RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17929 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 explores how membrane transporters, which move molecules in and out of cells, recognize different types of substances. Scientists chose sugar transporters to study this because they transport various molecules. Understanding how these transporters work can help design 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 that move molecules across cell membranes.
Membrane transporters are crucial for cells to take in nutrients and expel waste. They act like gatekeepers, selectively allowing specific molecules to pass through the cell membrane.
Drugs
Chemical substances used to treat or prevent diseases.
Drugs are designed to interact with specific targets in the body, such as proteins or enzymes. They can have various effects, such as relieving pain, fighting infections, or controlling symptoms.
Transporters
Proteins that move molecules across cell membranes.
Transporters are essential for cells to maintain their internal environment and function properly. They can transport a variety of molecules, including nutrients, ions, and waste products.
Sugar transporters
Proteins that specifically transport sugar molecules across cell membranes.
Sugar transporters are responsible for taking up glucose and other sugars from the environment into cells. They play a crucial role in energy metabolism.
Substrates
Molecules that are acted upon by an enzyme.
Substrates are the inputs for a chemical reaction catalyzed by an enzyme. They bind to the enzyme's active site and undergo a transformation.
Molecules
Atoms or groups of atoms held together by chemical bonds.
Molecules are the building blocks of all matter. They can be simple, like water (H2O), or complex, like DNA.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:03|
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 | 18,364,531 | 379,615 | 48.38 | 0 hrs 30 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 13,889,332 | 348,983 | 39.80 | 0 hrs 36 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 12,353,787 | 335,017 | 36.88 | 0 hrs 39 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 11,226,620 | 254,024 | 44.20 | 0 hrs 33 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 10,601,474 | 317,995 | 33.34 | 0 hrs 43 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 10,194,513 | 315,718 | 32.29 | 0 hrs 45 mins |
| 7 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,121,859 | 239,394 | 33.93 | 0 hrs 42 mins |
| 8 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 6,644,630 | 45,711 | 145.36 | 0 hrs 10 mins |
| 9 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,865,618 | 260,857 | 22.49 | 1 hrs 4 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,703,688 | 259,350 | 21.99 | 1 hrs 5 mins |
| 11 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,660,963 | 257,113 | 22.02 | 1 hrs 5 mins |
| 12 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 5,212,197 | 45,711 | 114.03 | 0 hrs 13 mins |
| 13 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,046,999 | 248,607 | 20.30 | 1 hrs 11 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,162,468 | 232,260 | 17.92 | 1 hrs 20 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 4,107,398 | 233,599 | 17.58 | 1 hrs 22 mins |
| 16 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,983,051 | 45,711 | 65.26 | 0 hrs 22 mins |
| 17 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,956,433 | 208,304 | 14.19 | 1 hrs 41 mins |
| 18 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,603,250 | 184,730 | 14.09 | 1 hrs 42 mins |
| 19 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 2,578,554 | 141,667 | 18.20 | 1 hrs 19 mins |
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| 20 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,390,631 | 194,779 | 12.27 | 1 hrs 57 mins |
| 21 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,892,670 | 181,108 | 10.45 | 2 hrs 18 mins |
| 22 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 818,386 | 136,977 | 5.97 | 4 hrs 1 mins |
| 23 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 369,309 | 81,944 | 4.51 | 5 hrs 20 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:03|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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