RESEARCH: CANCER
FOLDING PROJECT #17765 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 90,844Core: 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 super important because they're involved in many diseases and some even make good drug targets. By studying them, we can learn how different types of these proteins work together.
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 move molecules across cell membranes.
Secondary active transporters are crucial proteins found in all living organisms. They work by using the energy from an existing ion gradient to move other molecules across cell membranes. This process is essential for various biological functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are targets for drugs used to treat diseases such as cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a membrane.
An ion gradient is the unequal distribution of ions on either side of a cell membrane. This difference in concentration creates an electrical potential, which can be used by cells to power various processes, such as nerve impulse transmission and muscle contraction. Secondary active transporters utilize these ion gradients to drive the transport of other molecules across the membrane.
Drug targets
Molecules or proteins that are targeted by drugs to treat diseases.
Drug targets are specific molecules or proteins involved in disease pathways. By targeting these molecules, drugs can interfere with the disease process and alleviate symptoms. Secondary active transporters are often drug targets because they play crucial roles in various physiological processes, including nutrient uptake and signal transduction.
Simulations
Computer-based models that mimic real-world processes.
Simulations are powerful tools used in various scientific fields to study complex systems. In biotechnology, simulations can be used to model biological processes, such as protein folding and drug interactions. These simulations can provide valuable insights into the mechanisms underlying disease and guide the development of new treatments.
Proteins
Large biomolecules essential for various cellular functions.
Proteins are complex molecules composed of amino acids. They play a wide range of roles in living organisms, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Understanding the structure and function of proteins is crucial for advancements in biotechnology and medicine.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:51|
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 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,202,229 | 145,981 | 49.34 | 0 hrs 29 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,133,682 | 142,298 | 50.13 | 0 hrs 29 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,109,510 | 138,057 | 44.25 | 0 hrs 33 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,615,517 | 128,208 | 36.00 | 0 hrs 40 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,507,711 | 126,689 | 35.58 | 0 hrs 40 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,244,103 | 124,383 | 34.12 | 0 hrs 42 mins |
| 7 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,182,406 | 123,345 | 33.91 | 0 hrs 42 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,040,166 | 122,638 | 32.94 | 0 hrs 44 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,523,146 | 118,254 | 29.79 | 0 hrs 48 mins |
| 10 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,495,539 | 104,917 | 23.79 | 1 hrs 1 mins |
| 11 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,481,524 | 103,397 | 24.00 | 1 hrs 0 mins |
| 12 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,368,894 | 119,246 | 19.87 | 1 hrs 12 mins |
| 13 | GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB] |
Nvidia | TU106 | 1,922,538 | 95,682 | 20.09 | 1 hrs 12 mins |
| 14 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,791,152 | 93,289 | 19.20 | 1 hrs 15 mins |
| 15 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,782,892 | 93,877 | 18.99 | 1 hrs 16 mins |
| 16 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,628,155 | 89,973 | 18.10 | 1 hrs 20 mins |
| 17 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,537,602 | 88,965 | 17.28 | 1 hrs 23 mins |
| 18 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,494,198 | 88,199 | 16.94 | 1 hrs 25 mins |
| 19 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,201,102 | 82,006 | 14.65 | 1 hrs 38 mins |
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| 20 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,193,572 | 81,405 | 14.66 | 1 hrs 38 mins |
| 21 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,067,178 | 78,695 | 13.56 | 1 hrs 46 mins |
| 22 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,020,564 | 77,301 | 13.20 | 1 hrs 49 mins |
| 23 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 964,176 | 75,570 | 12.76 | 1 hrs 53 mins |
| 24 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 809,686 | 71,908 | 11.26 | 2 hrs 8 mins |
| 25 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 579,070 | 63,290 | 9.15 | 2 hrs 37 mins |
| 26 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 566,226 | 64,202 | 8.82 | 2 hrs 43 mins |
| 27 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 539,534 | 62,694 | 8.61 | 2 hrs 47 mins |
| 28 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 378,746 | 55,704 | 6.80 | 3 hrs 32 mins |
| 29 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 312,855 | 52,344 | 5.98 | 4 hrs 1 mins |
| 30 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 304,886 | 52,137 | 5.85 | 4 hrs 6 mins |
| 31 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 219,659 | 45,620 | 4.81 | 4 hrs 59 mins |
| 32 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 200,626 | 45,280 | 4.43 | 5 hrs 25 mins |
| 33 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 59,528 | 29,970 | 1.99 | 12 hrs 5 mins |
| 34 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 53,660 | 29,056 | 1.85 | 12 hrs 60 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:51|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|