RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17933 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 108,493Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. This project uses simulations to understand how these important proteins work, as they're used in 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
Secondary active transporters
Proteins that use ion gradients to move molecules across cell membranes.
Secondary active transporters are a type of protein found in all living things. They help move different molecules across the barriers of cells by using energy from an existing concentration gradient of ions (like sodium or potassium). This process is essential for many cellular functions, and malfunctions in these transporters can lead to various diseases.
Ion gradient
A difference in concentration of ions across a membrane.
An ion gradient is a difference in the number of charged particles (ions) on either side of a cell membrane. These gradients are created by specialized proteins that pump ions against their concentration gradient, using energy from processes like cellular respiration. Ion gradients play a crucial role in many cellular functions, including nerve impulse transmission, muscle contraction, and nutrient uptake.
Simulations
Computer models used to mimic biological processes.
Simulations are computer programs that create virtual environments to represent real-world systems, like cells or molecules. These simulations can be used to study complex biological processes, predict the behavior of drugs, and design new therapies.
Drug targets
Molecules or biological pathways involved in disease that can be targeted by drugs.
Drug targets are specific molecules or cellular processes that contribute to the development or progression of a disease. By targeting these molecules with drugs, scientists aim to interrupt disease pathways and achieve therapeutic effects.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:57|
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 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 18,784,495 | 8,750 | 2146.80 | 0 hrs 1 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 18,549,832 | 8,750 | 2119.98 | 0 hrs 1 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 15,977,458 | 106,247 | 150.38 | 0 hrs 10 mins |
| 4 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 11,500,401 | 230,497 | 49.89 | 0 hrs 29 mins |
| 5 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 10,575,099 | 105,264 | 100.46 | 0 hrs 14 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 9,187,173 | 102,253 | 89.85 | 0 hrs 16 mins |
| 7 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 7,029,406 | 90,211 | 77.92 | 0 hrs 18 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,717,093 | 73,137 | 50.82 | 0 hrs 28 mins |
| 9 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 3,018,842 | 69,057 | 43.72 | 0 hrs 33 mins |
| 10 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,972,566 | 68,663 | 43.29 | 0 hrs 33 mins |
| 11 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,833,499 | 8,750 | 323.83 | 0 hrs 4 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,483,812 | 64,072 | 38.77 | 0 hrs 37 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,025,243 | 59,931 | 33.79 | 0 hrs 43 mins |
| 14 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,686,903 | 57,207 | 29.49 | 0 hrs 49 mins |
| 15 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,663,720 | 56,833 | 29.27 | 0 hrs 49 mins |
| 16 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,660,201 | 8,750 | 189.74 | 0 hrs 8 mins |
| 17 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,632,821 | 55,747 | 29.29 | 0 hrs 49 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:57|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|