PROJECT #18123 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 2,492,501 128,131 19.45 1 hrs 14 mins
2 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,186,237 122,764 17.81 1 hrs 21 mins
3 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,586,471 110,373 14.37 2 hrs 40 mins
4 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,384,862 100,894 13.73 2 hrs 45 mins
5 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,196,025 100,476 11.90 2 hrs 1 mins
6 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,171,815 99,682 11.76 2 hrs 2 mins
7 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,114,238 98,099 11.36 2 hrs 7 mins
8 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,016,066 91,728 11.08 2 hrs 10 mins
9 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,003,058 94,903 10.57 2 hrs 16 mins
10 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 961,288 91,255 10.53 2 hrs 17 mins
11 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 751,990 86,163 8.73 3 hrs 45 mins
12 Quadro T1200 Mobile
TU117GLM [Quadro T1200 Mobile]
Nvidia TU117GLM 745,045 86,232 8.64 3 hrs 47 mins
13 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 709,301 84,392 8.40 3 hrs 51 mins
14 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 627,372 81,098 7.74 3 hrs 6 mins
15 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 582,277 47,829 12.17 2 hrs 58 mins
16 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 514,258 75,929 6.77 4 hrs 33 mins
17 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 387,962 69,467 5.58 4 hrs 18 mins
18 P104-100
GP104 [P104-100]
Nvidia GP104 318,779 65,083 4.90 5 hrs 54 mins
19 P106-090
GP106 [P106-090]
Nvidia GP106 196,809 55,229 3.56 7 hrs 44 mins
20 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 166,891 52,322 3.19 8 hrs 31 mins
21 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 145,833 49,773 2.93 8 hrs 11 mins
22 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 145,489 49,762 2.92 8 hrs 13 mins
23 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 80,823 40,785 1.98 12 hrs 7 mins
24 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 67,005 38,443 1.74 14 hrs 46 mins
25 GeForce GT 730
GK208B [GeForce GT 730] 692.7
Nvidia GK208B 40,683 32,697 1.24 19 hrs 17 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