RESEARCH: REFLECTIN-PEPTIDE-STRUCTURE
FOLDING PROJECT #12122 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 50,000Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at special peptides from squid and octopus that change color. Scientists want to understand how these peptides fold into different shapes, which could lead to new ways to diagnose and treat diseases.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Reflectin-derived peptides hold promise as therapeutic agents due to their unique properties, particularly in the realm of biomedical applications. These peptides, inspired by the reflective capabilities of cephalopods like squid and octopus, exhibit remarkable tunability and responsiveness to external stimuli, such as light and pH changes.
Their ability to self-assemble into ordered structures allows for precise control over their optical properties, making them invaluable in the development of biosensors, drug delivery systems, and tissue engineering scaffolds.
Moreover, their biocompatibility and potential for modification offer versatility in targeting specific biological pathways or cellular processes, opening doors for innovative approaches in disease diagnosis and treatment.
In these projects we study 4 mutant peptides derived from reflectin like proteins to study their secondary structure conformation.
RELATED TERMS GLOSSARY AI BETA
Reflectin-derived peptides
Peptides derived from reflectin proteins, known for their unique properties.
Reflectin-derived peptides are short chains of amino acids created from reflectin proteins. These peptides have exciting potential in medicine due to their ability to change based on their environment (like light or pH). This makes them useful for things like biosensors, delivering drugs, and even building artificial tissues. Scientists are also exploring how these peptides can help diagnose and treat diseases.
Cephalopods
A group of marine mollusks that includes squid and octopus.
Cephalopods are fascinating sea creatures like squid and octopus. They are known for their intelligence, complex nervous systems, and ability to change color and texture to blend in with their surroundings.
Biosensors
Devices that use biological components to detect and measure specific analytes.
Biosensors are like tiny detectors that use living things or their parts to find and measure things in our environment. They're used in medical tests to check for diseases, in food safety to detect harmful bacteria, and even in environmental monitoring to track pollution.
Drug Delivery Systems
Systems designed to deliver drugs to specific target sites in the body.
Drug delivery systems are like smart packages that carry medicine directly to where it's needed in the body. This can make treatments more effective and reduce side effects.
Tissue Engineering
The field of using cells, biomaterials, and biochemical factors to create functional tissues.
Tissue engineering is like building new tissues in the lab. Scientists use special materials and cells to grow replacement tissues that can be used to repair damaged organs or even create whole organs.
Mutant Peptides
Peptides with altered amino acid sequences from their natural counterparts.
Mutant peptides are like slightly changed versions of normal peptides. Scientists create these changes to see how they affect the peptide's function and potential uses.
Secondary Structure Conformation
The local folding patterns of proteins.
Proteins have a specific shape that's important for their function. Secondary structure conformation refers to the way parts of a protein fold into regular shapes like helices or sheets.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:44|
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 | 3,999,072 | 188,805 | 21.18 | 1 hrs 8 mins |
| 2 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 3,162,145 | 67,500 | 46.85 | 0 hrs 31 mins |
| 3 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,520,234 | 164,122 | 15.36 | 1 hrs 34 mins |
| 4 | GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB] |
Nvidia | TU106 | 2,470,429 | 250,231 | 9.87 | 2 hrs 26 mins |
| 5 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,377,474 | 252,005 | 9.43 | 2 hrs 33 mins |
| 6 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,034,240 | 240,013 | 8.48 | 2 hrs 50 mins |
| 7 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 1,900,180 | 125,873 | 15.10 | 1 hrs 35 mins |
| 8 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,818,739 | 67,500 | 26.94 | 0 hrs 53 mins |
| 9 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,382,959 | 195,072 | 7.09 | 3 hrs 23 mins |
| 10 | GeForce RTX 2070 Mobile TU106M [GeForce RTX 2070 Mobile] |
Nvidia | TU106M | 1,232,355 | 203,388 | 6.06 | 3 hrs 58 mins |
| 11 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,150,478 | 151,459 | 7.60 | 3 hrs 10 mins |
| 12 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,096,662 | 195,315 | 5.61 | 4 hrs 16 mins |
| 13 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,082,841 | 68,145 | 15.89 | 1 hrs 31 mins |
| 14 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 963,994 | 67,500 | 14.28 | 1 hrs 41 mins |
| 15 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 696,427 | 67,500 | 10.32 | 2 hrs 20 mins |
| 16 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 534,779 | 67,500 | 7.92 | 3 hrs 2 mins |
| 17 | Radeon Vega FE Vega 10 XTX [Radeon Vega FE] |
AMD | Vega 10 XTX | 528,523 | 164,070 | 3.22 | 7 hrs 27 mins |
| 18 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 512,220 | 163,652 | 3.13 | 7 hrs 40 mins |
| 19 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 347,706 | 67,500 | 5.15 | 4 hrs 40 mins |
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| 20 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 188,780 | 108,736 | 1.74 | 13 hrs 49 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:44|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|