RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12121 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 40,000Core: 0x22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores the different shapes short chains of amino acids (peptides) can take. Using computer models and machine learning, they aim to understand how these shapes affect peptide function. This knowledge could be used to design new peptides for use in 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 serve various functions in living organisms. They can act as hormones, enzymes, or structural components. Understanding their structure and how they fold is crucial for developing new drugs and materials.
Conformations
The different 3-dimensional shapes a molecule can adopt.
Conformations refer to the various shapes that molecules can take. For peptides, understanding their conformations is essential because it influences their function. Different shapes allow peptides to bind to specific targets or interact with other molecules in unique ways.
Simulations
Computer models that mimic real-world processes.
Simulations are used in various scientific fields to study complex systems. In biotechnology, simulations can be used to predict how molecules will behave under different conditions, such as protein folding or drug interactions.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning is a powerful tool used in biotechnology to analyze large datasets and identify patterns. It can be used to predict protein structures, design new drugs, or personalize treatment plans.
Rational Design
The process of designing molecules with specific properties.
Rational design involves using knowledge about a molecule's structure and function to create new molecules with desired characteristics. This is commonly used in drug development to create more effective and safer medications.
Biologically-Inspired Materials
Materials that are designed based on biological principles.
Biologically-inspired materials mimic the properties of natural materials, such as strength, flexibility, or self-healing abilities. These materials have potential applications in various industries, including medicine, aerospace, and construction.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:45|
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 | 2,841,389 | 176,358 | 16.11 | 1 hrs 29 mins |
| 2 | GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB] |
Nvidia | TU106 | 2,453,744 | 167,708 | 14.63 | 1 hrs 38 mins |
| 3 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,159,582 | 152,717 | 14.14 | 1 hrs 42 mins |
| 4 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,093,196 | 159,136 | 13.15 | 1 hrs 49 mins |
| 5 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,029,719 | 157,768 | 12.87 | 1 hrs 52 mins |
| 6 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,009,605 | 154,256 | 13.03 | 1 hrs 51 mins |
| 7 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 1,790,000 | 151,259 | 11.83 | 2 hrs 2 mins |
| 8 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,675,642 | 124,391 | 13.47 | 1 hrs 47 mins |
| 9 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 1,591,162 | 144,952 | 10.98 | 2 hrs 11 mins |
| 10 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,565,909 | 144,724 | 10.82 | 2 hrs 13 mins |
| 11 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,363,060 | 133,133 | 10.24 | 2 hrs 21 mins |
| 12 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,320,856 | 136,293 | 9.69 | 2 hrs 29 mins |
| 13 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,293,594 | 84,085 | 15.38 | 1 hrs 34 mins |
| 14 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,153,274 | 113,572 | 10.15 | 2 hrs 22 mins |
| 15 | GeForce RTX 2070 Mobile TU106M [GeForce RTX 2070 Mobile] |
Nvidia | TU106M | 1,146,250 | 130,337 | 8.79 | 2 hrs 44 mins |
| 16 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,096,808 | 82,863 | 13.24 | 1 hrs 49 mins |
| 17 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,071,162 | 127,275 | 8.42 | 2 hrs 51 mins |
| 18 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 978,858 | 123,671 | 7.92 | 3 hrs 2 mins |
| 19 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 850,005 | 42,950 | 19.79 | 1 hrs 13 mins |
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| 20 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 772,551 | 114,769 | 6.73 | 3 hrs 34 mins |
| 21 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 728,833 | 36,145 | 20.16 | 1 hrs 11 mins |
| 22 | Quadro P3200 Mobile GP104GLM [Quadro P3200 Mobile] |
Nvidia | GP104GLM | 724,526 | 85,590 | 8.47 | 2 hrs 50 mins |
| 23 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 694,017 | 110,921 | 6.26 | 3 hrs 50 mins |
| 24 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 662,848 | 108,527 | 6.11 | 3 hrs 56 mins |
| 25 | GeForce GTX 1650 Ti Mobile TU117M [GeForce GTX 1650 Ti Mobile] |
Nvidia | TU117M | 580,231 | 103,928 | 5.58 | 4 hrs 18 mins |
| 26 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 557,282 | 51,847 | 10.75 | 2 hrs 14 mins |
| 27 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 513,751 | 36,145 | 14.21 | 1 hrs 41 mins |
| 28 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 462,964 | 95,056 | 4.87 | 4 hrs 56 mins |
| 29 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 291,552 | 84,706 | 3.44 | 6 hrs 58 mins |
| 30 | Quadro M4000 GM204GL [Quadro M4000] |
Nvidia | GM204GL | 289,722 | 83,721 | 3.46 | 6 hrs 56 mins |
| 31 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 278,216 | 36,145 | 7.70 | 3 hrs 7 mins |
| 32 | GeForce GTX 1050 Mobile GP107M [GeForce GTX 1050 Mobile] |
Nvidia | GP107M | 260,216 | 79,674 | 3.27 | 7 hrs 21 mins |
| 33 | GeForce GTX 1050 3 GB Max-Q GP107M [GeForce GTX 1050 3 GB Max-Q] |
Nvidia | GP107M | 246,482 | 78,707 | 3.13 | 7 hrs 40 mins |
| 34 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 170,926 | 71,689 | 2.38 | 10 hrs 4 mins |
| 35 | GeForce GTX 750 GM107 [GeForce GTX 750] 1111 |
Nvidia | GM107 | 112,643 | 64,857 | 1.74 | 13 hrs 49 mins |
| 36 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 58,936 | 50,810 | 1.16 | 20 hrs 41 mins |
| 37 | GeForce GTX 660 GK106 [GeForce GTX 660] |
Nvidia | GK106 | 40,706 | 38,146 | 1.07 | 22 hrs 29 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:45|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|