RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17937 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 187,712Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to special proteins that move stuff across cell walls using energy from ions. These proteins are found everywhere and help treat diseases like cancer and diabetes. Studying them can teach us how different types of proteins work together.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
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 work by using the energy stored in an ion gradient (difference in electrical charge) across a cell membrane to move other molecules against their concentration gradient – meaning from an area of low concentration to an area of high concentration. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are also targets for drug development, as they play roles in various diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in concentration of ions across a cell membrane.
An ion gradient refers to an uneven distribution of electrically charged atoms (ions) across a cell membrane. This difference in charge can be maintained by active transport processes, which use energy to move ions against their concentration gradient. Ion gradients are crucial for many cellular functions, including nerve impulse transmission, muscle contraction, and nutrient uptake.
Membrane Transport
The movement of molecules across cell membranes.
Membrane transport is the process by which substances move into and out of cells. This is essential for maintaining cellular homeostasis – keeping a stable internal environment. There are various types of membrane transport, including passive transport (movement without energy expenditure) and active transport (requiring energy). Membrane transport plays a crucial role in nutrient uptake, waste removal, signal transduction, and cell communication.
Simulations
Computer models used to mimic biological processes.
Simulations are powerful tools in computational biology, allowing scientists to model and study complex biological systems using computer programs. These simulations can be used to understand how molecules interact, predict protein structures, and explore the dynamics of cellular processes. Simulations have become increasingly important in drug discovery, disease modeling, and understanding fundamental biological mechanisms.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:51|
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 | 11,598,418 | 196,986 | 58.88 | 0 hrs 24 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,117,109 | 179,472 | 50.80 | 0 hrs 28 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 9,028,796 | 180,805 | 49.94 | 0 hrs 29 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 8,694,075 | 173,925 | 49.99 | 0 hrs 29 mins |
| 5 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 8,316,542 | 172,748 | 48.14 | 0 hrs 30 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 6,979,098 | 21,070 | 331.23 | 0 hrs 4 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,625,142 | 142,872 | 32.37 | 0 hrs 44 mins |
| 8 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 4,583,098 | 143,042 | 32.04 | 0 hrs 45 mins |
| 9 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 3,652,981 | 21,070 | 173.37 | 0 hrs 8 mins |
| 10 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 3,384,748 | 129,164 | 26.21 | 0 hrs 55 mins |
| 11 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,257,107 | 127,955 | 25.46 | 0 hrs 57 mins |
| 12 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,814,850 | 69,611 | 40.44 | 0 hrs 36 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,311,621 | 21,070 | 109.71 | 0 hrs 13 mins |
| 14 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,105,143 | 95,255 | 22.10 | 1 hrs 5 mins |
| 15 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 2,059,370 | 112,593 | 18.29 | 1 hrs 19 mins |
| 16 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,376,562 | 91,727 | 15.01 | 1 hrs 36 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:51|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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