RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17939 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 90,887Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ions to move molecules across cell membranes. These transporters are found everywhere and work the same way, even though they look different. The project relates to understanding how these transporters work across many types of proteins because they're important for treating diseases like cancer and diabetes.
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 cell membranes that facilitate the movement of various substances into and out of cells. They play crucial roles in numerous physiological processes, including nutrient uptake, waste removal, and signaling.
Secondary Active Transporters
Proteins that use an ion gradient to power the transport of other molecules across a membrane.
Secondary active transporters are a type of membrane protein that utilize an existing concentration gradient of ions to drive the movement of other molecules. They couple the downhill flow of ions with the uphill transport of substrates, allowing for efficient transfer against concentration gradients.
Ion Gradient
A difference in concentration of ions across a membrane.
An ion gradient refers to the unequal distribution of charged particles (ions) across a cell membrane. This concentration difference is essential for various cellular processes, including nerve impulse transmission and nutrient uptake.
Drug Targets
Molecules or pathways that are essential for disease progression and can be targeted by drugs.
Drug targets are specific molecules or cellular processes involved in the development or progression of diseases. Identifying these targets allows researchers to develop medications that can effectively inhibit or modulate their activity, leading to therapeutic benefits.
Simulations
Computer-based models used to study biological systems.
Simulations are computer programs that mimic the behavior of complex biological systems. They allow researchers to explore various scenarios and test hypotheses without conducting real-world experiments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:48|
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 | 16,305,134 | 5,996 | 2719.34 | 0 hrs 1 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 15,749,391 | 88,656 | 177.65 | 0 hrs 8 mins |
| 3 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 15,586,139 | 5,996 | 2599.42 | 0 hrs 1 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 8,120,621 | 77,638 | 104.60 | 0 hrs 14 mins |
| 5 | RTX 5000 Ada Generation Laptop GPU AD103GLM [RTX 5000 Ada Generation Laptop GPU] |
Nvidia | AD103GLM | 6,728,595 | 70,360 | 95.63 | 0 hrs 15 mins |
| 6 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,132,552 | 62,390 | 82.27 | 0 hrs 18 mins |
| 7 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,557,917 | 5,996 | 593.38 | 0 hrs 2 mins |
| 8 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 3,016,708 | 53,237 | 56.67 | 0 hrs 25 mins |
| 9 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,708,902 | 51,311 | 52.79 | 0 hrs 27 mins |
| 10 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,397,226 | 49,160 | 48.76 | 0 hrs 30 mins |
| 11 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,229,571 | 47,921 | 46.53 | 0 hrs 31 mins |
| 12 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 2,037,844 | 5,996 | 339.87 | 0 hrs 4 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,771,883 | 45,356 | 39.07 | 0 hrs 37 mins |
| 14 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,715,127 | 52,243 | 32.83 | 0 hrs 44 mins |
| 15 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,514,184 | 43,298 | 34.97 | 0 hrs 41 mins |
| 16 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,463,513 | 42,813 | 34.18 | 0 hrs 42 mins |
| 17 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 957,984 | 21,250 | 45.08 | 0 hrs 32 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:48|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|