RESEARCH: CANCER
FOLDING PROJECT #17773 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 125,196Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how proteins use ion gradients to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and play a key role in many diseases, making them important drug targets. Studying these proteins will help us understand how they work across different types of organisms.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17745-17750 Molecular basis of secondary active transporters. Secondary active membrane transporters are proteins that utilize ions to transport an assortment of molecules across cell membranes.
These proteins are found in all domains in life and surprisingly, despite vastly different structures, operate under the same mechanism by using an ion gradient to assist in small molecule transport.
Furthermore, many of these secondary active transporters are drug targets to treat diseases like cancer, diabetes, and neurological disorders.
The simulations in this project will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
Secondary active transporters
Proteins that use ion gradients to move molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They utilize the energy stored in an ion gradient to transport various molecules across cell membranes. This process is crucial for numerous cellular functions, including nutrient uptake, waste removal, and signal transduction. Many of these transporters are targets for drug development, as they play a role in diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient refers to the unequal distribution of charged ions across a cell membrane. This difference in concentration is maintained by various transport proteins and plays a vital role in cellular processes such as nerve impulse transmission, muscle contraction, and ATP synthesis.
Proteins
Large, complex molecules made up of amino acids.
Proteins are the workhorses of cells, performing a vast array of functions essential for life. They act as enzymes to catalyze biochemical reactions, provide structural support, transport molecules, and regulate cellular processes.
Cell membranes
Thin barriers that surround cells and regulate the passage of substances in and out.
Cell membranes are crucial for maintaining cellular integrity and separating the internal environment from the external world. They are composed of a phospholipid bilayer with embedded proteins that facilitate transport, communication, and recognition.
Simulations
Computer models used to mimic real-world processes.
Simulations are powerful tools used in computational biology to study complex biological systems. By creating computer models of molecules, cells, or even entire organisms, researchers can explore various scenarios and gain insights into biological mechanisms.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:39|
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 | 7,731,889 | 144,477 | 53.52 | 0 hrs 27 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,609,758 | 144,224 | 52.76 | 0 hrs 27 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,312,043 | 143,871 | 50.82 | 0 hrs 28 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,177,298 | 135,843 | 45.47 | 0 hrs 32 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,523,364 | 121,571 | 37.21 | 0 hrs 39 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,364,414 | 121,233 | 36.00 | 0 hrs 40 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,783,784 | 114,936 | 32.92 | 0 hrs 44 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,708,397 | 113,668 | 32.62 | 0 hrs 44 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,580,117 | 112,131 | 31.93 | 0 hrs 45 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,287,704 | 110,351 | 29.79 | 0 hrs 48 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,981,243 | 106,966 | 27.87 | 0 hrs 52 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,517,455 | 101,230 | 24.87 | 0 hrs 58 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,412,884 | 120,063 | 20.10 | 1 hrs 12 mins |
| 14 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,841,093 | 85,235 | 21.60 | 1 hrs 7 mins |
| 15 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,782,832 | 88,570 | 20.13 | 1 hrs 12 mins |
| 16 | GeForce RTX 2080 Mobile TU104M [GeForce RTX 2080 Mobile] |
Nvidia | TU104M | 1,769,590 | 90,059 | 19.65 | 1 hrs 13 mins |
| 17 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,236,726 | 78,937 | 15.67 | 1 hrs 32 mins |
| 18 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,201,358 | 78,785 | 15.25 | 1 hrs 34 mins |
| 19 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,155,493 | 77,568 | 14.90 | 1 hrs 37 mins |
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| 20 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,141,564 | 77,605 | 14.71 | 1 hrs 38 mins |
| 21 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,131,107 | 76,820 | 14.72 | 1 hrs 38 mins |
| 22 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,118,537 | 76,806 | 14.56 | 1 hrs 39 mins |
| 23 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 989,496 | 55,184 | 17.93 | 1 hrs 20 mins |
| 24 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 961,529 | 73,450 | 13.09 | 1 hrs 50 mins |
| 25 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 873,342 | 67,974 | 12.85 | 1 hrs 52 mins |
| 26 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 741,256 | 66,702 | 11.11 | 2 hrs 10 mins |
| 27 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 619,465 | 62,458 | 9.92 | 2 hrs 25 mins |
| 28 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 540,840 | 60,467 | 8.94 | 2 hrs 41 mins |
| 29 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 504,664 | 58,923 | 8.56 | 2 hrs 48 mins |
| 30 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 476,694 | 58,261 | 8.18 | 2 hrs 56 mins |
| 31 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 439,740 | 56,281 | 7.81 | 3 hrs 4 mins |
| 32 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 423,480 | 55,383 | 7.65 | 3 hrs 8 mins |
| 33 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 324,535 | 51,019 | 6.36 | 3 hrs 46 mins |
| 34 | Radeon RX 6400 / 6500 XT Navi 24 [Radeon RX 6400 / 6500 XT] |
AMD | Navi 24 | 140,643 | 38,646 | 3.64 | 6 hrs 36 mins |
| 35 | Radeon Pro WX 5100 Ellesmere Pro [Radeon Pro WX 5100] 122 |
AMD | Ellesmere Pro | 138,560 | 37,695 | 3.68 | 6 hrs 32 mins |
| 36 | Radeon Vega Series / Radeon Vega Mobile Series Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series] |
AMD | Raven Ridge | 11,229 | 16,663 | 0.67 | 35 hrs 37 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:39|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|