RESEARCH: CANCER
FOLDING PROJECT #17761 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 116,677Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how proteins use ion power to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and are important for things like fighting diseases. By studying them, we can learn about a universal rule of how these proteins work.
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 transport 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 move various molecules across cell membranes. These transporters play a crucial role in many biological processes, including nutrient uptake, waste removal, and signal transduction. Their importance makes them attractive targets for drug development to treat diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient is a key concept in understanding how cells maintain their internal environment and carry out essential functions. It refers to the unequal distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that can be harnessed by proteins like secondary active transporters to move molecules against their concentration gradient.
Cell membrane
A thin layer that surrounds every cell.
The cell membrane is a vital structure that separates the inside of a cell from its external environment. It acts as a selective barrier, controlling the passage of molecules in and out of the cell. This regulation is essential for maintaining cellular homeostasis and allowing cells to interact with their surroundings.
Proteins
Large, complex molecules that perform a variety of functions in living organisms.
Proteins are the workhorses of cells, carrying out a vast array of tasks essential for life. They act as enzymes, catalyzing biochemical reactions; provide structural support; transport molecules; and regulate cellular processes. Their diverse functions make them crucial targets for drug development.
Drug targets
Molecules or biological pathways that are the focus of drug development.
Drug targets are specific molecules or pathways within cells that are implicated in disease processes. Identifying and targeting these pathways with drugs can help to treat or manage a variety of conditions. Drug target discovery is a crucial step in the pharmaceutical research process.
Simulations
Computer models used to mimic biological processes.
Simulations are powerful tools in biotechnology, allowing researchers to study complex biological systems in a virtual environment. They can be used to predict protein interactions, drug binding affinities, and the behavior of cells under different conditions. Simulations provide valuable insights that complement experimental research.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:57|
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 | 7,875,611 | 209,833 | 37.53 | 0 hrs 38 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,876,720 | 198,189 | 34.70 | 0 hrs 42 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,705,417 | 199,179 | 33.67 | 0 hrs 43 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,410,593 | 186,241 | 29.05 | 0 hrs 50 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,754,901 | 178,811 | 26.59 | 0 hrs 54 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,358,695 | 173,480 | 25.13 | 0 hrs 57 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,576,054 | 162,654 | 21.99 | 1 hrs 5 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,034,098 | 154,514 | 19.64 | 1 hrs 13 mins |
| 9 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,622,615 | 147,112 | 17.83 | 1 hrs 21 mins |
| 10 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,833,186 | 129,795 | 14.12 | 1 hrs 42 mins |
| 11 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,534,634 | 122,862 | 12.49 | 1 hrs 55 mins |
| 12 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,438,478 | 101,135 | 14.22 | 1 hrs 41 mins |
| 13 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,296,955 | 116,094 | 11.17 | 2 hrs 9 mins |
| 14 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,230,492 | 114,188 | 10.78 | 2 hrs 14 mins |
| 15 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,205,541 | 113,480 | 10.62 | 2 hrs 16 mins |
| 16 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,146,120 | 111,137 | 10.31 | 2 hrs 20 mins |
| 17 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,120,477 | 108,935 | 10.29 | 2 hrs 20 mins |
| 18 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,110,394 | 110,476 | 10.05 | 2 hrs 23 mins |
| 19 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 745,268 | 96,427 | 7.73 | 3 hrs 6 mins |
|
|
|||||||
| 20 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 655,988 | 92,742 | 7.07 | 3 hrs 24 mins |
| 21 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 564,101 | 88,291 | 6.39 | 3 hrs 45 mins |
| 22 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 462,434 | 82,431 | 5.61 | 4 hrs 17 mins |
| 23 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 410,378 | 79,292 | 5.18 | 4 hrs 38 mins |
| 24 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 304,727 | 71,788 | 4.24 | 5 hrs 39 mins |
| 25 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 302,488 | 71,365 | 4.24 | 5 hrs 40 mins |
| 26 | Radeon RX Vega M XT/ M GH [Radeon RX Vega M XT/ M GH] |
AMD | 235,735 | 63,845 | 3.69 | 6 hrs 30 mins | |
| 27 | Radeon R9 200/300 Series Tonga [Radeon R9 200/300 Series] |
AMD | Tonga | 37,939 | 28,699 | 1.32 | 18 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:57|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|