RESEARCH: CANCER
FOLDING PROJECT #16493 PROFILE
PROJECT TEAM
Manager(s): Sukrit SinghInstitution: Memorial Sloan-Kettering Cancer-Center
Project URL: View Project Website
WORK UNIT INFO
Atoms: 54,231Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies RIPK2, a protein involved in cancer growth. Researchers are using computer models to see how 3 known inhibitors block RIPK2 from interacting with another protein called XIAP. This could help develop better cancer drugs.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
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 16493 – RIPK2 apo from a Gefitinib bound structure 16494 – RIPK2 apo from a Novartis-produced inhibitor 16495 – RIPK2:Gefitinib complex 16496 – RIPK2:Novartis inhibitor structure.
RELATED TERMS GLOSSARY AI BETA
Kinases
Enzymes that transfer phosphate groups to molecules.
Kinases are a crucial type of enzyme involved in many cellular processes. They add phosphate groups to other molecules, acting like switches to turn processes on or off. This makes them important targets for cancer therapies because cancer cells often have abnormal kinase activity.
RIPK2
Receptor-interacting protein kinase 2
RIPK2 is a protein that plays a role in the body's immune response. It's involved in signaling pathways triggered by infections or cellular damage. Researchers are studying RIPK2 as a potential target for treating inflammatory diseases and cancer because its overactivity can contribute to these conditions.
XIAP
X-linked inhibitor of apoptosis protein
XIAP is a protein that blocks programmed cell death (apoptosis). Cancer cells often overexpress XIAP to evade destruction. Inhibiting XIAP could be a strategy for killing cancer cells.
Inhibitors
Substances that block the activity of a target molecule.
Inhibitors are molecules designed to prevent or reduce the action of other molecules. They are crucial tools in drug development because they can be used to target specific proteins involved in disease processes.
Ligands
Molecules that bind to receptors or other target proteins.
Ligands are molecules that have a specific shape and chemical properties allowing them to bind to target molecules like proteins. They play important roles in biological processes and are often used in drug development as they can be designed to activate or inhibit specific targets.
Apoptosis
Programmed cell death.
Apoptosis is a natural process of cell death that's essential for development and maintaining healthy tissues. It involves a series of carefully controlled steps that lead to the dismantling and removal of cells. Dysregulation of apoptosis can contribute to diseases like cancer.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Saturday, 25 April 2026 18:45:05|
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 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,033,559 | 119,141 | 42.25 | 0 hrs 34 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,022,099 | 117,958 | 42.58 | 0 hrs 34 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,895,352 | 118,725 | 41.23 | 0 hrs 35 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,276,496 | 114,809 | 37.25 | 0 hrs 39 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,707,475 | 109,371 | 33.90 | 0 hrs 42 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,591,894 | 108,165 | 33.21 | 0 hrs 43 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,485,772 | 105,360 | 33.08 | 0 hrs 44 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,971,544 | 100,907 | 29.45 | 0 hrs 49 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,754,007 | 99,441 | 27.69 | 0 hrs 52 mins |
| 10 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 2,627,122 | 97,465 | 26.95 | 0 hrs 53 mins |
| 11 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 2,403,741 | 92,494 | 25.99 | 0 hrs 55 mins |
| 12 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,261,536 | 92,875 | 24.35 | 0 hrs 59 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,260,994 | 92,898 | 24.34 | 0 hrs 59 mins |
| 14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,128,910 | 84,859 | 25.09 | 0 hrs 57 mins |
| 15 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,119,514 | 91,278 | 23.22 | 1 hrs 2 mins |
| 16 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,097,131 | 89,807 | 23.35 | 1 hrs 2 mins |
| 17 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,955,835 | 88,284 | 22.15 | 1 hrs 5 mins |
| 18 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 1,883,579 | 87,203 | 21.60 | 1 hrs 7 mins |
| 19 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,793,693 | 85,871 | 20.89 | 1 hrs 9 mins |
|
|
|||||||
| 20 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 1,688,992 | 84,058 | 20.09 | 1 hrs 12 mins |
| 21 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,601,640 | 83,418 | 19.20 | 1 hrs 15 mins |
| 22 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,520,821 | 79,357 | 19.16 | 1 hrs 15 mins |
| 23 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,370,494 | 77,349 | 17.72 | 1 hrs 21 mins |
| 24 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,362,524 | 78,176 | 17.43 | 1 hrs 23 mins |
| 25 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,273,225 | 76,512 | 16.64 | 1 hrs 27 mins |
| 26 | GeForce RTX 2080 Mobile TU104M [GeForce RTX 2080 Mobile] |
Nvidia | TU104M | 1,189,787 | 56,766 | 20.96 | 1 hrs 9 mins |
| 27 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,162,798 | 74,534 | 15.60 | 1 hrs 32 mins |
| 28 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,136,095 | 73,729 | 15.41 | 1 hrs 33 mins |
| 29 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,027,243 | 69,784 | 14.72 | 1 hrs 38 mins |
| 30 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 990,416 | 71,055 | 13.94 | 1 hrs 43 mins |
| 31 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 743,848 | 49,827 | 14.93 | 1 hrs 36 mins |
| 32 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 685,644 | 62,107 | 11.04 | 2 hrs 10 mins |
| 33 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 619,577 | 60,229 | 10.29 | 2 hrs 20 mins |
| 34 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 607,712 | 59,161 | 10.27 | 2 hrs 20 mins |
| 35 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 591,314 | 58,647 | 10.08 | 2 hrs 23 mins |
| 36 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 529,467 | 57,219 | 9.25 | 2 hrs 36 mins |
| 37 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 450,122 | 53,696 | 8.38 | 2 hrs 52 mins |
| 38 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 448,331 | 53,357 | 8.40 | 2 hrs 51 mins |
| 39 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 354,266 | 49,204 | 7.20 | 3 hrs 20 mins |
|
|
|||||||
| 40 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 327,016 | 48,932 | 6.68 | 3 hrs 35 mins |
| 41 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 318,078 | 48,387 | 6.57 | 3 hrs 39 mins |
| 42 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 301,463 | 47,148 | 6.39 | 3 hrs 45 mins |
| 43 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 229,181 | 42,903 | 5.34 | 4 hrs 30 mins |
| 44 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 218,943 | 41,748 | 5.24 | 4 hrs 35 mins |
| 45 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 207,421 | 42,127 | 4.92 | 4 hrs 52 mins |
| 46 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 126,984 | 33,175 | 3.83 | 6 hrs 16 mins |
| 47 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 123,401 | 35,199 | 3.51 | 6 hrs 51 mins |
| 48 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 115,434 | 34,264 | 3.37 | 7 hrs 7 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Saturday, 25 April 2026 18:45:05|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|