RESEARCH: CANCER
FOLDING PROJECT #17781 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 90,871Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to how proteins use ion energy to move molecules across cell membranes. These transporters are important for many bodily functions and are even drug targets. Studying them will help us understand how they 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 mechanisms behind a molecule's function.
Molecular basis refers to the underlying structures and interactions within a molecule that determine its behavior and role. In biology, this is crucial for understanding how proteins, DNA, and other molecules work.
Secondary active transporters
Proteins that use an ion gradient to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They work by harnessing the energy stored in an ion concentration gradient to move other molecules across cell membranes. This process is vital for various cellular functions, including nutrient uptake and waste removal.
Ion
An atom or molecule with a net electrical charge.
Ions are atoms or molecules that have gained or lost electrons, giving them a positive or negative charge. They play crucial roles in many biological processes, including nerve impulse transmission and muscle contraction.
Simulations
Computer-based models used to mimic real-world phenomena.
Simulations are powerful tools in scientific research. By creating virtual environments and running experiments within them, scientists can study complex systems like proteins or cellular processes without needing physical lab work.
Drug targets
Molecules or pathways that are potential therapeutic targets.
Drug targets are specific molecules or cellular processes involved in disease development. By understanding these targets, scientists can design drugs that interfere with their function and treat the underlying condition.
Cancer
A group of diseases involving abnormal cell growth and proliferation.
Cancer is a complex group of diseases characterized by uncontrolled cell division. These abnormal cells can invade surrounding tissues and spread to other parts of the body, leading to severe health complications.
Diabetes
A metabolic disorder affecting blood sugar regulation.
Diabetes is a chronic disease that affects how the body regulates blood sugar. It occurs when the pancreas doesn't produce enough insulin (Type 1 diabetes) or when the body becomes resistant to insulin (Type 2 diabetes). This can lead to high blood sugar levels, causing various complications.
Neurological disorders
Conditions affecting the nervous system.
Neurological disorders encompass a wide range of conditions that affect the brain, spinal cord, and nerves. These can include Alzheimer's disease, Parkinson's disease, stroke, epilepsy, and multiple sclerosis.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:27|
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 | 7,037,855 | 145,085 | 48.51 | 0 hrs 30 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,959,801 | 145,933 | 47.69 | 0 hrs 30 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,165,360 | 132,102 | 39.10 | 0 hrs 37 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,616,310 | 128,109 | 36.03 | 0 hrs 40 mins |
| 5 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,123,126 | 124,076 | 33.23 | 0 hrs 43 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,855,347 | 120,480 | 32.00 | 0 hrs 45 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,667,023 | 118,838 | 30.86 | 0 hrs 47 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,059,450 | 111,503 | 27.44 | 0 hrs 52 mins |
| 9 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,684,497 | 107,731 | 24.92 | 0 hrs 58 mins |
| 10 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,575,963 | 103,722 | 24.84 | 0 hrs 58 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,497,751 | 104,073 | 24.00 | 1 hrs 0 mins |
| 12 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,921,351 | 95,622 | 20.09 | 1 hrs 12 mins |
| 13 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,498,217 | 88,436 | 16.94 | 1 hrs 25 mins |
| 14 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,475,644 | 87,885 | 16.79 | 1 hrs 26 mins |
| 15 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,277,022 | 83,474 | 15.30 | 1 hrs 34 mins |
| 16 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,197,830 | 79,479 | 15.07 | 1 hrs 36 mins |
| 17 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,184,373 | 81,215 | 14.58 | 1 hrs 39 mins |
| 18 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,137,098 | 79,837 | 14.24 | 1 hrs 41 mins |
| 19 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,082,293 | 78,909 | 13.72 | 1 hrs 45 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 | 443,575 | 58,333 | 7.60 | 3 hrs 9 mins |
| 21 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 325,645 | 53,284 | 6.11 | 3 hrs 56 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:27|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|