RESEARCH: CANCER
FOLDING PROJECT #17764 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 92,122Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how proteins use energy from ions to move molecules across cell membranes. These proteins are found everywhere and are important for things like fighting cancer, diabetes, and brain disorders. By studying these proteins, we can learn how they work and maybe find new ways to treat diseases.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17745-17750 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 ions to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They work by utilizing an existing ion gradient to power the movement of other molecules across cell membranes. This process is crucial for various biological functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are also drug targets for treating diseases like cancer, diabetes, and neurological disorders.
Membrane transporters
Proteins that move molecules across cell membranes.
Membrane transporters are proteins embedded within the cell membrane that play a vital role in regulating the movement of substances into and out of the cell. They facilitate the transport of nutrients, ions, waste products, and signaling molecules across this selective barrier. Different types of membrane transporters exist, including passive transporters (which move substances down their concentration gradient) and active transporters (which require energy to move substances against their concentration gradient).
Ion gradient
A difference in ion concentration across a membrane.
An ion gradient is a fundamental concept in cellular biology that describes the unequal distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that can be harnessed by cells for various purposes, such as powering active transport processes, generating nerve impulses, and maintaining cellular pH balance.
Drug targets
Molecules or biological processes that are potential therapeutic targets.
Drug targets are specific molecules or cellular pathways that pharmaceutical companies aim to modulate (activate, inhibit, or modify) in order to treat diseases. These targets can include proteins, enzymes, receptors, DNA sequences, or even entire signaling networks. Identifying and understanding drug targets is crucial for the development of new and effective medications.
Cancer
A disease characterized by uncontrolled cell growth and spread.
Cancer is a group of diseases characterized by the abnormal growth and spread of cells. These cells divide uncontrollably and can invade surrounding tissues and organs. Cancer arises from mutations in DNA that disrupt normal cellular regulatory mechanisms, leading to unchecked proliferation and evasion of cell death. Various factors contribute to cancer development, including genetic predisposition, environmental exposures, and lifestyle choices.
Diabetes
A metabolic disorder characterized by high blood sugar levels.
Diabetes is a chronic metabolic disorder characterized by elevated levels of glucose (sugar) in the bloodstream. This occurs when 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 regulates blood sugar by allowing glucose to enter cells for energy production. Without sufficient insulin, glucose builds up in the bloodstream, leading to various complications such as damage to blood vessels, nerves, and organs.
Neurological disorders
Conditions affecting the nervous system.
Neurological disorders encompass a wide range of conditions that affect the brain, spinal cord, and peripheral nerves. These disorders can manifest as various symptoms, including impaired movement, sensation, cognition, and behavior. Some common neurological disorders include Alzheimer's disease, Parkinson's disease, stroke, epilepsy, multiple sclerosis, and autism spectrum disorder.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:53|
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 | 8,172,780 | 160,528 | 50.91 | 0 hrs 28 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,415,068 | 157,190 | 47.17 | 0 hrs 31 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,216,091 | 153,845 | 46.90 | 0 hrs 31 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,482,261 | 142,647 | 38.43 | 0 hrs 37 mins |
| 5 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,739,967 | 135,196 | 35.06 | 0 hrs 41 mins |
| 6 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,349,115 | 132,508 | 32.82 | 0 hrs 44 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,324,601 | 132,583 | 32.62 | 0 hrs 44 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,926,997 | 127,085 | 30.90 | 0 hrs 47 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,376,562 | 121,150 | 27.87 | 0 hrs 52 mins |
| 10 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,731,355 | 113,806 | 24.00 | 0 hrs 60 mins |
| 11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,713,256 | 108,417 | 25.03 | 0 hrs 58 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,691,698 | 112,154 | 24.00 | 0 hrs 60 mins |
| 13 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,279,217 | 117,813 | 19.35 | 1 hrs 14 mins |
| 14 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,480,923 | 92,585 | 16.00 | 1 hrs 30 mins |
| 15 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,367,708 | 90,231 | 15.16 | 1 hrs 35 mins |
| 16 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,262,683 | 87,686 | 14.40 | 1 hrs 40 mins |
| 17 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,248,564 | 84,171 | 14.83 | 1 hrs 37 mins |
| 18 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,213,755 | 86,607 | 14.01 | 1 hrs 43 mins |
| 19 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,194,560 | 85,710 | 13.94 | 1 hrs 43 mins |
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| 20 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 956,514 | 73,407 | 13.03 | 1 hrs 51 mins |
| 21 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 898,240 | 74,453 | 12.06 | 1 hrs 59 mins |
| 22 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 880,958 | 77,491 | 11.37 | 2 hrs 7 mins |
| 23 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 725,001 | 72,946 | 9.94 | 2 hrs 25 mins |
| 24 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 551,206 | 66,158 | 8.33 | 2 hrs 53 mins |
| 25 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 550,416 | 66,146 | 8.32 | 2 hrs 53 mins |
| 26 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 546,232 | 66,382 | 8.23 | 2 hrs 55 mins |
| 27 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 544,146 | 66,253 | 8.21 | 2 hrs 55 mins |
| 28 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 445,205 | 61,886 | 7.19 | 3 hrs 20 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 312,328 | 54,313 | 5.75 | 4 hrs 10 mins |
| 30 | GeForce GTX 950 GM206 [GeForce GTX 950] 1572 |
Nvidia | GM206 | 221,900 | 48,090 | 4.61 | 5 hrs 12 mins |
| 31 | Radeon HD 7800 Series Pitcairn PRO [Radeon HD 7800 Series] |
AMD | Pitcairn PRO | 101,225 | 37,725 | 2.68 | 8 hrs 57 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:53|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|