RESEARCH: REFLECTIN-PEPTIDE-CONFORMATION
FOLDING PROJECT #12136 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 202,377Core: 0x24
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Squid and octopus reflectin proteins inspire new medicines! Scientists are studying special protein pieces that can change shape based on light or pH. These changes could be used to create better sensors, deliver drugs, build tissues, or even 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 the protein reflectin found in cephalopods.
Reflectin-derived peptides are molecules created from a protein called reflectin found in animals like squid and octopus. They are being studied for their potential use in medicine because they can change shape in response to things like light or acidity. This makes them useful for creating biosensors, delivering drugs, and building tissues.
Cephalopods
A class of marine animals including squid, octopus, and cuttlefish.
Cephalopods are a group of sea creatures that include squid, octopus, and cuttlefish. They are known for their intelligence and ability to change color.
Biosensors
Devices that detect and measure biological analytes.
Biosensors are tools used to detect and measure substances in living things. They can be used to diagnose diseases, monitor health conditions, and even detect environmental pollutants.
Drug Delivery Systems
Systems designed to deliver drugs to specific target sites in the body.
Drug delivery systems are methods for getting medicine to the right place in the body. This can help make treatments more effective and have fewer side effects.
Tissue Engineering Scaffolds
Three-dimensional structures that provide a framework for tissue growth and repair.
Tissue engineering scaffolds are like building blocks for creating new tissues. They help cells grow and organize into functional tissues, which can be used to repair damaged organs or create artificial tissues.
Secondary Structure Conformation
The arrangement of amino acids in a polypeptide chain, forming alpha-helices, beta-sheets, and other local structures.
Secondary structure conformation refers to the way that protein chains fold into specific patterns. These patterns are important for how proteins function.
Mutant Peptides
Peptides with altered amino acid sequences from their wild-type counterparts.
Mutant peptides are versions of proteins that have been changed in their building blocks (amino acids). Scientists often create mutant peptides to study how changes in a protein's structure affect its function.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:37|
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 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 42,558,395 | 131,200 | 324.38 | 0 hrs 4 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 26,852,305 | 439,790 | 61.06 | 0 hrs 24 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 21,724,283 | 131,200 | 165.58 | 0 hrs 9 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,519,717 | 850,089 | 20.61 | 1 hrs 10 mins |
| 5 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 15,470,706 | 131,200 | 117.92 | 0 hrs 12 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 15,349,642 | 438,202 | 35.03 | 0 hrs 41 mins |
| 7 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 14,988,247 | 727,179 | 20.61 | 1 hrs 10 mins |
| 8 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 13,871,588 | 744,789 | 18.62 | 1 hrs 17 mins |
| 9 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 11,219,106 | 131,200 | 85.51 | 0 hrs 17 mins |
| 10 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 10,857,103 | 131,200 | 82.75 | 0 hrs 17 mins |
| 11 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 10,344,579 | 131,200 | 78.85 | 0 hrs 18 mins |
| 12 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 9,468,870 | 389,895 | 24.29 | 0 hrs 59 mins |
| 13 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 9,331,505 | 131,200 | 71.12 | 0 hrs 20 mins |
| 14 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 7,167,473 | 131,200 | 54.63 | 0 hrs 26 mins |
| 15 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 7,042,328 | 767,352 | 9.18 | 2 hrs 37 mins |
| 16 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,586,019 | 458,960 | 14.35 | 1 hrs 40 mins |
| 17 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 6,455,170 | 398,586 | 16.20 | 1 hrs 29 mins |
| 18 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 6,261,077 | 131,200 | 47.72 | 0 hrs 30 mins |
| 19 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 5,915,167 | 131,200 | 45.09 | 0 hrs 32 mins |
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|||||||
| 20 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 5,287,328 | 131,200 | 40.30 | 0 hrs 36 mins |
| 21 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,246,004 | 131,200 | 39.98 | 0 hrs 36 mins |
| 22 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 4,590,083 | 131,200 | 34.99 | 0 hrs 41 mins |
| 23 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 4,373,061 | 131,200 | 33.33 | 0 hrs 43 mins |
| 24 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 4,083,455 | 131,200 | 31.12 | 0 hrs 46 mins |
| 25 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 3,842,443 | 131,200 | 29.29 | 0 hrs 49 mins |
| 26 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,775,058 | 131,200 | 28.77 | 0 hrs 50 mins |
| 27 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,467,542 | 131,200 | 26.43 | 0 hrs 54 mins |
| 28 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,407,080 | 131,200 | 25.97 | 0 hrs 55 mins |
| 29 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 2,149,403 | 517,167 | 4.16 | 5 hrs 46 mins |
| 30 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 1,522,017 | 131,200 | 11.60 | 2 hrs 4 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:37|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|