RESEARCH: CANCER
FOLDING PROJECT #17788 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 131,328Core: 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 and help transport many things, including drugs. The project uses simulations to understand how these transporters work in 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 found in all living organisms. They help move various molecules across cell membranes by utilizing the energy stored in an existing ion gradient. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. Disruptions in these transporters can lead to various diseases, making them important drug targets.
membrane transporters
Proteins that facilitate the movement of substances across cell membranes.
Membrane transporters are specialized proteins embedded within cell membranes. They play a vital role in regulating the flow of molecules into and out of cells, ensuring proper cellular function. Different types of transporters move specific molecules, such as nutrients, ions, or waste products, through various mechanisms.
ion gradient
A difference in ion concentration across a membrane.
An ion gradient refers to an uneven distribution of charged ions (like sodium or potassium) across a cell membrane. This concentration difference creates a potential energy that can be harnessed for various cellular processes. For example, nerve cells utilize ion gradients to transmit electrical signals.
drug targets
Molecules or pathways that are targeted by drugs to treat diseases.
Drug targets are specific molecules or biological pathways involved in disease development. By targeting these molecules, drugs can either inhibit their function (for example, blocking a harmful enzyme) or enhance their activity (for example, stimulating a beneficial protein). Identifying effective drug targets is crucial for developing new therapies.
simulations
Computer models used to study biological systems.
Simulations are powerful tools used in computational biology to model and analyze complex biological processes. By creating virtual representations of cells, molecules, or entire organisms, researchers can investigate how these systems behave under different conditions. Simulations can help accelerate drug discovery by predicting the effects of potential drugs on target molecules.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:16|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,696,035 | 155,695 | 49.43 | 0 hrs 29 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,445,409 | 148,735 | 50.06 | 0 hrs 29 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,243,279 | 149,634 | 48.41 | 0 hrs 30 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,631,167 | 132,339 | 34.99 | 0 hrs 41 mins |
| 5 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,042,531 | 126,329 | 32.00 | 0 hrs 45 mins |
| 6 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,351,934 | 118,287 | 28.34 | 0 hrs 51 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,552,949 | 109,088 | 23.40 | 1 hrs 2 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 2,486,870 | 62,926 | 39.52 | 0 hrs 36 mins |
| 9 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,169,700 | 101,694 | 21.34 | 1 hrs 7 mins |
| 10 | GeForce RTX 2080 Mobile TU104M [GeForce RTX 2080 Mobile] |
Nvidia | TU104M | 2,082,426 | 102,418 | 20.33 | 1 hrs 11 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,901,322 | 99,027 | 19.20 | 1 hrs 15 mins |
| 12 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,539,086 | 92,326 | 16.67 | 1 hrs 26 mins |
| 13 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,363,310 | 87,138 | 15.65 | 1 hrs 32 mins |
| 14 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,292,373 | 86,756 | 14.90 | 1 hrs 37 mins |
| 15 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,201,118 | 84,784 | 14.17 | 1 hrs 42 mins |
| 16 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,187,750 | 84,537 | 14.05 | 1 hrs 42 mins |
| 17 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,036,881 | 80,702 | 12.85 | 1 hrs 52 mins |
| 18 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 843,880 | 72,258 | 11.68 | 2 hrs 3 mins |
| 19 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 609,716 | 66,990 | 9.10 | 2 hrs 38 mins |
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| 20 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 469,957 | 61,823 | 7.60 | 3 hrs 9 mins |
| 21 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 427,456 | 60,002 | 7.12 | 3 hrs 22 mins |
| 22 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 308,833 | 53,795 | 5.74 | 4 hrs 11 mins |
| 23 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 284,266 | 47,604 | 5.97 | 4 hrs 1 mins |
| 24 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 121,394 | 39,515 | 3.07 | 7 hrs 49 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:16|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|