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 Thursday, 30 June 2022 00:49:55

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 Tesla P100 16GB
GP100GL [Tesla P100 16GB] 9340
Nvidia GP100GL 2,700,174 131,713 20.50 1 hrs 10 mins
2 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,318,588 124,534 18.62 1 hrs 17 mins
3 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,271,749 124,612 18.23 1 hrs 19 mins
4 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,217,118 123,315 17.98 1 hrs 20 mins
5 GeForce RTX 2070 SUPER Mobile / Max-Q
TU104M [GeForce RTX 2070 SUPER Mobile / Max-Q]
Nvidia TU104M 2,142,535 121,509 17.63 1 hrs 22 mins
6 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,139,606 121,976 17.54 1 hrs 22 mins
7 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,942,590 118,093 16.45 1 hrs 28 mins
8 GeForce RTX 2080 Mobile
TU104M [GeForce RTX 2080 Mobile]
Nvidia TU104M 1,754,113 113,678 15.43 2 hrs 33 mins
9 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,689,871 112,758 14.99 2 hrs 36 mins
10 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 1,635,035 110,643 14.78 2 hrs 37 mins
11 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,497,602 108,082 13.86 2 hrs 44 mins
12 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,270,303 101,764 12.48 2 hrs 55 mins
13 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,243,295 101,863 12.21 2 hrs 58 mins
14 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,178,933 98,714 11.94 2 hrs 1 mins
15 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,151,933 98,579 11.69 2 hrs 3 mins
16 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,127,035 97,218 11.59 2 hrs 4 mins
17 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,060,434 94,974 11.17 2 hrs 9 mins
18 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 949,078 93,178 10.19 2 hrs 21 mins
19 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 935,053 67,091 13.94 2 hrs 43 mins
20 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 930,841 91,381 10.19 2 hrs 21 mins
21 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 886,486 91,310 9.71 2 hrs 28 mins
22 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 874,941 75,963 11.52 2 hrs 5 mins
23 Tesla P40
GP102GL [Tesla P40] 11760
Nvidia GP102GL 788,234 87,582 9.00 3 hrs 40 mins
24 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 777,667 86,781 8.96 3 hrs 41 mins
25 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 698,795 83,052 8.41 3 hrs 51 mins
26 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 603,494 79,496 7.59 3 hrs 10 mins
27 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 528,191 76,160 6.94 3 hrs 28 mins
28 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 516,103 74,335 6.94 3 hrs 27 mins
29 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 491,052 74,772 6.57 4 hrs 39 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 479,884 73,807 6.50 4 hrs 41 mins
31 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 405,144 70,065 5.78 4 hrs 9 mins
32 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 394,660 69,328 5.69 4 hrs 13 mins
33 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 374,072 67,753 5.52 4 hrs 21 mins
34 P106-090
GP106 [P106-090]
Nvidia GP106 334,750 65,701 5.10 5 hrs 43 mins
35 T600
TU117GL [T600]
Intel TU117GL 307,690 61,673 4.99 5 hrs 49 mins
36 P104-100
GP104 [P104-100]
Nvidia GP104 303,137 63,920 4.74 5 hrs 4 mins
37 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 245,061 58,260 4.21 6 hrs 42 mins
38 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 218,487 57,248 3.82 6 hrs 17 mins
39 P106-100
GP106 [P106-100]
Nvidia GP106 196,623 55,174 3.56 7 hrs 44 mins
40 GeForce GTX 1050 Ti Mobile
GP107M [GeForce GTX 1050 Ti Mobile]
Nvidia GP107M 190,448 54,004 3.53 7 hrs 48 mins
41 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 154,554 50,738 3.05 8 hrs 53 mins
42 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 143,965 49,426 2.91 8 hrs 14 mins
43 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 140,398 49,092 2.86 8 hrs 24 mins
44 GeForce GTX 690
GK104 [GeForce GTX 690] 3130
Nvidia GK104 140,131 49,453 2.83 8 hrs 28 mins
45 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 139,177 48,978 2.84 8 hrs 27 mins
46 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 135,013 47,144 2.86 8 hrs 23 mins
47 GeForce GTX 660 Ti
GK104 [GeForce GTX 660 Ti] 2634
Nvidia GK104 110,577 45,628 2.42 10 hrs 54 mins
48 GeForce GTX 750
GM107 [GeForce GTX 750] 1111
Nvidia GM107 109,476 44,720 2.45 10 hrs 48 mins
49 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 104,799 44,263 2.37 10 hrs 8 mins
50 GeForce GTX 860M
GM107 [GeForce GTX 860M] 1389
Nvidia GM107 102,663 44,136 2.33 10 hrs 19 mins
51 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 83,991 41,302 2.03 12 hrs 48 mins
52 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 74,808 39,796 1.88 13 hrs 46 mins
53 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 56,943 26,037 2.19 11 hrs 58 mins
54 GeForce GT 730
GK208B [GeForce GT 730] 692.7
Nvidia GK208B 28,572 27,907 1.02 23 hrs 26 mins
55 GeForce GT 755M
GK107 [GeForce GT 755M]
Nvidia GK107 19,412 24,000 0.81 30 hrs 40 mins
56 GeForce GT 720
GK208 [GeForce GT 720]
Nvidia GK208 18,084 24,000 0.75 32 hrs 51 mins
57 GeForce GT 710
GK208B [GeForce GT 710] 366
Nvidia GK208B 15,885 24,000 0.66 36 hrs 16 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Thursday, 30 June 2022 00:49:55

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