RESEARCH: REFLECTIN-PEPTIDE-CONFORMATION
FOLDING PROJECT #12132 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 222,255Core: 0x24
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at special peptides made from squid and octopus proteins. These peptides can change shape based on things like light and pH, making them useful for things like sensors, drug delivery, and even building new tissues. Scientists are studying four different types of these peptides to understand how they fold and work.
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
A protein found in cephalopods that gives them their iridescent coloration.
Reflectin is a type of protein found in the skin of squid and octopuses. It allows them to change color by reflecting light. Scientists are studying reflectin because it has potential uses in medicine, such as creating new drugs and biosensors.
Peptides
Short chains of amino acids that are important for many biological processes.
Peptides are short chains made up of building blocks called amino acids. They play many roles in the body, including sending signals between cells, fighting infections, and breaking down food. Scientists are exploring how peptides can be used to create new drugs and therapies.
Therapeutic Agents
Substances used to treat or prevent diseases.
Therapeutic agents are medications or treatments that are used to diagnose, treat, or prevent diseases. They can work in many different ways, such as by killing bacteria, reducing inflammation, or blocking the growth of cancer cells.
Biosensors
Devices that use biological components to detect or measure specific analytes.
Biosensors are devices that combine biology and technology to detect or measure substances. They can be used in a variety of applications, such as monitoring blood sugar levels, detecting pathogens in food, or measuring the concentration of pollutants in water.
Drug Delivery Systems
Systems designed to deliver drugs to specific target sites in the body.
Drug delivery systems are technologies that are used to deliver medications to specific parts of the body. This can improve the effectiveness of the medication and reduce side effects.
Tissue Engineering
The process of creating functional tissues or organs in the laboratory.
Tissue engineering is a field that aims to create new tissues and organs in the laboratory. This could be used to repair damaged tissues, grow replacement organs for transplantation, or develop models for studying diseases.
Mutant Peptides
Peptides that have had their amino acid sequence altered.
Mutant peptides are created by making changes to the sequence of amino acids in a peptide. These changes can alter the function of the peptide or make it more suitable for specific applications.
Secondary Structure Conformation
The arrangement of amino acids in a protein that is stabilized by hydrogen bonds.
Secondary structure conformation refers to the way in which parts of a protein chain are arranged. This arrangement is important for the protein's function.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:39|
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 | 46,615,245 | 154,065 | 302.57 | 0 hrs 5 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 27,556,023 | 355,451 | 77.52 | 0 hrs 19 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 20,804,166 | 154,065 | 135.03 | 0 hrs 11 mins |
| 4 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 20,018,974 | 753,368 | 26.57 | 0 hrs 54 mins |
| 5 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 19,041,829 | 1,180,942 | 16.12 | 1 hrs 29 mins |
| 6 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 16,606,864 | 154,065 | 107.79 | 0 hrs 13 mins |
| 7 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 16,394,880 | 210,722 | 77.80 | 0 hrs 19 mins |
| 8 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 15,706,574 | 687,900 | 22.83 | 1 hrs 3 mins |
| 9 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 11,817,748 | 289,892 | 40.77 | 0 hrs 35 mins |
| 10 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 10,864,433 | 154,065 | 70.52 | 0 hrs 20 mins |
| 11 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 10,244,942 | 489,490 | 20.93 | 1 hrs 9 mins |
| 12 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 9,144,369 | 154,065 | 59.35 | 0 hrs 24 mins |
| 13 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 7,427,188 | 154,065 | 48.21 | 0 hrs 30 mins |
| 14 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 7,217,034 | 465,053 | 15.52 | 1 hrs 33 mins |
| 15 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 7,081,303 | 425,314 | 16.65 | 1 hrs 26 mins |
| 16 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 6,789,265 | 154,065 | 44.07 | 0 hrs 33 mins |
| 17 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 6,731,228 | 412,288 | 16.33 | 1 hrs 28 mins |
| 18 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 6,044,498 | 154,065 | 39.23 | 0 hrs 37 mins |
| 19 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,973,762 | 154,065 | 38.77 | 0 hrs 37 mins |
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| 20 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 5,348,878 | 154,065 | 34.72 | 0 hrs 41 mins |
| 21 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 4,444,741 | 179,601 | 24.75 | 0 hrs 58 mins |
| 22 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 4,334,600 | 154,065 | 28.13 | 0 hrs 51 mins |
| 23 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,186,750 | 379,928 | 11.02 | 2 hrs 11 mins |
| 24 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 4,028,824 | 154,065 | 26.15 | 0 hrs 55 mins |
| 25 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,647,067 | 347,851 | 10.48 | 2 hrs 17 mins |
| 26 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 3,395,177 | 154,065 | 22.04 | 1 hrs 5 mins |
| 27 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,854,309 | 154,065 | 18.53 | 1 hrs 18 mins |
| 28 | Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics] |
Intel | Battlemage G21 | 2,661,641 | 154,065 | 17.28 | 1 hrs 23 mins |
| 29 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 234,412 | 154,065 | 1.52 | 15 hrs 46 mins |
| 30 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 96,031 | 154,065 | 0.62 | 38 hrs 30 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:39|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|