RESEARCH: CANCER
FOLDING PROJECT #17770 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 143,182Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins use ion power to move molecules across cell membranes. These proteins are important for many processes in the body and are even drug targets for diseases like cancer and diabetes. By simulating these proteins, researchers can learn more about how they work and potentially develop new treatments.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17745-17750 Molecular basis of secondary active transporters. Secondary active membrane transporters are proteins that utilize ions to transport an assortment of molecules across cell membranes.
These proteins are found in all domains in life and surprisingly, despite vastly different structures, operate under the same mechanism by using an ion gradient to assist in small molecule transport.
Furthermore, many of these secondary active transporters are drug targets to treat diseases like cancer, diabetes, and neurological disorders.
The simulations in this project will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
Secondary active transporters
Proteins that use ion gradients to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They work by utilizing the energy stored in an ion gradient to move other molecules across cell membranes. This process is crucial for various cellular functions, including nutrient uptake, waste removal, and signal transduction. These transporters are also important drug targets because they play a role in diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a membrane.
An ion gradient refers to the unequal distribution of ions (electrically charged atoms) across a cell membrane. This difference in concentration creates an electrochemical potential that can be used by cells to perform various functions, such as transporting molecules and generating electrical signals. The movement of ions across membranes is crucial for maintaining cellular homeostasis.
Proteins
Large, complex molecules that perform a wide variety of functions in living organisms.
Proteins are essential macromolecules found in all living organisms. They play diverse roles, including catalyzing biochemical reactions (enzymes), transporting molecules, providing structural support, and transmitting signals. Proteins are made up of chains of amino acids, folded into complex three-dimensional structures that determine their function.
Cell membranes
Thin, flexible barriers that surround cells and regulate the passage of molecules in and out.
Cell membranes are crucial components of all living cells. They act as selective barriers, controlling the movement of substances into and out of the cell. This regulation is essential for maintaining cellular homeostasis and carrying out various functions. Cell membranes are composed primarily of lipids and proteins.
Drug targets
Molecules or pathways that are involved in disease processes and can be targeted by drugs.
Drug targets are specific molecules or biological pathways that play a role in the development or progression of diseases. By targeting these molecules with drugs, researchers aim to disrupt the disease process and alleviate symptoms. Drug targets can include proteins, enzymes, receptors, or genes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:43|
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 | 8,114,864 | 172,430 | 47.06 | 0 hrs 31 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,434,530 | 172,095 | 43.20 | 0 hrs 33 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,428,338 | 167,367 | 44.38 | 0 hrs 32 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,917,046 | 157,514 | 37.57 | 0 hrs 38 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,933,516 | 148,462 | 33.23 | 0 hrs 43 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,587,135 | 144,903 | 31.66 | 0 hrs 45 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,499,463 | 144,463 | 31.15 | 0 hrs 46 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,391,262 | 142,504 | 30.82 | 0 hrs 47 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,740,787 | 135,385 | 27.63 | 0 hrs 52 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,127,587 | 127,569 | 24.52 | 0 hrs 59 mins |
| 11 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 3,059,955 | 127,498 | 24.00 | 0 hrs 60 mins |
| 12 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,841,116 | 123,087 | 23.08 | 1 hrs 2 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,320,128 | 111,391 | 20.83 | 1 hrs 9 mins |
| 14 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,219,006 | 113,632 | 19.53 | 1 hrs 14 mins |
| 15 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,848,581 | 110,175 | 16.78 | 1 hrs 26 mins |
| 16 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,633,362 | 103,209 | 15.83 | 1 hrs 31 mins |
| 17 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,378,666 | 97,336 | 14.16 | 1 hrs 42 mins |
| 18 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,281,529 | 94,928 | 13.50 | 1 hrs 47 mins |
| 19 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,213,234 | 93,564 | 12.97 | 1 hrs 51 mins |
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| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,174,779 | 91,870 | 12.79 | 1 hrs 53 mins |
| 21 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,152,546 | 91,593 | 12.58 | 1 hrs 54 mins |
| 22 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,133,853 | 90,534 | 12.52 | 1 hrs 55 mins |
| 23 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 863,439 | 80,785 | 10.69 | 2 hrs 15 mins |
| 24 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 666,762 | 76,310 | 8.74 | 2 hrs 45 mins |
| 25 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 660,255 | 76,006 | 8.69 | 2 hrs 46 mins |
| 26 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 580,490 | 73,338 | 7.92 | 3 hrs 2 mins |
| 27 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 425,867 | 65,784 | 6.47 | 3 hrs 42 mins |
| 28 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 420,359 | 65,759 | 6.39 | 3 hrs 45 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 326,000 | 60,068 | 5.43 | 4 hrs 25 mins |
| 30 | Radeon RX Vega M XT/ M GH [Radeon RX Vega M XT/ M GH] |
AMD | 259,394 | 54,931 | 4.72 | 5 hrs 5 mins | |
| 31 | GeForce GTX 750 GM107 [GeForce GTX 750] 1111 |
Nvidia | GM107 | 109,362 | 41,836 | 2.61 | 9 hrs 11 mins |
| 32 | FirePro W4100 Cape Verde GL [FirePro W4100] |
AMD | Cape Verde GL | 17,737 | 21,426 | 0.83 | 28 hrs 59 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:43|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|