mins |
7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,346,583 | 233,948 | 14.30 | 2 hrs 41 mins |
8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,061,483 | 227,983 | 13.43 | 2 hrs 47 mins |
9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,013,943 | 226,499 | 13.31 | 2 hrs 48 mins |
10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,873,072 | 223,359 | 12.86 | 2 hrs 52 mins |
11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,710,411 | 216,913 | 12.50 | 2 hrs 55 mins |
12 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,448,867 | 212,132 | 11.54 | 2 hrs 5 mins |
13 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 2,209,575 | 204,982 | 10.78 | 2 hrs 14 mins |
14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,105,368 | 200,688 | 10.49 | 2 hrs 17 mins |
15 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,074,048 | 199,541 | 10.39 | 2 hrs 19 mins |
16 | Tesla P100 16GB GP100GL [Tesla P100 16GB] 9340 |
Nvidia | GP100GL | 1,973,661 | 197,288 | 10.00 | 2 hrs 24 mins |
17 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,692,469 | 186,770 | 9.06 | 3 hrs 39 mins |
18 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,684,496 | 186,008 | 9.06 | 3 hrs 39 mins |
19 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,295,781 | 170,895 | 7.58 | 3 hrs 10 mins |
20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,200,522 | 165,097 | 7.27 | 3 hrs 18 mins |
21 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,167,146 | 165,901 | 7.04 | 3 hrs 25 mins |
22 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,125,169 | 155,407 | 7.24 | 3 hrs 19 mins |
23 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,057,014 | 160,172 | 6.60 | 4 hrs 38 mins |
24 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 985,100 | 153,866 | 6.40 | 4 hrs 45 mins |
25 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 739,522 | 142,505 | 5.19 | 5 hrs 37 mins |
26 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 696,307 | 139,159 | 5.00 | 5 hrs 48 mins |
27 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 633,959 | 134,875 | 4.70 | 5 hrs 6 mins |
28 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 631,881 | 135,076 | 4.68 | 5 hrs 8 mins |
29 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 593,078 | 133,816 | 4.43 | 5 hrs 25 mins |
30 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 568,078 | 129,590 | 4.38 | 5 hrs 28 mins |
31 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 487,337 | 80,889 | 6.02 | 4 hrs 59 mins |
32 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 334,130 | 108,962 | 3.07 | 8 hrs 50 mins |
33 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 333,690 | 108,899 | 3.06 | 8 hrs 50 mins |
34 | Quadro M4000 GM204GL [Quadro M4000] |
Nvidia | GM204GL | 288,329 | 106,032 | 2.72 | 9 hrs 50 mins |
35 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 230,386 | 105,670 | 2.18 | 11 hrs 0 mins |
36 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 226,999 | 95,164 | 2.39 | 10 hrs 4 mins |
37 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 120,911 | 78,796 | 1.53 | 16 hrs 38 mins |
38 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 79,512 | 67,604 | 1.18 | 20 hrs 24 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
PPDDB data as of Saturday, 01 April 2023 12:14:47
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|