RESEARCH: CANCER
FOLDING PROJECT #17776 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 109,766Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins use ions to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and help with important processes. They're also drug targets for diseases like cancer and diabetes. By simulating these proteins, we can learn how they work the same way despite different structures.
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
Transporters
Proteins that move molecules across cell membranes.
Transporters are essential proteins found in all living organisms. They facilitate the movement of various substances, including nutrients, ions, and waste products, across cell membranes. This process is crucial for maintaining cellular homeostasis and carrying out vital biological functions.
Secondary Active Transporters
Transporters that use an ion gradient to move molecules across cell membranes.
Secondary active transporters are a type of membrane protein that utilize the electrochemical gradient created by ions to drive the transport of other molecules. This process is crucial for various cellular functions, such as nutrient uptake, waste removal, and signal transduction.
Ion Gradient
A difference in ion concentration across a membrane.
An ion gradient refers to the unequal distribution of ions across a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including the movement of nutrients and waste products.
Simulations
Computer models used to study complex systems.
Simulations are powerful tools used in various scientific disciplines to model and analyze complex systems. In computational biology, simulations can be employed to study biological processes, such as protein folding or drug interactions.
Proteins
Large biomolecules essential for life.
Proteins are complex macromolecules that play a vital role in virtually all biological processes. They are responsible for a wide range of functions, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:34|
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 | 7,208,412 | 186,250 | 38.70 | 0 hrs 37 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,155,827 | 184,226 | 38.84 | 0 hrs 37 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,049,563 | 185,790 | 37.94 | 0 hrs 38 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,580,715 | 171,085 | 32.62 | 0 hrs 44 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,478,860 | 159,357 | 28.11 | 0 hrs 51 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,348,560 | 158,488 | 27.44 | 0 hrs 52 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,179,448 | 155,726 | 26.84 | 0 hrs 54 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,157,085 | 154,550 | 26.90 | 0 hrs 54 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,722,535 | 150,797 | 24.69 | 0 hrs 58 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,717,112 | 149,492 | 24.86 | 0 hrs 58 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,472,959 | 131,202 | 18.85 | 1 hrs 16 mins |
| 12 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,660,993 | 113,284 | 14.66 | 1 hrs 38 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,649,542 | 109,022 | 15.13 | 1 hrs 35 mins |
| 14 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,528,134 | 111,520 | 13.70 | 1 hrs 45 mins |
| 15 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,486,240 | 110,657 | 13.43 | 1 hrs 47 mins |
| 16 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,450,539 | 109,678 | 13.23 | 1 hrs 49 mins |
| 17 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,379,037 | 108,113 | 12.76 | 1 hrs 53 mins |
| 18 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,295,433 | 106,024 | 12.22 | 1 hrs 58 mins |
| 19 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,171,448 | 102,231 | 11.46 | 2 hrs 6 mins |
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| 20 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,153,651 | 99,127 | 11.64 | 2 hrs 4 mins |
| 21 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,145,120 | 101,260 | 11.31 | 2 hrs 7 mins |
| 22 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 938,484 | 94,274 | 9.95 | 2 hrs 25 mins |
| 23 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 550,796 | 79,112 | 6.96 | 3 hrs 27 mins |
| 24 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 546,609 | 79,076 | 6.91 | 3 hrs 28 mins |
| 25 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 510,849 | 77,598 | 6.58 | 3 hrs 39 mins |
| 26 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 444,893 | 71,460 | 6.23 | 3 hrs 51 mins |
| 27 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 441,922 | 73,526 | 6.01 | 3 hrs 60 mins |
| 28 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 394,369 | 71,147 | 5.54 | 4 hrs 20 mins |
| 29 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 221,581 | 58,450 | 3.79 | 6 hrs 20 mins |
| 30 | Radeon RX 6400 / 6500 XT Navi 24 [Radeon RX 6400 / 6500 XT] |
AMD | Navi 24 | 126,501 | 48,695 | 2.60 | 9 hrs 14 mins |
| 31 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 111,637 | 46,847 | 2.38 | 10 hrs 4 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|