PROJECT #18124 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: 25,000

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Saturday, 15 January 2022 23:40:51

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,350,502 125,784 18.69 1 hrs 17 mins
2 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,044,333 120,242 17.00 1 hrs 25 mins
3 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,579,102 110,312 14.31 2 hrs 41 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,409,493 106,038 13.29 2 hrs 48 mins
5 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,372,679 105,149 13.05 2 hrs 50 mins
6 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,140,806 98,585 11.57 2 hrs 4 mins
7 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,087,591 93,728 11.60 2 hrs 4 mins
8 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,030,474 95,414 10.80 2 hrs 13 mins
9 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 866,597 87,500 9.90 2 hrs 25 mins
10 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 801,786 88,343 9.08 3 hrs 39 mins
11 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 611,927 79,828 7.67 3 hrs 8 mins
12 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 606,190 81,168 7.47 3 hrs 13 mins
13 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 592,509 79,348 7.47 3 hrs 13 mins
14 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 572,552 78,476 7.30 3 hrs 17 mins
15 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 485,622 74,467 6.52 4 hrs 41 mins
16 T600
TU117GL [T600]
TU117GL 389,751 69,018 5.65 4 hrs 15 mins
17 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 385,433 69,490 5.55 4 hrs 20 mins
18 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 320,113 55,796 5.74 4 hrs 11 mins
19 P104-100
GP104 [P104-100]
Nvidia GP104 294,386 63,257 4.65 5 hrs 9 mins
20 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 252,244 59,277 4.26 6 hrs 38 mins
21 P106-090
GP106 [P106-090]
Nvidia GP106 210,752 56,204 3.75 6 hrs 24 mins
22 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 160,976 51,588 3.12 8 hrs 41 mins
23 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 143,000 49,414 2.89 8 hrs 18 mins
24 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 130,778 48,122 2.72 9 hrs 50 mins
25 GeForce GTX 1050 Mobile
GP107M [GeForce GTX 1050 Mobile]
Nvidia GP107M 126,363 48,142 2.62 9 hrs 9 mins
26 GeForce GTX 660 Ti
GK104 [GeForce GTX 660 Ti] 2634
Nvidia GK104 112,059 45,701 2.45 10 hrs 47 mins
27 GeForce 940MX
GM108M [GeForce 940MX]
Nvidia GM108M 46,190 33,413 1.38 17 hrs 22 mins
28 GeForce GTX 765M
GK106 [GeForce GTX 765M]
Nvidia GK106 33,925 30,831 1.10 22 hrs 49 mins
29 GeForce MX130
GM108M [GeForce MX130]
Nvidia GM108M 32,231 24,000 1.34 18 hrs 52 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Saturday, 15 January 2022 23:40:51

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