RESEARCH: CANCER
FOLDING PROJECT #13009 PROFILE
PROJECT TEAM
Manager(s): Lin ZhuInstitution: the University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 60,000Core: 0x24
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project aims to create supercharged fluorescent proteins that can better detect rare earth elements in our bodies. These proteins could help diagnose diseases like cancer and neurodegenerative disorders earlier. It also might lead to new drugs and therapies by targeting specific cells or tissues.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Super Charged Fluorescent protein design Rare earth elements play vital roles in various biological processes, and their detection using fluorescent proteins could advance biomedical research and diagnostics.
By developing more sensitive and specific sensors for rare earth elements, this project could contribute to early detection of diseases, such as cancer and neurodegenerative disorders, where abnormal levels of certain elements are often observed.
Moreover, the enhanced understanding of binding mechanisms between fluorescent proteins and rare earth elements could lead to the development of novel therapeutic agents or drug delivery systems tailored to target specific cellular pathways or tissues, thereby potentially improving treatment outcomes and patient well-being.This project aims to investigate the binding mechanism of supercharged fluorescent proteins for capturing rare earth elements and then design a new variant of fluorescent protein that exhibits stronger binding affinity for specific rare earth elements than existing fluorescent proteins.
In this project, there are 557 double mutants systems of yellow fluorescent protein with a negative charge of 31.
RELATED TERMS GLOSSARY AI BETA
Fluorescent protein
A protein that emits light when exposed to a specific wavelength of light.
Fluorescent proteins are naturally occurring or genetically engineered proteins that glow when excited by light. They are widely used in biological research for visualizing cells and molecules, tracking processes within living organisms, and developing diagnostic tools.
Rare earth elements
A group of 17 metallic elements with unique magnetic, optical, and electronic properties.
Rare earth elements are a group of 17 metals found in the periodic table. They have special properties that make them valuable for various applications, including magnets, lasers, batteries, and electronics.
Biomedical research
Scientific investigation into biological processes and their application to medicine.
Biomedical research is the study of living organisms and their functions to understand diseases and develop new treatments. It involves various disciplines like biology, chemistry, and engineering to improve human health.
Cancer
A disease characterized by uncontrolled cell growth.
Cancer is a group of diseases where abnormal cells grow uncontrollably and can spread to other parts of the body. It is caused by various factors like genetics, lifestyle, and environmental exposure.
Neurodegenerative disorders
A group of diseases characterized by the progressive loss of brain cells and function.
Neurodegenerative disorders are a category of diseases that involve the gradual deterioration of nerve cells in the brain. Examples include Alzheimer's disease, Parkinson's disease, and Huntington's disease.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:33:45|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,224,510 | 84,870 | 61.56 | 0 hrs 23 mins |
| 2 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 3,703,314 | 389,908 | 9.50 | 2 hrs 32 mins |
| 3 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,879,284 | 362,225 | 7.95 | 3 hrs 1 mins |
| 4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,313,419 | 320,326 | 7.22 | 3 hrs 19 mins |
| 5 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,247,348 | 323,827 | 6.94 | 3 hrs 27 mins |
| 6 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 2,048,704 | 319,737 | 6.41 | 3 hrs 45 mins |
| 7 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,035,757 | 160,654 | 12.67 | 1 hrs 54 mins |
| 8 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,018,267 | 174,087 | 11.59 | 2 hrs 4 mins |
| 9 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,739,676 | 156,793 | 11.10 | 2 hrs 10 mins |
| 10 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,667,846 | 307,253 | 5.43 | 4 hrs 25 mins |
| 11 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,412,536 | 289,523 | 4.88 | 4 hrs 55 mins |
| 12 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,314,705 | 276,221 | 4.76 | 5 hrs 3 mins |
| 13 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,192,041 | 264,944 | 4.50 | 5 hrs 20 mins |
| 14 | Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics] |
Intel | Battlemage G21 | 1,112,661 | 262,792 | 4.23 | 5 hrs 40 mins |
| 15 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,086,929 | 258,773 | 4.20 | 5 hrs 43 mins |
| 16 | GeForce GTX 1070 Mobile GP104M [GeForce GTX 1070 Mobile] |
Nvidia | GP104M | 987,384 | 84,870 | 11.63 | 2 hrs 4 mins |
| 17 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 787,717 | 90,329 | 8.72 | 2 hrs 45 mins |
| 18 | GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107M | 751,703 | 84,870 | 8.86 | 2 hrs 43 mins |
| 19 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 739,452 | 226,811 | 3.26 | 7 hrs 22 mins |
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| 20 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 704,421 | 223,484 | 3.15 | 7 hrs 37 mins |
| 21 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 677,816 | 84,870 | 7.99 | 3 hrs 0 mins |
| 22 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 591,564 | 84,870 | 6.97 | 3 hrs 27 mins |
| 23 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 503,721 | 150,597 | 3.34 | 7 hrs 11 mins |
| 24 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 502,957 | 212,027 | 2.37 | 10 hrs 7 mins |
| 25 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 478,446 | 84,870 | 5.64 | 4 hrs 15 mins |
| 26 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 471,733 | 213,613 | 2.21 | 10 hrs 52 mins |
| 27 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 281,779 | 84,870 | 3.32 | 7 hrs 14 mins |
| 28 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 226,272 | 84,870 | 2.67 | 9 hrs 0 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 191,437 | 106,794 | 1.79 | 13 hrs 23 mins |
| 30 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 179,990 | 88,737 | 2.03 | 11 hrs 50 mins |
| 31 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 127,995 | 127,602 | 1.00 | 23 hrs 56 mins |
| 32 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 122,137 | 96,669 | 1.26 | 18 hrs 60 mins |
| 33 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 90,365 | 84,870 | 1.06 | 22 hrs 32 mins |
| 34 | GeForce GTX 750 GM107 [GeForce GTX 750] 1111 |
Nvidia | GM107 | 86,673 | 84,870 | 1.02 | 23 hrs 30 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:33:45|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 5 3600X 6-CORE | 12 | AMD | ||
| 2 | CORE I7-4770S CPU @ 3.10GHZ | 8 | Intel | ||
| 3 | RYZEN 9 9950X 16-CORE | 32 | AMD |