RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17928 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 79,000Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project investigates how membrane transporters work. These proteins help move molecules into and out of cells. The project focuses on sugar transporters that can move different types of sugars. By understanding how these transporters recognize different molecules, researchers can design new drugs that specifically target certain transporters.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Membrane transporters are important for enabling molecules to go in and out of cells.
What is interesting is that while transporters typically have set functions, they can also transport molecules that do not necessarily relate to their function or cellular purpose.
For example, drugs often hijack transporters to enter cells without necessarily resembling the molecules or metabolites which that target transporter normally transports.
The goal of this project is to see how exactly a typical membrane transporter recognizes and transports molecules that look different from one another.
We choose a class of sugar transporters that transports a variety of different types of substrates to satisfy this goal.
Findings from this study can be generalized for the design of molecules (e.g., drugs) specific to a given transporter and its general mechanism.
RELATED TERMS GLOSSARY AI BETA
Membrane transporters
Proteins embedded in cell membranes that facilitate the movement of molecules across.
Membrane transporters are proteins found within the membrane of cells. Their primary role is to control the movement of molecules both into and out of the cell. This process is essential for cellular function as it allows for the uptake of nutrients, removal of waste products, and signaling between cells.
Drugs
Chemical substances used for the diagnosis, treatment, or prevention of disease.
Drugs are chemical compounds designed to have a specific effect on the body. They can be used to treat various conditions like infections, pain, and chronic diseases. Drug development involves complex research to ensure efficacy and safety.
Sugar transporters
Membrane proteins that facilitate the transport of sugar molecules across cell membranes.
Sugar transporters are a specialized type of membrane protein responsible for moving sugar molecules into and out of cells. They play a crucial role in cellular energy production, storage, and signaling pathways.
Substrates
Molecules that bind to an enzyme and undergo a chemical reaction catalyzed by the enzyme.
In biochemistry, substrates are the molecules that enzymes act upon. Enzymes are biological catalysts that speed up chemical reactions. Substrates bind to the active site of an enzyme, where the reaction takes place.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:05|
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 | 15,010,458 | 174,063 | 86.24 | 0 hrs 17 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 12,068,002 | 161,552 | 74.70 | 0 hrs 19 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,440,927 | 149,299 | 63.24 | 0 hrs 23 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 8,810,981 | 145,249 | 60.66 | 0 hrs 24 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 7,023,041 | 15,660 | 448.47 | 0 hrs 3 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,377,164 | 130,702 | 48.79 | 0 hrs 30 mins |
| 7 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 5,559,084 | 126,728 | 43.87 | 0 hrs 33 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 5,157,037 | 122,682 | 42.04 | 0 hrs 34 mins |
| 9 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 3,959,609 | 15,660 | 252.85 | 0 hrs 6 mins |
| 10 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,103,598 | 57,375 | 54.09 | 0 hrs 27 mins |
| 11 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 2,871,738 | 94,235 | 30.47 | 0 hrs 47 mins |
| 12 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,853,038 | 81,125 | 35.17 | 0 hrs 41 mins |
| 13 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,776,243 | 99,769 | 27.83 | 0 hrs 52 mins |
| 14 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,638,543 | 96,917 | 27.22 | 0 hrs 53 mins |
| 15 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,915,861 | 87,955 | 21.78 | 1 hrs 6 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 633,380 | 60,431 | 10.48 | 2 hrs 17 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:05|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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