RESEARCH: CANCER
FOLDING PROJECT #17782 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 110,227Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters use ion gradients to move molecules across cell membranes. This process is vital for life and is being studied to develop new drugs for diseases like cancer, diabetes, and neurological disorders. The project will help us understand 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 ions to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They utilize the energy stored in an ion gradient to move various molecules across cell membranes. This process is crucial for numerous cellular functions and plays a significant role in drug development targeting diseases like cancer, diabetes, and neurological disorders.
ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient refers to the unequal distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including the function of secondary active transporters.
simulations
Computer-based models used to study biological systems.
Simulations are powerful tools used in computational biology to recreate and analyze complex biological processes. By simulating the behavior of molecules and cells, researchers can gain insights into various phenomena, such as drug action or protein interactions.
proteins
Large, complex molecules essential for various biological functions.
Proteins are the workhorses of cells, carrying out a wide range of functions essential for life. They act as enzymes, structural components, signaling molecules, and transporters.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:25|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,054,870 | 187,803 | 37.57 | 0 hrs 38 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,838,516 | 186,148 | 36.74 | 0 hrs 39 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,584,854 | 175,653 | 37.49 | 0 hrs 38 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,445,451 | 171,957 | 31.67 | 0 hrs 45 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,567,421 | 162,062 | 28.18 | 0 hrs 51 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,813,655 | 152,955 | 24.93 | 0 hrs 58 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,751,705 | 151,979 | 24.69 | 0 hrs 58 mins |
| 8 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,644,153 | 136,163 | 19.42 | 1 hrs 14 mins |
| 9 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,471,249 | 132,509 | 18.65 | 1 hrs 17 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,304,135 | 126,214 | 18.26 | 1 hrs 19 mins |
| 11 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 2,086,277 | 125,563 | 16.62 | 1 hrs 27 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,067,929 | 125,243 | 16.51 | 1 hrs 27 mins |
| 13 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,713,299 | 117,965 | 14.52 | 1 hrs 39 mins |
| 14 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,595,445 | 114,942 | 13.88 | 1 hrs 44 mins |
| 15 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,220,037 | 105,169 | 11.60 | 2 hrs 4 mins |
| 16 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,156,294 | 102,699 | 11.26 | 2 hrs 8 mins |
| 17 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,154,683 | 103,568 | 11.15 | 2 hrs 9 mins |
| 18 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 952,261 | 96,160 | 9.90 | 2 hrs 25 mins |
| 19 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 927,159 | 94,859 | 9.77 | 2 hrs 27 mins |
|
|
|||||||
| 20 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 804,865 | 91,662 | 8.78 | 2 hrs 44 mins |
| 21 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 578,981 | 81,637 | 7.09 | 3 hrs 23 mins |
| 22 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 569,032 | 81,451 | 6.99 | 3 hrs 26 mins |
| 23 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 545,548 | 80,321 | 6.79 | 3 hrs 32 mins |
| 24 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 420,143 | 71,839 | 5.85 | 4 hrs 6 mins |
| 25 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 399,337 | 72,332 | 5.52 | 4 hrs 21 mins |
| 26 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 361,765 | 57,047 | 6.34 | 3 hrs 47 mins |
| 27 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 340,046 | 68,571 | 4.96 | 4 hrs 50 mins |
| 28 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 144,329 | 51,471 | 2.80 | 8 hrs 34 mins |
| 29 | Radeon R7 240/HD8570 Hawaii [Radeon R7 240/HD8570] |
AMD | Hawaii | 16,059 | 25,386 | 0.63 | 37 hrs 56 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:25|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|