RESEARCH: CANCER
FOLDING PROJECT #17792 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 109,778Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to special proteins that move molecules across cell walls using energy from ion gradients. These transporters are important for many functions in living things and can be used as targets for treating diseases like cancer and diabetes. Simulations will help us understand how these proteins work.
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 transport molecules across cell membranes.
Secondary active transporters are a crucial type of protein found in all living organisms. They work by utilizing the energy stored in an ion gradient to move other molecules across cell membranes. This process is essential for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Many of these transporters are also targets for drugs used to treat diseases like cancer, diabetes, and neurological disorders.
Ion Gradient
A difference in ion concentration across a membrane.
An ion gradient is a difference in the concentration of charged particles (ions) on either side of a cell membrane. This difference in concentration creates an electrochemical potential that can be used to drive cellular processes, such as the transport of molecules across the membrane. Ion gradients are essential for maintaining cell function and are often exploited by drugs to target specific biological pathways.
Proteins
Large biomolecules composed of amino acids.
Proteins are essential macromolecules that perform a wide variety of functions in living organisms. They are made up of chains of amino acids folded into complex three-dimensional structures. Proteins play crucial roles in cellular processes such as catalysis, transport, signaling, and structural support.
Cell Membranes
Thin lipid bilayers that enclose cells and regulate the passage of substances.
Cell membranes are essential barriers that separate the internal environment of a cell from its surroundings. They are composed primarily of lipids and proteins arranged in a bilayer structure. Cell membranes play crucial roles in maintaining cellular integrity, regulating transport of molecules, and mediating communication between cells.
Drug Targets
Molecules or cellular processes that are targeted by drugs to treat diseases.
Drug targets are specific molecules or pathways within cells that are involved in the development or progression of diseases. Drugs are designed to interact with these targets and modulate their activity to achieve a therapeutic effect. Identifying effective drug targets is a crucial step in the drug discovery process.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:10|
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 | 7,081,214 | 184,271 | 38.43 | 0 hrs 37 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,845,244 | 181,156 | 37.79 | 0 hrs 38 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,042,119 | 174,346 | 34.66 | 0 hrs 42 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,892,114 | 164,203 | 29.79 | 0 hrs 48 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,166,910 | 155,599 | 26.78 | 0 hrs 54 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,076,630 | 155,704 | 26.18 | 0 hrs 55 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,063,175 | 154,226 | 26.35 | 0 hrs 55 mins |
| 8 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,260,113 | 143,384 | 22.74 | 1 hrs 3 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,056,043 | 141,483 | 21.60 | 1 hrs 7 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,931,901 | 138,946 | 21.10 | 1 hrs 8 mins |
| 11 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,073,692 | 123,831 | 16.75 | 1 hrs 26 mins |
| 12 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,779,733 | 117,413 | 15.16 | 1 hrs 35 mins |
| 13 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,466,851 | 109,005 | 13.46 | 1 hrs 47 mins |
| 14 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,134,451 | 100,760 | 11.26 | 2 hrs 8 mins |
| 15 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,095,141 | 99,486 | 11.01 | 2 hrs 11 mins |
| 16 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,067,813 | 98,871 | 10.80 | 2 hrs 13 mins |
| 17 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 508,400 | 77,077 | 6.60 | 3 hrs 38 mins |
| 18 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 501,485 | 77,118 | 6.50 | 3 hrs 41 mins |
| 19 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 449,128 | 74,630 | 6.02 | 3 hrs 59 mins |
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| 20 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 317,635 | 66,320 | 4.79 | 5 hrs 1 mins |
| 21 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 288,351 | 64,010 | 4.50 | 5 hrs 20 mins |
| 22 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 156,018 | 48,108 | 3.24 | 7 hrs 24 mins |
| 23 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 94,117 | 43,933 | 2.14 | 11 hrs 12 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:10|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|