RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17930 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 looks at how sugar transporters work. These proteins move different types of sugars into and out of cells. The researchers want to understand how these transporters recognize and move such diverse molecules. This could help us 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 essential proteins found in cell membranes. They act like gates, controlling the movement of various substances, such as nutrients and waste products, into and out of cells. This process is crucial for maintaining cellular function and homeostasis.
Drugs
Substances used to treat, cure, or prevent diseases.
Drugs are chemical substances designed to have a specific effect on the body. They can be used to treat a wide range of conditions, from infections to chronic diseases. Pharmacists and researchers work together to develop new drugs that are safe and effective.
Molecules
Smallest unit of a compound that retains its chemical properties.
Molecules are the building blocks of all matter. They are formed when two or more atoms bond together. Different types of molecules have different properties and functions. For example, water is a molecule made up of two hydrogen atoms and one oxygen atom.
Cellular purpose
The specific role or function that a cell performs within an organism.
Every cell in an organism has a specific job to do. This is known as its cellular purpose. For example, some cells are responsible for transporting nutrients, while others are involved in fighting infection.
Substrates
Substances that are acted upon by an enzyme.
Substrates are the molecules that enzymes work on. Enzymes are biological catalysts that speed up chemical reactions. The substrate binds to the active site of the enzyme, and the enzyme then catalyzes a reaction, transforming the substrate into a product.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:02|
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 | 18,554,914 | 267,809 | 69.28 | 0 hrs 21 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 15,122,728 | 48,980 | 308.75 | 0 hrs 5 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 14,804,817 | 285,960 | 51.77 | 0 hrs 28 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 12,252,107 | 351,275 | 34.88 | 0 hrs 41 mins |
| 5 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 11,454,467 | 342,711 | 33.42 | 0 hrs 43 mins |
| 6 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 10,913,263 | 337,048 | 32.38 | 0 hrs 44 mins |
| 7 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 9,925,874 | 325,700 | 30.48 | 0 hrs 47 mins |
| 8 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 7,837,609 | 48,980 | 160.02 | 0 hrs 9 mins |
| 9 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 7,770,669 | 299,316 | 25.96 | 0 hrs 55 mins |
| 10 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,937,347 | 275,396 | 21.56 | 1 hrs 7 mins |
| 11 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,717,826 | 272,910 | 20.95 | 1 hrs 9 mins |
| 12 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 5,278,225 | 262,426 | 20.11 | 1 hrs 12 mins |
| 13 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 4,488,368 | 250,799 | 17.90 | 1 hrs 20 mins |
| 14 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,061,437 | 244,936 | 16.58 | 1 hrs 27 mins |
| 15 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,275,319 | 224,994 | 14.56 | 1 hrs 39 mins |
| 16 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,210,103 | 63,947 | 50.20 | 0 hrs 29 mins |
| 17 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,818,924 | 215,076 | 13.11 | 1 hrs 50 mins |
| 18 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,682,668 | 182,720 | 9.21 | 2 hrs 36 mins |
| 19 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 474,458 | 62,959 | 7.54 | 3 hrs 11 mins |
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| 20 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 364,455 | 48,980 | 7.44 | 3 hrs 14 mins |
| 21 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 238,481 | 90,066 | 2.65 | 9 hrs 4 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:02|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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