PROJECT #18122 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 2,486,578 127,995 19.43 1 hrs 14 mins
2 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,158,731 122,167 17.67 1 hrs 21 mins
3 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,810,007 115,191 15.71 2 hrs 32 mins
4 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,564,517 109,978 14.23 2 hrs 41 mins
5 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,307,085 104,050 12.56 2 hrs 55 mins
6 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,228,433 100,233 12.26 2 hrs 57 mins
7 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,207,435 100,725 11.99 2 hrs 0 mins
8 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,101,757 96,735 11.39 2 hrs 6 mins
9 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,043,452 94,155 11.08 2 hrs 10 mins
10 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 836,053 89,498 9.34 3 hrs 34 mins
11 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 678,600 83,272 8.15 3 hrs 57 mins
12 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 628,717 80,728 7.79 3 hrs 5 mins
13 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 621,159 81,220 7.65 3 hrs 8 mins
14 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 537,672 76,970 6.99 3 hrs 26 mins
15 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 430,836 68,569 6.28 4 hrs 49 mins
16 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 390,997 69,634 5.62 4 hrs 16 mins
17 T600
TU117GL [T600]
TU117GL 332,203 63,158 5.26 5 hrs 34 mins
18 P104-100
GP104 [P104-100]
Nvidia GP104 303,137 63,920 4.74 5 hrs 4 mins
19 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 251,173 59,362 4.23 6 hrs 40 mins
20 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 155,179 50,952 3.05 8 hrs 53 mins
21 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 149,345 50,207 2.97 8 hrs 4 mins
22 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 128,950 47,855 2.69 9 hrs 54 mins
23 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 107,438 42,051 2.55 9 hrs 24 mins
24 GeForce GTX 860M
GM107 [GeForce GTX 860M] 1389
Nvidia GM107 102,663 44,136 2.33 10 hrs 19 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