RESEARCH: CANCER
FOLDING PROJECT #17767 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 187,712Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how proteins called secondary active transporters use ions to move molecules across cell membranes. These transporters are important for many processes in our bodies and are being studied as potential drug targets for diseases like cancer and diabetes.
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 crucial proteins found in all living organisms. They help move various molecules across cell membranes by using the energy stored in ion gradients. This process is vital for many cellular functions and plays a significant role in treating diseases like cancer, diabetes, and neurological disorders.
Ion Gradient
A difference in ion concentration across a cell membrane.
An ion gradient is a difference in the concentration of electrically charged atoms (ions) across a cell membrane. This concentration difference creates potential energy that can be used by cells to power various processes, including the transport of molecules across membranes.
Simulations
Computer models used to study complex systems.
Simulations are computer programs that mimic the behavior of real-world systems. In research, simulations are widely used to study complex biological processes, predict outcomes, and test hypotheses without conducting physical experiments.
Proteins
Large biomolecules essential for all living organisms.
Proteins are complex molecules that play vital roles in all living organisms. They perform a wide range of functions, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes.
Drug Targets
Molecules or pathways that are involved in disease processes and can be targeted by drugs.
Drug targets are specific molecules or cellular pathways involved in the development and progression of diseases. By targeting these molecules with drugs, researchers aim to inhibit or modulate their function and ultimately treat the disease.
Cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a group of diseases characterized by the abnormal and uncontrolled growth of cells. This uncontrolled growth can spread to other parts of the body, damaging tissues and organs.
Diabetes
A group of metabolic disorders characterized by high blood sugar levels.
Diabetes is a group of metabolic disorders that affect how the body regulates blood sugar (glucose). In diabetes, either the body doesn't produce enough insulin, or it can't effectively use the insulin it produces. This leads to high blood sugar levels, which can damage various organs and tissues.
Neurological Disorders
A range of disorders that affect the brain, spinal cord, or nerves.
Neurological disorders encompass a wide range of conditions that affect the brain, spinal cord, and nerves. These disorders can cause a variety of symptoms, such as weakness, numbness, pain, seizures, and cognitive impairment.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:48|
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 | 8,292,527 | 232,014 | 35.74 | 0 hrs 40 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,810,459 | 228,910 | 34.12 | 0 hrs 42 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,100,080 | 216,334 | 32.82 | 0 hrs 44 mins |
| 4 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 6,386,844 | 214,373 | 29.79 | 0 hrs 48 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,033,498 | 197,046 | 25.54 | 0 hrs 56 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,960,069 | 197,061 | 25.17 | 0 hrs 57 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,921,973 | 196,386 | 25.06 | 0 hrs 57 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,823,890 | 195,507 | 24.67 | 0 hrs 58 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,166,844 | 186,387 | 22.36 | 1 hrs 4 mins |
| 10 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,174,381 | 156,121 | 20.33 | 1 hrs 11 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,165,035 | 170,147 | 18.60 | 1 hrs 17 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,654,840 | 160,494 | 16.54 | 1 hrs 27 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,567,872 | 158,496 | 16.20 | 1 hrs 29 mins |
| 14 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,394,743 | 135,974 | 17.61 | 1 hrs 22 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,205,403 | 151,440 | 14.56 | 1 hrs 39 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,646,105 | 136,693 | 12.04 | 1 hrs 60 mins |
| 17 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,512,484 | 133,692 | 11.31 | 2 hrs 7 mins |
| 18 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,436,009 | 130,811 | 10.98 | 2 hrs 11 mins |
| 19 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,221,567 | 95,783 | 12.75 | 1 hrs 53 mins |
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| 20 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,199,752 | 122,972 | 9.76 | 2 hrs 28 mins |
| 21 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,184,073 | 122,807 | 9.64 | 2 hrs 29 mins |
| 22 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,153,565 | 122,877 | 9.39 | 2 hrs 33 mins |
| 23 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 763,910 | 106,536 | 7.17 | 3 hrs 21 mins |
| 24 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 699,337 | 102,890 | 6.80 | 3 hrs 32 mins |
| 25 | Radeon R9 Fury X Fiji XT [Radeon R9 Fury X] |
AMD | Fiji XT | 674,995 | 101,562 | 6.65 | 3 hrs 37 mins |
| 26 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 603,101 | 96,149 | 6.27 | 3 hrs 50 mins |
| 27 | Radeon VII Vega 20 [Radeon VII] 13,284 |
AMD | Vega 20 | 594,837 | 97,691 | 6.09 | 3 hrs 56 mins |
| 28 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 556,757 | 95,485 | 5.83 | 4 hrs 7 mins |
| 29 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 488,202 | 91,158 | 5.36 | 4 hrs 29 mins |
| 30 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 417,839 | 51,648 | 8.09 | 2 hrs 58 mins |
| 31 | Radeon RX 6600/6600 XT/6600M Navi 23 [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 | 375,174 | 84,506 | 4.44 | 5 hrs 24 mins |
| 32 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 353,669 | 82,080 | 4.31 | 5 hrs 34 mins |
| 33 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 285,177 | 76,778 | 3.71 | 6 hrs 28 mins |
| 34 | Radeon RX Vega M XT/ M GH [Radeon RX Vega M XT/ M GH] |
AMD | 241,158 | 68,825 | 3.50 | 6 hrs 51 mins | |
| 35 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 123,897 | 57,789 | 2.14 | 11 hrs 12 mins |
| 36 | Radeon Vega Series / Radeon Vega Mobile Series Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series] |
AMD | Raven Ridge | 62,561 | 46,082 | 1.36 | 17 hrs 41 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:48|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|