PROJECT #18107 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: 19,714

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Friday, 23 July 2021 15:14:08

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 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 3,785,993 241,316 15.69 2 hrs 32 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 2,918,435 124,589 23.42 1 hrs 1 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 2,833,485 124,173 22.82 1 hrs 3 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 2,452,272 118,408 20.71 1 hrs 10 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 2,376,181 117,261 20.26 1 hrs 11 mins
6 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 2,348,076 117,317 20.01 1 hrs 12 mins
7 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 2,184,895 114,683 19.05 1 hrs 16 mins
8 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 2,074,730 111,428 18.62 1 hrs 17 mins
9 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,888,177 109,286 17.28 1 hrs 23 mins
10 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 1,843,126 108,319 17.02 1 hrs 25 mins
11 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 1,687,449 105,108 16.05 1 hrs 30 mins
12 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 1,604,013 103,190 15.54 2 hrs 33 mins
13 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 1,578,502 102,741 15.36 2 hrs 34 mins
14 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 1,491,906 101,005 14.77 2 hrs 37 mins
15 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 1,464,539 100,077 14.63 2 hrs 38 mins
16 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,454,810 100,108 14.53 2 hrs 39 mins
17 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,381,972 97,454 14.18 2 hrs 42 mins
18 GeForce RTX 3070 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3070 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,257,675 95,092 13.23 2 hrs 49 mins
19 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,212,623 93,952 12.91 2 hrs 52 mins
20 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,161,292 92,687 12.53 2 hrs 55 mins
21 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,138,482 91,976 12.38 2 hrs 56 mins
22 Quadro P5000
GP104GL [Quadro P5000]
Nvidia GP104GL 1,028,479 82,135 12.52 2 hrs 55 mins
23 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 972,955 87,321 11.14 2 hrs 9 mins
24 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 929,383 86,156 10.79 2 hrs 13 mins
25 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 809,643 59,575 13.59 2 hrs 46 mins
26 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 742,976 63,840 11.64 2 hrs 4 mins
27 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 579,682 73,706 7.86 3 hrs 3 mins
28 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 453,265 67,737 6.69 4 hrs 35 mins
29 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 402,014 58,733 6.84 4 hrs 30 mins
30 P104-100
GP104 [P104-100]
Nvidia GP104 255,328 56,126 4.55 5 hrs 17 mins
31 P106-100
GP106 [P106-100]
Nvidia GP106 186,298 50,770 3.67 7 hrs 32 mins
32 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 112,076 42,568 2.63 9 hrs 7 mins
33 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 62,445 29,114 2.14 11 hrs 11 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Friday, 23 July 2021 15:14:08

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