RESEARCH: CANCER
FOLDING PROJECT #17784 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 100,981Core: 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 in all living things and are important drug targets for diseases like cancer. This project uses simulations to 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
Molecular basis
The fundamental principles governing how molecules interact.
Molecular basis refers to the underlying mechanisms and interactions between molecules that drive biological processes. In this context, it explores how molecules like proteins and nucleic acids contribute to the function of secondary active transporters.
Secondary active transporters
Membrane proteins that use an ion gradient to transport molecules across cell membranes.
Secondary active transporters are vital proteins found in all living organisms. They utilize the energy stored in an ion gradient to move various molecules across cell membranes against their concentration gradient. This process is essential for nutrient uptake, waste removal, and signaling within cells.
Membrane transporters
Proteins embedded in cell membranes that facilitate the movement of substances across.
Membrane transporters are specialized proteins embedded within cell membranes. They play a crucial role in regulating the passage of molecules into and out of cells, maintaining cellular homeostasis and enabling various biological functions.
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 is essential for various cellular processes, including nerve impulse transmission and nutrient transport.
Drug targets
Molecules or biological pathways that are intended to be modified by drugs to treat diseases.
Drug targets are specific molecules or cellular processes involved in disease development. By targeting these molecules with medications, researchers aim to disrupt disease progression and alleviate symptoms.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:22|
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 | 7,097,395 | 167,211 | 42.45 | 0 hrs 34 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,947,276 | 149,991 | 39.65 | 0 hrs 36 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,220,703 | 150,906 | 34.60 | 0 hrs 42 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,518,235 | 144,182 | 31.34 | 0 hrs 46 mins |
| 5 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,162,845 | 139,725 | 29.79 | 0 hrs 48 mins |
| 6 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,549,930 | 133,560 | 26.58 | 0 hrs 54 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,482,026 | 118,911 | 20.87 | 1 hrs 9 mins |
| 8 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,823,985 | 107,087 | 17.03 | 1 hrs 25 mins |
| 9 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,499,746 | 100,090 | 14.98 | 1 hrs 36 mins |
| 10 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,210,232 | 93,376 | 12.96 | 1 hrs 51 mins |
| 11 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,102,026 | 90,129 | 12.23 | 1 hrs 58 mins |
| 12 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 482,693 | 68,742 | 7.02 | 3 hrs 25 mins |
| 13 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 467,486 | 67,754 | 6.90 | 3 hrs 29 mins |
| 14 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 424,694 | 65,793 | 6.46 | 3 hrs 43 mins |
| 15 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 396,463 | 64,500 | 6.15 | 3 hrs 54 mins |
| 16 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 303,756 | 58,790 | 5.17 | 4 hrs 39 mins |
| 17 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 288,310 | 57,995 | 4.97 | 4 hrs 50 mins |
| 18 | Radeon RX Vega M XT/ M GH [Radeon RX Vega M XT/ M GH] |
AMD | 224,051 | 50,308 | 4.45 | 5 hrs 23 mins | |
| 19 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 128,923 | 44,336 | 2.91 | 8 hrs 15 mins |
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| 20 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 117,694 | 42,719 | 2.76 | 8 hrs 43 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:22|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|