PROJECT #16497 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

In drug discovery, particularly that of cancer, maximizing state exploration is a useful novel strategy – providing new protein states and conformations to point drug design methods at increases the likelihood that a potential binder and inhibitor may be found.

However, in many cases a new state that is "useful for design" (ie.

ones distinct enough to be worth targeting to identify novel drugs) require a lot of sampling or simulation.

Sometimes, even exascale computers like Folding@home are not enough! Adaptive methods are very powerful here, but have the drawback of requiring system knowledge, or having to guess which protein features are worth adaptively exploring on, which may not always turn out to be true.

Another promising strategy, explored in these projects, is to "Accelerate" the simulations.

By broadly applying "boosters" to the simulation, we effectively "flatten" the energy landscape of a protein's conformations, allowing the protein to visit states more easily than it normally would.

Alongside the ability to discover new states that we can seed simulations of, just like adaptive sampling simulations, these boosters have specific technical and physiclal properties that allow us to infer something about a new state's "accessibility" (ie where it exists on the landscape). In projects 16497–16499 we test three such boosters to accelerate our simulations to identify how well boosted simulations work for our purposes.

Here we apply it to the protein MET kinase, a protein drug target in many cancers such as non-small-cell lung carcinoma.

MET kinase is targeted by the drug crizotinib but often evolves resistance against the drug, rendering it ineffective.

With our boosted simulations we hope to observe never before seen states of MET!.

PROJECT INFO

Manager(s): Sukrit Singh

Institution: Memorial Sloan-Kettering Cancer-Center

Project URL: http://sukritsingh.github.io/

PROJECT WORK UNIT SUMMARY

Atoms: 59,897

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 27 June 2022 11:45:25

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 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 4,748,390 251,305 18.89 1 hrs 16 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,332,929 261,229 16.59 1 hrs 27 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 3,997,810 254,975 15.68 2 hrs 32 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 3,927,275 256,064 15.34 2 hrs 34 mins
5 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,887,424 245,763 15.82 2 hrs 31 mins
6 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,647,529 224,198 16.27 1 hrs 29 mins
7 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,545,733 246,061 14.41 2 hrs 40 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,538,004 247,238 14.31 2 hrs 41 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,243,783 239,416 13.55 2 hrs 46 mins
10 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,074,279 234,980 13.08 2 hrs 50 mins
11 GeForce RTX 3070 Mobile / Max-Q
GA104M [GeForce RTX 3070 Mobile / Max-Q]
Nvidia GA104M 2,877,682 231,670 12.42 2 hrs 56 mins
12 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,750,860 228,167 12.06 2 hrs 59 mins
13 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,704,602 208,909 12.95 2 hrs 51 mins
14 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,411,715 213,169 11.31 2 hrs 7 mins
15 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,382,046 215,077 11.08 2 hrs 10 mins
16 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,377,531 213,941 11.11 2 hrs 10 mins
17 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 2,333,332 215,177 10.84 2 hrs 13 mins
18 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 1,971,291 202,443 9.74 2 hrs 28 mins
19 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,769,704 189,393 9.34 3 hrs 34 mins
20 GeForce RTX 3060
GA104 [GeForce RTX 3060]
Nvidia GA104 1,701,605 193,562 8.79 3 hrs 44 mins
21 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,613,187 182,612 8.83 3 hrs 43 mins
22 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,461,449 156,824 9.32 3 hrs 35 mins
23 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,218,378 173,166 7.04 3 hrs 25 mins
24 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,160,121 170,922 6.79 4 hrs 32 mins
25 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,114,895 166,608 6.69 4 hrs 35 mins
26 Geforce RTX 3050
GA106 [Geforce RTX 3050]
Nvidia GA106 1,065,868 165,833 6.43 4 hrs 44 mins
27 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,037,320 164,429 6.31 4 hrs 48 mins
28 GeForce GTX 1660 Mobile
TU116M [GeForce GTX 1660 Mobile]
Nvidia TU116M 1,006,248 162,084 6.21 4 hrs 52 mins
29 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 907,832 156,557 5.80 4 hrs 8 mins
30 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 825,397 143,165 5.77 4 hrs 10 mins
31 P104-100
GP104 [P104-100]
Nvidia GP104 624,600 139,072 4.49 5 hrs 21 mins
32 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 512,441 135,511 3.78 6 hrs 21 mins
33 GeForce GTX 1650 Mobile / Max-Q
TU117M [GeForce GTX 1650 Mobile / Max-Q]
Nvidia TU117M 453,747 125,271 3.62 7 hrs 38 mins
34 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 397,699 121,206 3.28 7 hrs 19 mins
35 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 388,830 118,458 3.28 7 hrs 19 mins
36 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 346,307 93,367 3.71 6 hrs 28 mins
37 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 186,861 93,098 2.01 12 hrs 57 mins
38 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 117,604 80,027 1.47 16 hrs 20 mins
39 Quadro M2000
GM206GL [Quadro M2000]
Nvidia GM206GL 87,721 84,525 1.04 23 hrs 8 mins
40 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 52,483 60,601 0.87 28 hrs 43 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 27 June 2022 11:45:25

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