RESEARCH: CANCER
FOLDING PROJECT #17772 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 131,306Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins use ion gradients to move molecules across cell membranes. These proteins are found everywhere and are important for health. Simulations will help us understand how they work, which could lead to new treatments for diseases like cancer and diabetes.
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 ions to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They utilize an existing ion gradient to power the movement of other molecules across cell membranes. These transporters play a crucial role in various biological processes, including nutrient absorption, waste removal, and signal transduction. Many secondary active transporters are also drug targets for treating diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient refers to an unequal distribution of ions (electrically charged atoms) between the inside and outside of a cell. This difference in ion concentration creates an electrochemical potential that drives various cellular processes, including nerve impulse transmission, muscle contraction, and nutrient uptake.
Drug targets
Molecules or cellular pathways that can be modified to treat diseases.
Drug targets are biological molecules or processes that play a role in the development or progression of a disease. By inhibiting or activating these targets, drugs can aim to modulate disease-causing pathways and restore normal cellular function.
Simulations
Computer models that mimic biological processes.
Simulations are powerful tools used in computational biology to study complex biological systems. By creating virtual models of cells, tissues, or entire organisms, researchers can investigate how different factors influence biological phenomena and test hypotheses without performing physical experiments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:40|
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 | 7,842,944 | 157,388 | 49.83 | 0 hrs 29 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,811,881 | 149,098 | 45.69 | 0 hrs 32 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,097,662 | 142,691 | 42.73 | 0 hrs 34 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,376,692 | 138,388 | 38.85 | 0 hrs 37 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,856,154 | 134,893 | 36.00 | 0 hrs 40 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,527,820 | 131,013 | 34.56 | 0 hrs 42 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,401,924 | 130,158 | 33.82 | 0 hrs 43 mins |
| 8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 3,847,607 | 124,691 | 30.86 | 0 hrs 47 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,473,599 | 119,792 | 29.00 | 0 hrs 50 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,299,563 | 118,387 | 27.87 | 0 hrs 52 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,236,631 | 117,699 | 27.50 | 0 hrs 52 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,042,638 | 116,211 | 26.18 | 0 hrs 55 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,511,286 | 108,496 | 23.15 | 1 hrs 2 mins |
| 14 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,496,319 | 108,324 | 23.04 | 1 hrs 2 mins |
| 15 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 2,145,620 | 102,628 | 20.91 | 1 hrs 9 mins |
| 16 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,141,740 | 115,666 | 18.52 | 1 hrs 18 mins |
| 17 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,608,990 | 93,113 | 17.28 | 1 hrs 23 mins |
| 18 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,204,169 | 85,016 | 14.16 | 1 hrs 42 mins |
| 19 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,177,196 | 84,014 | 14.01 | 1 hrs 43 mins |
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| 20 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,169,755 | 83,633 | 13.99 | 1 hrs 43 mins |
| 21 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,006,107 | 79,405 | 12.67 | 1 hrs 54 mins |
| 22 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 955,045 | 77,987 | 12.25 | 1 hrs 58 mins |
| 23 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 949,229 | 77,223 | 12.29 | 1 hrs 57 mins |
| 24 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 880,945 | 73,025 | 12.06 | 1 hrs 59 mins |
| 25 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 583,589 | 66,750 | 8.74 | 2 hrs 45 mins |
| 26 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 582,563 | 66,741 | 8.73 | 2 hrs 45 mins |
| 27 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 491,835 | 62,703 | 7.84 | 3 hrs 4 mins |
| 28 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 460,139 | 61,351 | 7.50 | 3 hrs 12 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 309,246 | 53,820 | 5.75 | 4 hrs 11 mins |
| 30 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 209,999 | 47,839 | 4.39 | 5 hrs 28 mins |
| 31 | Radeon RX 6400 / 6500 XT Navi 24 [Radeon RX 6400 / 6500 XT] |
AMD | Navi 24 | 135,183 | 41,017 | 3.30 | 7 hrs 17 mins |
| 32 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 134,761 | 40,730 | 3.31 | 7 hrs 15 mins |
| 33 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 120,025 | 39,394 | 3.05 | 7 hrs 53 mins |
| 34 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 98,825 | 36,675 | 2.69 | 8 hrs 54 mins |
| 35 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 69,915 | 32,922 | 2.12 | 11 hrs 18 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:40|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|