RESEARCH: CANCER
FOLDING PROJECT #17780 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 92,160Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ions to move molecules across cell membranes. Different types of these transporters exist but work the same way. The project relates to understanding how they use ion gradients for transport across various protein families, which could help develop new drugs.
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
Relating to molecules.
Molecular refers to the level of study that focuses on individual molecules and their interactions. In biology, it often involves understanding the structure, function, and behavior of molecules like DNA, proteins, and carbohydrates.
Transporters
Proteins that move substances across cell membranes.
Transporters are essential proteins found in cell membranes that facilitate the movement of molecules and ions into and out of cells. They play a crucial role in various cellular processes, such as nutrient uptake, waste removal, and signal transduction.
Secondary Active Transporters
A type of transporter that uses an ion gradient to power the transport of another molecule.
Secondary active transporters are a specialized class of membrane proteins that utilize the energy stored in an electrochemical gradient of ions to move other molecules across the cell membrane. This process couples the movement of an ion down its concentration gradient with the movement of a different molecule against its concentration gradient.
Membrane Transporters
Proteins that transport molecules across cell membranes.
Membrane transporters are integral membrane proteins that facilitate the movement of molecules and ions across the lipid bilayer of cell membranes. They play a vital role in various cellular processes, including nutrient uptake, waste removal, signal transduction, and maintaining cellular homeostasis.
Drug Targets
Molecules that can be inhibited or activated by drugs to treat disease.
Drug targets are specific biomolecules, such as proteins, enzymes, or receptors, that are involved in the pathogenesis of diseases. Drugs are designed to interact with these targets and modulate their activity to achieve therapeutic effects.
Cancer
A disease caused by uncontrolled cell growth.
Cancer is a group of diseases characterized by the abnormal growth and spread of cells. These uncontrolled cells can invade surrounding tissues and organs, leading to various health complications.
Diabetes
A disease that affects how the body regulates blood sugar.
Diabetes is a chronic metabolic disorder characterized by high blood sugar levels. This occurs because the body either does not produce enough insulin (Type 1 diabetes) or cannot effectively use the insulin it produces (Type 2 diabetes). Insulin is a hormone that helps regulate blood sugar levels.
Neurological Disorders
Conditions that affect the nervous system.
Neurological disorders encompass a wide range of conditions that affect the brain, spinal cord, and nerves. These disorders can cause various symptoms, including cognitive impairments, movement problems, sensory disturbances, and emotional changes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:28|
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 | 7,570,352 | 154,714 | 48.93 | 0 hrs 29 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,942,448 | 154,831 | 44.84 | 0 hrs 32 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,580,262 | 142,248 | 39.23 | 0 hrs 37 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,741,237 | 135,058 | 35.11 | 0 hrs 41 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,544,784 | 133,271 | 34.10 | 0 hrs 42 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,209,613 | 131,283 | 32.07 | 0 hrs 45 mins |
| 7 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,380,799 | 121,302 | 27.87 | 0 hrs 52 mins |
| 8 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,538,121 | 110,624 | 22.94 | 1 hrs 3 mins |
| 9 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,194,738 | 105,399 | 20.82 | 1 hrs 9 mins |
| 10 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,107,249 | 103,233 | 20.41 | 1 hrs 11 mins |
| 11 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,676,381 | 95,710 | 17.52 | 1 hrs 22 mins |
| 12 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,387,201 | 89,911 | 15.43 | 1 hrs 33 mins |
| 13 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,355,072 | 89,385 | 15.16 | 1 hrs 35 mins |
| 14 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,244,712 | 86,438 | 14.40 | 1 hrs 40 mins |
| 15 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 1,144,906 | 84,328 | 13.58 | 1 hrs 46 mins |
| 16 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,136,026 | 84,787 | 13.40 | 1 hrs 47 mins |
| 17 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 552,217 | 65,919 | 8.38 | 2 hrs 52 mins |
| 18 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 523,153 | 65,188 | 8.03 | 2 hrs 59 mins |
| 19 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 415,449 | 58,001 | 7.16 | 3 hrs 21 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 | 349,278 | 57,195 | 6.11 | 3 hrs 56 mins |
| 21 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 300,172 | 54,241 | 5.53 | 4 hrs 20 mins |
| 22 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 136,188 | 41,589 | 3.27 | 7 hrs 20 mins |
| 23 | GeForce GTX 680 GK104 [GeForce GTX 680] 3250 |
Nvidia | GK104 | 99,453 | 37,686 | 2.64 | 9 hrs 6 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:28|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|