PROJECT #18118 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 Thursday, 01 December 2022 06:13:24

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 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,458,544 131,452 18.70 1 hrs 17 mins
2 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,257,270 126,944 17.78 1 hrs 21 mins
3 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,100,130 124,789 16.83 1 hrs 26 mins
4 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,926,328 118,530 16.25 1 hrs 29 mins
5 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,791,179 118,544 15.11 2 hrs 35 mins
6 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,776,607 116,639 15.23 2 hrs 35 mins
7 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,746,875 112,077 15.59 2 hrs 32 mins
8 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,525,998 116,499 13.10 2 hrs 50 mins
9 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,385,604 108,778 12.74 2 hrs 53 mins
10 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,338,199 107,273 12.47 2 hrs 55 mins
11 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q] 4550
Nvidia TU106M 1,312,071 106,432 12.33 2 hrs 57 mins
12 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,240,454 104,160 11.91 2 hrs 1 mins
13 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,150,524 102,003 11.28 2 hrs 8 mins
14 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,116,046 100,517 11.10 2 hrs 10 mins
15 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,004,548 95,874 10.48 2 hrs 17 mins
16 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 832,498 91,665 9.08 3 hrs 39 mins
17 Quadro P2200
GP106GL [Quadro P2200]
Nvidia GP106GL 793,124 89,957 8.82 3 hrs 43 mins
18 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 776,712 88,323 8.79 3 hrs 44 mins
19 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 765,366 89,122 8.59 3 hrs 48 mins
20 GeForce GTX 1650
TU117 [GeForce GTX 1650] 3091
Nvidia TU117 712,968 87,223 8.17 3 hrs 56 mins
21 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 545,853 79,587 6.86 3 hrs 30 mins
22 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 504,335 75,626 6.67 4 hrs 36 mins
23 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 485,300 72,031 6.74 4 hrs 34 mins
24 P104-100
GP104 [P104-100]
Nvidia GP104 263,630 62,532 4.22 6 hrs 42 mins
25 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 262,249 61,552 4.26 6 hrs 38 mins
26 P106-100
GP106 [P106-100]
Nvidia GP106 194,761 56,516 3.45 7 hrs 58 mins
27 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 177,170 52,868 3.35 7 hrs 10 mins
28 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 141,418 50,698 2.79 9 hrs 36 mins
29 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 141,220 50,637 2.79 9 hrs 36 mins
30 GeForce GTX 690
GK104 [GeForce GTX 690] 3130
Nvidia GK104 132,774 47,403 2.80 9 hrs 34 mins
31 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 123,267 47,297 2.61 9 hrs 13 mins
32 Quadro P620
GP107GL [Quadro P620]
Nvidia GP107GL 100,828 44,930 2.24 11 hrs 42 mins
33 GeForce GTX 660 Ti
GK104 [GeForce GTX 660 Ti] 2634
Nvidia GK104 100,622 45,274 2.22 11 hrs 48 mins
34 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 90,282 43,681 2.07 12 hrs 37 mins
35 GeForce 940MX
GM108M [GeForce 940MX]
Nvidia GM108M 55,125 37,004 1.49 16 hrs 7 mins
36 GeForce GT 730
GK208B [GeForce GT 730] 692.7
Nvidia GK208B 52,839 36,729 1.44 17 hrs 41 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Thursday, 01 December 2022 06:13:24

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