RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17943 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 125,196Core: 0x23
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. These transporters are important for many biological processes and are targets for drugs treating diseases like cancer and diabetes. This project uses simulations to understand how these proteins work across different types of organisms.
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
Transporters
Proteins that move molecules across cell membranes.
Transporters are essential proteins found in all living cells. They play a crucial role in moving various molecules, such as nutrients, ions, and waste products, across the cell membrane. This process is vital for maintaining cellular homeostasis and function.
Secondary Active Transporters
Proteins that use ion gradients to transport molecules across cell membranes.
Secondary active transporters are a type of membrane protein that rely on the energy stored in an existing ion gradient to move other molecules across the cell membrane. They play a vital role in various cellular processes, including 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 (charged particles) across a cell membrane. This concentration difference is essential for various cellular processes, including nerve impulse transmission, muscle contraction, and nutrient transport.
Simulations
Computer models used to study biological systems.
Simulations are computer programs that mimic the behavior of real-world systems. In computational biology, simulations are used to study complex biological processes, such as protein folding, drug interactions, and disease progression.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:42|
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 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 16,354,908 | 9,825 | 1664.62 | 0 hrs 1 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,153,622 | 9,825 | 1644.13 | 0 hrs 1 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 14,261,463 | 123,666 | 115.32 | 0 hrs 12 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 9,249,024 | 106,255 | 87.05 | 0 hrs 17 mins |
| 5 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 6,667,341 | 97,836 | 68.15 | 0 hrs 21 mins |
| 6 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 5,625,727 | 9,825 | 572.59 | 0 hrs 3 mins |
| 7 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,128,948 | 74,647 | 41.92 | 0 hrs 34 mins |
| 8 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,011,231 | 9,825 | 306.49 | 0 hrs 5 mins |
| 9 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,463,746 | 48,616 | 50.68 | 0 hrs 28 mins |
| 10 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,338,099 | 68,196 | 34.28 | 0 hrs 42 mins |
| 11 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,078,357 | 9,825 | 211.54 | 0 hrs 7 mins |
| 12 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,562,740 | 60,460 | 25.85 | 0 hrs 56 mins |
| 13 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,252,555 | 55,803 | 22.45 | 1 hrs 4 mins |
| 14 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 848,721 | 47,916 | 17.71 | 1 hrs 21 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 430,368 | 34,847 | 12.35 | 1 hrs 57 mins |
| 16 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 15,492 | 9,825 | 1.58 | 15 hrs 13 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:42|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|