RESEARCH: CANCER
FOLDING PROJECT #17789 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 125,216Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. They're found everywhere in life and some are used as drug targets for diseases like cancer. The project relates to understanding how these transporters work across different types of proteins.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
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 move molecules across cell membranes.
Secondary active transporters are essential proteins that help cells move different substances across their membranes. They work by using the energy stored in an ion gradient (like a concentration difference of charged particles) to power the movement of other molecules. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. These transporters are also important drug targets because they play a role in various diseases.
Ion gradient
A difference in concentration of ions across a membrane.
An ion gradient is a difference in the number of charged particles (ions) on either side of a cell membrane. This separation creates a potential energy that can be used to drive various cellular processes, such as transporting molecules against their concentration gradient. Secondary active transporters rely on this stored energy from an ion gradient to power their function.
Simulations
Computer models used to represent and study complex systems.
Simulations are powerful tools used in various fields, including biotechnology and pharmaceuticals, to understand and predict the behavior of complex systems. In drug discovery, simulations can help researchers model how a drug interacts with its target protein and predict its potential efficacy and safety.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:14|
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 | 6,766,391 | 134,853 | 50.18 | 0 hrs 29 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,683,742 | 133,963 | 49.89 | 0 hrs 29 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,555,556 | 130,272 | 42.65 | 0 hrs 34 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,904,819 | 124,891 | 39.27 | 0 hrs 37 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,493,339 | 122,140 | 36.79 | 0 hrs 39 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,068,939 | 117,203 | 34.72 | 0 hrs 41 mins |
| 7 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 3,210,827 | 103,870 | 30.91 | 0 hrs 47 mins |
| 8 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,589,263 | 101,892 | 25.41 | 0 hrs 57 mins |
| 9 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,109,291 | 94,899 | 22.23 | 1 hrs 5 mins |
| 10 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,835,869 | 91,011 | 20.17 | 1 hrs 11 mins |
| 11 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,412,589 | 83,381 | 16.94 | 1 hrs 25 mins |
| 12 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,241,101 | 79,005 | 15.71 | 1 hrs 32 mins |
| 13 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,204,256 | 78,976 | 15.25 | 1 hrs 34 mins |
| 14 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 847,816 | 64,360 | 13.17 | 1 hrs 49 mins |
| 15 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 718,824 | 65,931 | 10.90 | 2 hrs 12 mins |
| 16 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 560,280 | 61,213 | 9.15 | 2 hrs 37 mins |
| 17 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 489,937 | 58,175 | 8.42 | 2 hrs 51 mins |
| 18 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 471,478 | 57,508 | 8.20 | 2 hrs 56 mins |
| 19 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 467,992 | 57,603 | 8.12 | 2 hrs 57 mins |
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| 20 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 445,001 | 56,306 | 7.90 | 3 hrs 2 mins |
| 21 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 420,948 | 55,458 | 7.59 | 3 hrs 10 mins |
| 22 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 344,743 | 51,927 | 6.64 | 3 hrs 37 mins |
| 23 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 334,054 | 51,276 | 6.51 | 3 hrs 41 mins |
| 24 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 253,453 | 46,825 | 5.41 | 4 hrs 26 mins |
| 25 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 142,243 | 38,612 | 3.68 | 6 hrs 31 mins |
| 26 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 67,106 | 29,984 | 2.24 | 10 hrs 43 mins |
| 27 | GeForce GT 710 GK208B [GeForce GT 710] 366 |
Nvidia | GK208B | 11,221 | 16,663 | 0.67 | 35 hrs 38 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:14|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|