RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12111 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 3,900Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to exploring how short chains of amino acids (peptides) fold into different shapes. By using computer models and machine learning, we want to understand all the possible shapes peptides can take. This knowledge can then be used to design new peptides for uses like creating new materials inspired by nature.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Peptide Conformations Short peptides can be used to observe possible conformations, which are important factors for determining their function.
Insights from these simulations can then be further extended to larger peptides.
In this study, we aim to explore the conformational space of all possible short peptides and using modeling techniques like machine learning to gain insights.
These can then be used for rational design of peptides for applications in biologically-inspired materials.
RELATED TERMS GLOSSARY AI BETA
Peptide
A short chain of amino acids linked together.
Peptides are small chains of amino acids, the building blocks of proteins. They play many roles in the body and can be used in various applications like medicine and materials science.
Conformations
The different shapes a molecule can take.
Molecules like proteins can fold into many different shapes called conformations. These shapes determine how molecules interact and function.
Simulations
Computer models of biological processes.
Simulations are computer programs that mimic real-world processes like protein folding. They help researchers understand complex biological systems.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is a powerful tool that enables computers to identify patterns and make predictions based on large datasets. It's widely used in fields like healthcare and drug discovery.
Rational Design
The process of designing molecules with specific properties.
Rational design involves using knowledge of a molecule's structure and function to create new molecules with desired characteristics. This is often used in drug development.
Biologically-inspired Materials
Materials that are designed based on biological systems.
Biologically-inspired materials take inspiration from nature's designs to create new materials with unique properties. This can lead to advancements in areas like sustainability and medicine.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:50|
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 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 6,203,381 | 68,699 | 90.30 | 0 hrs 16 mins |
| 2 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 5,997,710 | 69,211 | 86.66 | 0 hrs 17 mins |
| 3 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 5,280,872 | 6,000 | 880.15 | 0 hrs 2 mins |
| 4 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 4,926,432 | 60,327 | 81.66 | 0 hrs 18 mins |
| 5 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 4,838,150 | 32,282 | 149.87 | 0 hrs 10 mins |
| 6 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 4,685,260 | 62,276 | 75.23 | 0 hrs 19 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 4,306,425 | 67,533 | 63.77 | 0 hrs 23 mins |
| 8 | TITAN X GP102 [TITAN X] 6144 |
Nvidia | GP102 | 4,263,758 | 61,021 | 69.87 | 0 hrs 21 mins |
| 9 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 3,995,114 | 61,866 | 64.58 | 0 hrs 22 mins |
| 10 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 3,982,550 | 59,520 | 66.91 | 0 hrs 22 mins |
| 11 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 3,976,793 | 57,312 | 69.39 | 0 hrs 21 mins |
| 12 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,931,667 | 59,114 | 66.51 | 0 hrs 22 mins |
| 13 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 3,173,331 | 54,358 | 58.38 | 0 hrs 25 mins |
| 14 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 2,612,920 | 29,086 | 89.83 | 0 hrs 16 mins |
| 15 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 1,748,279 | 44,544 | 39.25 | 0 hrs 37 mins |
| 16 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 1,382,400 | 6,000 | 230.40 | 0 hrs 6 mins |
| 17 | RX 5500/5500M/Pro 5500M Navi 14 [RX 5500/5500M/Pro 5500M] |
AMD | Navi 14 | 980,575 | 36,970 | 26.52 | 0 hrs 54 mins |
| 18 | GeForce GTX Titan Z GK110 [GeForce GTX Titan Z] 8122 |
Nvidia | GK110 | 950,462 | 34,548 | 27.51 | 0 hrs 52 mins |
| 19 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 662,400 | 6,000 | 110.40 | 0 hrs 13 mins |
|
|
|||||||
| 20 | Vega Mobile 5000 series APU Cezanne [Vega Mobile 5000 series APU] |
AMD | Cezanne | 350,445 | 9,147 | 38.31 | 0 hrs 38 mins |
| 21 | RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590] |
AMD | Ellesmere XT | 264,605 | 22,776 | 11.62 | 2 hrs 4 mins |
| 22 | R7 370/R9 270/370 OEM Curacao Pro [R7 370/R9 270/370 OEM] |
AMD | Curacao Pro | 190,308 | 22,057 | 8.63 | 2 hrs 47 mins |
| 23 | Radeon R9 285/380 Tonga PRO [Radeon R9 285/380] |
AMD | Tonga PRO | 185,191 | 21,453 | 8.63 | 2 hrs 47 mins |
| 24 | HD 7850/R7 265/R9 270 1024SP Pitcairn PRO [HD 7850/R7 265/R9 270 1024SP] |
AMD | Pitcairn PRO | 155,829 | 20,750 | 7.51 | 3 hrs 12 mins |
| 25 | Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU] |
AMD | Raphael | 151,635 | 19,107 | 7.94 | 3 hrs 1 mins |
| 26 | R7 370/R9 270X/370X Curacao XT/Trinidad XT [R7 370/R9 270X/370X] |
AMD | Curacao XT/Trinidad XT | 121,703 | 18,486 | 6.58 | 3 hrs 39 mins |
| 27 | R9 280X/HD 7900/8970 OEM Tahiti XT [R9 280X/HD 7900/8970 OEM] |
AMD | Tahiti XT | 79,230 | 15,959 | 4.96 | 4 hrs 50 mins |
| 28 | Radeon 540/540X/550/550X/RX 540X/550/550X Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X] |
AMD | Lexa PRO | 54,166 | 14,864 | 3.64 | 6 hrs 35 mins |
| 29 | Ryzen 4900HS mobile Renoir [Ryzen 4900HS mobile] |
AMD | Renoir | 33,436 | 10,953 | 3.05 | 7 hrs 52 mins |
| 30 | Radeon HD 8600M Series Sun [Radeon HD 8600M Series] |
AMD | Sun | 28,689 | 11,620 | 2.47 | 9 hrs 43 mins |
| 31 | R7 240/340/520/HD8570 Hawaii [R7 240/340/520/HD8570] |
AMD | Hawaii | 21,990 | 10,434 | 2.11 | 11 hrs 23 mins |
| 32 | Vega Mobile APU Lucienne [Vega Mobile APU] |
AMD | Lucienne | 7,732 | 7,768 | 1.00 | 24 hrs 7 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:50|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|