RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17927 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 studies how sugar transporters work. They usually move sugar molecules, but can also move other things like drugs. By figuring out how they recognize different molecules, scientists hope to design better drugs that target specific 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 the membrane.
Membrane transporters are essential proteins found in cell membranes. They act like gateways, allowing specific molecules to enter or exit the cell. This is crucial for many cellular processes, including nutrient uptake, waste removal, and signaling.
Drugs
Substances used for the diagnosis, treatment, or prevention of disease.
Drugs are chemical substances that have a biological effect on the body. They can be used to treat illnesses, manage symptoms, prevent diseases, and even diagnose certain conditions. The development and use of drugs is a complex process involving extensive research and testing.
Substrates
Molecules that bind to an enzyme and undergo a chemical transformation.
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.
Transporters
Membrane proteins that facilitate the movement of molecules across cell membranes.
Transporters are essential proteins found in cell membranes. They play a vital role in regulating the flow of molecules into and out of cells. This process is crucial for maintaining cellular homeostasis and carrying out various biological functions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:06|
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 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,939,763 | 14,690 | 1221.22 | 0 hrs 1 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 11,459,783 | 151,283 | 75.75 | 0 hrs 19 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,285,164 | 142,334 | 65.24 | 0 hrs 22 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 9,221,576 | 141,972 | 64.95 | 0 hrs 22 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 6,400,856 | 14,690 | 435.73 | 0 hrs 3 mins |
| 6 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,055,398 | 121,523 | 49.83 | 0 hrs 29 mins |
| 7 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,539,244 | 119,059 | 46.53 | 0 hrs 31 mins |
| 8 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 4,977,383 | 116,830 | 42.60 | 0 hrs 34 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,261,603 | 108,946 | 39.12 | 0 hrs 37 mins |
| 10 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 3,438,333 | 14,690 | 234.06 | 0 hrs 6 mins |
| 11 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,073,941 | 99,043 | 31.04 | 0 hrs 46 mins |
| 12 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 3,047,791 | 98,938 | 30.81 | 0 hrs 47 mins |
| 13 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,030,188 | 99,200 | 30.55 | 0 hrs 47 mins |
| 14 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,833,288 | 54,176 | 52.30 | 0 hrs 28 mins |
| 15 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,622,553 | 95,090 | 27.58 | 0 hrs 52 mins |
| 16 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,423,029 | 54,791 | 44.22 | 0 hrs 33 mins |
| 17 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,191,157 | 89,010 | 24.62 | 0 hrs 58 mins |
| 18 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 1,617,669 | 79,291 | 20.40 | 1 hrs 11 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:06|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|