RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12119 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 40,000Core: 0x22
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
This project looks at how short pieces of protein (peptides) fold into different shapes. By using computer models, we can learn about all the possible shapes peptides can take. This knowledge can then be used to design new peptides for special purposes, 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 that play important roles in biological processes. They can act as hormones, enzymes, and signaling molecules. Understanding the structure and function of peptides is crucial for drug development and other biotechnological applications.
Conformations
The different 3-dimensional shapes that a molecule can adopt.
Conformations refer to the various three-dimensional shapes that molecules can take. These shapes are influenced by factors like chemical bonds and interactions between atoms. Understanding conformations is essential for studying how molecules function, especially in biological systems.
Simulations
Computer models used to mimic real-world processes.
Simulations are computer programs that recreate complex systems and processes. In biotechnology, simulations are used to study biological phenomena, design experiments, and predict outcomes. They allow researchers to explore scenarios that are difficult or impossible to test in a laboratory setting.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is a powerful tool in biotechnology that enables computers to analyze vast amounts of data and identify patterns. By training algorithms on large datasets, researchers can develop predictive models for tasks like drug discovery, disease diagnosis, and personalized medicine.
Rational Design
The process of designing molecules with specific properties.
Rational design is a systematic approach to creating new molecules, such as drugs or materials, with desired characteristics. It involves understanding the structure-activity relationship of molecules and using computational tools to predict the effects of modifications. This approach allows for the efficient development of innovative solutions in various fields.
Biologically-inspired Materials
Materials that are designed based on biological principles.
Biologically-inspired materials draw inspiration from the structure and function of living organisms to create innovative materials with unique properties. These materials can exhibit self-healing capabilities, strength comparable to bone, or biocompatibility for medical applications.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:46|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,052,662 | 191,669 | 15.93 | 1 hrs 30 mins |
| 2 | GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB] |
Nvidia | TU106 | 2,698,496 | 181,425 | 14.87 | 1 hrs 37 mins |
| 3 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,386,391 | 170,601 | 13.99 | 1 hrs 43 mins |
| 4 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,296,127 | 173,719 | 13.22 | 1 hrs 49 mins |
| 5 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,278,378 | 165,772 | 13.74 | 1 hrs 45 mins |
| 6 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,204,688 | 171,688 | 12.84 | 1 hrs 52 mins |
| 7 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,091,751 | 168,695 | 12.40 | 1 hrs 56 mins |
| 8 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,849,257 | 130,602 | 14.16 | 1 hrs 42 mins |
| 9 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 1,804,048 | 160,476 | 11.24 | 2 hrs 8 mins |
| 10 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,701,625 | 157,817 | 10.78 | 2 hrs 14 mins |
| 11 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,404,518 | 146,240 | 9.60 | 2 hrs 30 mins |
| 12 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,397,189 | 138,285 | 10.10 | 2 hrs 23 mins |
| 13 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,346,635 | 128,263 | 10.50 | 2 hrs 17 mins |
| 14 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,338,290 | 67,783 | 19.74 | 1 hrs 13 mins |
| 15 | GeForce RTX 2070 Mobile TU106M [GeForce RTX 2070 Mobile] |
Nvidia | TU106M | 1,274,966 | 143,187 | 8.90 | 2 hrs 42 mins |
| 16 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,232,455 | 141,598 | 8.70 | 2 hrs 45 mins |
| 17 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,175,185 | 139,269 | 8.44 | 2 hrs 51 mins |
| 18 | GeForce RTX 3050 6GB Laptop GPU GN20-P0-R-K2 [GeForce RTX 3050 6GB Laptop GPU] |
Nvidia | GN20-P0-R-K2 | 1,030,126 | 130,084 | 7.92 | 3 hrs 2 mins |
| 19 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 930,465 | 129,026 | 7.21 | 3 hrs 20 mins |
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|
|||||||
| 20 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 795,311 | 121,186 | 6.56 | 3 hrs 39 mins |
| 21 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 766,600 | 117,855 | 6.50 | 3 hrs 41 mins |
| 22 | Quadro P3200 Mobile GP104GLM [Quadro P3200 Mobile] |
Nvidia | GP104GLM | 753,873 | 120,291 | 6.27 | 3 hrs 50 mins |
| 23 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 715,857 | 117,336 | 6.10 | 3 hrs 56 mins |
| 24 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 685,609 | 117,237 | 5.85 | 4 hrs 6 mins |
| 25 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 588,368 | 39,450 | 14.91 | 1 hrs 37 mins |
| 26 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 580,480 | 109,279 | 5.31 | 4 hrs 31 mins |
| 27 | Quadro T1200 Mobile TU117GLM [Quadro T1200 Mobile] |
Nvidia | TU117GLM | 543,903 | 110,281 | 4.93 | 4 hrs 52 mins |
| 28 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 407,657 | 71,440 | 5.71 | 4 hrs 12 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 327,408 | 88,390 | 3.70 | 6 hrs 29 mins |
| 30 | GeForce GTX 1050 3 GB Max-Q GP107M [GeForce GTX 1050 3 GB Max-Q] |
Nvidia | GP107M | 274,232 | 86,136 | 3.18 | 7 hrs 32 mins |
| 31 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 204,900 | 80,239 | 2.55 | 9 hrs 24 mins |
| 32 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 58,786 | 53,968 | 1.09 | 22 hrs 2 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:46|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|