RESEARCH: CANCER
FOLDING PROJECT #13002 PROFILE
PROJECT TEAM
Manager(s): Lin ZhuInstitution: the University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 60,000Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how special proteins called fluorescent proteins can be used to find rare earth elements in our bodies. These elements are important for health, and finding them early could help diagnose diseases like cancer. The project will try to make even stronger fluorescent proteins that grab onto specific rare earth elements better than before. They'll do this by testing 100 different versions of a yellow protein, changing one part at a time to see what works best.
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 100 single-point mutant 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 absorb light at one wavelength and emit it at a longer wavelength. This property makes them valuable tools in biological research for visualizing cells, tracking molecules, and studying protein interactions.
Rare earth elements
A group of 17 metallic elements with similar chemical properties. They are used in various applications, including electronics and medicine.
Rare earth elements are a set of 17 chemical elements known for their unique magnetic, luminescent, and catalytic properties. They are widely used in technologies like smartphones, lasers, and medical imaging due to their versatility.
Supercharged
Modified to have increased activity or efficiency.
In biotechnology, 'supercharged' often refers to proteins that have been genetically engineered to enhance their function. This can involve altering the protein's structure or adding specific domains to improve its binding affinity, stability, or catalytic activity.
Single-point mutant
A protein with a single amino acid substitution.
In protein engineering, a 'single-point mutant' refers to a protein where only one amino acid has been changed. This is often used to study the function of specific amino acids and how changes in their sequence can affect protein properties.
Yellow fluorescent protein
A type of fluorescent protein that emits yellow light.
Yellow fluorescent protein (YFP) is a widely used fluorescent marker in biological research. It belongs to the class of green fluorescent proteins (GFPs) and emits yellow-green fluorescence when exposed to blue light. YFP is valuable for visualizing cellular processes, tracking protein interactions, and studying gene expression.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:33:49|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 3,501,369 | 354,905 | 9.87 | 2 hrs 26 mins |
| 2 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,441,918 | 316,135 | 7.72 | 3 hrs 6 mins |
| 3 | GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB] |
Nvidia | TU106 | 2,433,378 | 314,999 | 7.73 | 3 hrs 6 mins |
| 4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,106,846 | 300,609 | 7.01 | 3 hrs 25 mins |
| 5 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 1,937,875 | 293,039 | 6.61 | 3 hrs 38 mins |
| 6 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,920,375 | 276,379 | 6.95 | 3 hrs 27 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 1,912,347 | 281,219 | 6.80 | 3 hrs 32 mins |
| 8 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 1,893,025 | 281,022 | 6.74 | 3 hrs 34 mins |
| 9 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,773,224 | 286,273 | 6.19 | 3 hrs 52 mins |
| 10 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 1,736,080 | 282,020 | 6.16 | 3 hrs 54 mins |
| 11 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,561,221 | 76,132 | 20.51 | 1 hrs 10 mins |
| 12 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,284,175 | 266,656 | 4.82 | 4 hrs 59 mins |
| 13 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,263,239 | 107,183 | 11.79 | 2 hrs 2 mins |
| 14 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,167,058 | 248,290 | 4.70 | 5 hrs 6 mins |
| 15 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 948,439 | 239,050 | 3.97 | 6 hrs 3 mins |
| 16 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 807,921 | 216,216 | 3.74 | 6 hrs 25 mins |
| 17 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 774,733 | 215,388 | 3.60 | 6 hrs 40 mins |
| 18 | Quadro P3200 Mobile GP104GLM [Quadro P3200 Mobile] |
Nvidia | GP104GLM | 656,321 | 204,402 | 3.21 | 7 hrs 28 mins |
| 19 | Radeon RX 6600/6600 XT/6600M Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 XT-XL | 633,557 | 221,748 | 2.86 | 8 hrs 24 mins |
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| 20 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 201,308 | 155,341 | 1.30 | 18 hrs 31 mins |
| 21 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 162,694 | 128,323 | 1.27 | 18 hrs 56 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:33:49|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|