RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #19200 PROFILE
PROJECT TEAM
Manager(s): Tanner DeanInstitution: University of Illinois
WORK UNIT INFO
Atoms: 84,576Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to how tiny proteins in cell membranes move things around. They come in three types: uniporters (move one thing), symporters (move two things together), and antiporters (move two things in opposite directions). By comparing similar proteins that work differently, we can learn more about how these transporters control the flow of important stuff like nutrients and medicine across cell membranes.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
A United Picture of Transporter Functions
Membrane transporters are an integral part of the cell and serve many roles across their involvement in the transport of ions, nutrients, neurotransmitters, and many drugs across cellular membranes.
There are three major classes of membrane transport proteins that facilitate the transport of various solutes.
These classes are Uniporters, which transport one solute across the membrane in one direction, Symporters, which transport two solutes across the membrane in the same direction, and Antiporters, which transfer two solutes in opposite directions across the membrane.
These classes follow different mechanisms of transport to move their solute(s) across the membrane.
Simulations for this project will be primarily focused on studying how structural differences, sequence level differences, and energetic level differences account for the difference in transport mechanisms across membrane transporters with high sequence similarity and similar solutes, yet varying classes of transporter.
By evaluating the differences in mechanism of transport arising in similar transporters of different classes, we hope to further improve our understanding of the controlling mechanisms behind membrane transport.
RELATED TERMS GLOSSARY AI BETA
Membrane transporters
Proteins embedded in cell membranes that move substances across.
Membrane transporters are essential proteins found within cell membranes. They act as gatekeepers, selectively allowing specific molecules like nutrients, ions, and drugs to enter or exit the cell. This process is crucial for maintaining cellular balance and function.
Uniporters
Membrane proteins that transport one molecule across the membrane.
Uniporters are a type of membrane transporter protein that move a single molecule across the cell membrane in one direction. This unidirectional transport is essential for processes like nutrient uptake and waste removal.
Symporters
Membrane proteins that transport two molecules across the membrane in the same direction.
Symporters are membrane transporter proteins that facilitate the movement of two different molecules across the cell membrane simultaneously in the same direction. This co-transport mechanism is crucial for processes like nutrient absorption and signal transduction.
Antiporters
Membrane proteins that transport two molecules across the membrane in opposite directions.
Antiporters are membrane transporter proteins that move two different molecules across the cell membrane in opposite directions. This counter-transport mechanism is essential for processes like ion balance and waste elimination.
Structural differences
Variations in the arrangement of atoms within a molecule.
Structural differences refer to variations in the way atoms are arranged within a molecule. These differences can significantly impact a molecule's shape, function, and interactions with other molecules.
Sequence level differences
Variations in the order of amino acids within a protein.
Sequence level differences refer to variations in the order of amino acids that make up a protein. These differences can alter a protein's structure, function, and interactions with other molecules.
Energetic level differences
Variations in the energy required for a process.
Energetic level differences refer to variations in the amount of energy required for a biological process. These differences can influence the rate and efficiency of biochemical reactions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:26:16|
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,680,283 | 113,483 | 67.68 | 0 hrs 21 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,595,330 | 108,678 | 60.69 | 0 hrs 24 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,451,105 | 108,113 | 59.67 | 0 hrs 24 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 4,749,529 | 98,949 | 48.00 | 0 hrs 30 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,721,366 | 97,172 | 48.59 | 0 hrs 30 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,634,491 | 96,552 | 48.00 | 0 hrs 30 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,555,780 | 95,919 | 47.50 | 0 hrs 30 mins |
| 8 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,187,182 | 93,389 | 44.84 | 0 hrs 32 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,753,028 | 89,846 | 41.77 | 0 hrs 34 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,169,232 | 85,237 | 37.18 | 0 hrs 39 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,166,551 | 85,700 | 36.95 | 0 hrs 39 mins |
| 12 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,886,802 | 82,379 | 35.04 | 0 hrs 41 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,554,588 | 100,507 | 25.42 | 0 hrs 57 mins |
| 14 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,478,472 | 78,640 | 31.52 | 0 hrs 46 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,146,765 | 74,872 | 28.67 | 0 hrs 50 mins |
| 16 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,910,273 | 72,195 | 26.46 | 0 hrs 54 mins |
| 17 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,773,762 | 70,252 | 25.25 | 0 hrs 57 mins |
| 18 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,297,417 | 63,173 | 20.54 | 1 hrs 10 mins |
| 19 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,201,676 | 62,149 | 19.34 | 1 hrs 14 mins |
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|||||||
| 20 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,128,142 | 59,058 | 19.10 | 1 hrs 15 mins |
| 21 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,023,766 | 57,151 | 17.91 | 1 hrs 20 mins |
| 22 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 989,934 | 58,433 | 16.94 | 1 hrs 25 mins |
| 23 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 956,674 | 58,311 | 16.41 | 1 hrs 28 mins |
| 24 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 806,018 | 54,725 | 14.73 | 1 hrs 38 mins |
| 25 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 697,440 | 51,376 | 13.58 | 1 hrs 46 mins |
| 26 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 590,740 | 48,372 | 12.21 | 1 hrs 58 mins |
| 27 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 568,932 | 48,305 | 11.78 | 2 hrs 2 mins |
| 28 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 314,979 | 39,444 | 7.99 | 3 hrs 0 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 310,707 | 39,230 | 7.92 | 3 hrs 2 mins |
| 30 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 132,873 | 29,974 | 4.43 | 5 hrs 25 mins |
| 31 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 77,464 | 24,327 | 3.18 | 7 hrs 32 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:26:16|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|