RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17941 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 94,854Core: 0x23
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
The project relates to proteins called secondary active transporters that use ion power to move molecules across cell membranes. These proteins are found everywhere in nature and help with many important processes, including treating diseases like cancer and diabetes. Simulations will help us understand how these transporters work in different types of proteins.
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
Molecular basis
Fundamental mechanisms at a molecular level.
This refers to the underlying processes and interactions at the molecular level that determine how something functions. In this case, it's looking at the specific molecules and their relationships involved in how secondary active transporters work.
Secondary active transporters
Membrane proteins that use an ion gradient to transport molecules across cell membranes.
These are specialized proteins found in cell membranes. They help move various substances (like nutrients or drugs) across the membrane by using the energy stored in an ion concentration difference. This is like a 'coupled transport' system, where one molecule's movement helps power another.
Ion gradient
Difference in concentration of charged particles (ions) across a membrane.
Think of it like this: imagine two sides of a barrier (the cell membrane). On one side, there are more positively charged particles than the other. This difference in concentration is called an ion gradient and it's a form of stored energy that cells can use.
Drug targets
Molecules or processes that are involved in a disease and can be targeted by drugs.
When scientists are developing new medications, they look for specific molecules or pathways (like secondary active transporters) that are essential to the development or progression of a disease. These 'drug targets' become the focus of research because if we can block or manipulate them, we can potentially treat the illness.
Simulations
Computer models used to study complex biological processes.
Simulations are like virtual experiments. Scientists use computer programs to create models of biological systems (like a cell or a protein) and then run them on computers. This allows them to explore how things work in a controlled environment and test different hypotheses without needing to conduct real-life experiments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:45|
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 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 18,280,102 | 100,138 | 182.55 | 0 hrs 8 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 14,632,467 | 65,026 | 225.02 | 0 hrs 6 mins |
| 3 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,670,215 | 6,417 | 1662.80 | 0 hrs 1 mins |
| 4 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 9,960,466 | 82,242 | 121.11 | 0 hrs 12 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,846,462 | 83,089 | 118.51 | 0 hrs 12 mins |
| 6 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 8,872,645 | 78,245 | 113.40 | 0 hrs 13 mins |
| 7 | RTX 5000 Ada Generation Laptop GPU AD103GLM [RTX 5000 Ada Generation Laptop GPU] |
Nvidia | AD103GLM | 6,548,496 | 71,876 | 91.11 | 0 hrs 16 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,119,034 | 66,356 | 77.15 | 0 hrs 19 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,568,828 | 58,916 | 60.57 | 0 hrs 24 mins |
| 10 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,811,101 | 6,417 | 438.07 | 0 hrs 3 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,771,865 | 56,428 | 49.12 | 0 hrs 29 mins |
| 12 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,704,668 | 53,101 | 50.93 | 0 hrs 28 mins |
| 13 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,357,634 | 50,339 | 46.84 | 0 hrs 31 mins |
| 14 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,232,410 | 50,898 | 43.86 | 0 hrs 33 mins |
| 15 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,963,658 | 6,417 | 306.01 | 0 hrs 5 mins |
| 16 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,466,095 | 43,315 | 33.85 | 0 hrs 43 mins |
| 17 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,264,976 | 6,417 | 197.13 | 0 hrs 7 mins |
| 18 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,075,651 | 40,417 | 26.61 | 0 hrs 54 mins |
| 19 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 869,626 | 8,859 | 98.16 | 0 hrs 15 mins |
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| 20 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 246,842 | 23,952 | 10.31 | 2 hrs 20 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:45|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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