PROJECT #16491 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

Kinases are a major target for a variety of cancer therapies, but their mechanism of action is relatively unknown at a detailed atomic level, preventing us from understanding and optimizing known inhibitors. 

One example is the Serine/Threonine Kinase RIPK2. RIPK2 inhibition is useful for cancer targeting as it prevents RIPK2 from binding a protein partner named XIAP. In fact, there are already 3 known inhibitors that bind to RIPK2 and prevent RIPK2-XIAP binding! However, it remains difficult to optimize these ligands for clinical purposes because we do not understand how any of these three inhibitors actually act on RIPK2 to prevent XIAP binding behavior. 

These four projects are simulating RIPK2 by itself and bound to each of the three inhibitors, with the hope that this will reveal a more detailed mechanism of how each inhibitor works to prevent XIAP binding. As a bonus, this will help reveal how RIPK2 *binds* XIAP (also unknown)! 

In this set of projects we are studying the following systems:

    16466 – RIPK2

    16467 – RIPK2:CSLP43 inhibitor-bound complex

    16468 – RIPK2:CSLP48 inhibitor-bound complex

    16469 – RIPK2:GSK583 inhibitor-bound complex

    16470 – RIPK2:WEHI-345 inhibitor-bound complex

    16471 – RIPK2:BI inhibitor-bound complex

    16488 – RIPK2:BI inhibitor-bound complex (alt configuration)

    16489 – RIPK2:BI inhibitor-bound complex (alt configuration 2)

    16490 – RIPK2:NVS inhibitor-bound complex

    16491 – RIPK2 apo from the GSK bound structure

    16492 – RIPK2 apo fro the BI bound structure

 

PROJECT INFO

Manager(s): Sukrit Singh

Institution: Memorial Sloan-Kettering Cancer-Center

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

PROJECT WORK UNIT SUMMARY

Atoms: 45,123

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Saturday, 15 January 2022 23:40:54

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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 4,913,160 97,605 50.34 0 hrs 29 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 3,827,282 88,870 43.07 1 hrs 33 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 3,576,111 87,190 41.02 1 hrs 35 mins
4 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,561,323 84,597 42.10 1 hrs 34 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,069,793 82,915 37.02 1 hrs 39 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 2,789,894 80,417 34.69 1 hrs 42 mins
7 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,243,182 73,965 30.33 1 hrs 47 mins
8 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 2,199,573 74,029 29.71 1 hrs 48 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,172,180 73,581 29.52 1 hrs 49 mins
10 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 2,082,237 73,180 28.45 1 hrs 51 mins
11 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,069,678 73,537 28.14 1 hrs 51 mins
12 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,034,354 72,477 28.07 1 hrs 51 mins
13 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 1,869,837 70,314 26.59 1 hrs 54 mins
14 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,869,711 70,902 26.37 1 hrs 55 mins
15 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,521,807 65,845 23.11 1 hrs 2 mins
16 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,500,757 65,737 22.83 1 hrs 3 mins
17 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,095,056 59,054 18.54 1 hrs 18 mins
18 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,078,934 59,103 18.26 1 hrs 19 mins
19 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 985,253 56,965 17.30 1 hrs 23 mins
20 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 964,611 56,699 17.01 1 hrs 25 mins
21 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 583,551 47,954 12.17 2 hrs 58 mins
22 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 510,255 45,827 11.13 2 hrs 9 mins
23 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 447,079 42,949 10.41 2 hrs 18 mins
24 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 366,903 41,189 8.91 3 hrs 42 mins
25 P104-100
GP104 [P104-100]
Nvidia GP104 314,029 39,254 8.00 3 hrs 0 mins
26 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 222,249 34,538 6.43 4 hrs 44 mins
27 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 46,780 20,808 2.25 11 hrs 41 mins
28 GeForce 920M
GK208 [GeForce 920M]
Nvidia GK208 25,781 17,059 1.51 16 hrs 53 mins
29 GeForce GT 710
GK208B [GeForce GT 710] 366
Nvidia GK208B 8,203 11,019 0.74 32 hrs 14 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Saturday, 15 January 2022 23:40:54

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