RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12101 PROFILE
PROJECT TEAM
Manager(s): Hassan NadeemInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 4,745Core: 0x22
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
This project explores how short chains of amino acids (peptides) fold into different shapes. By using computer models and machine learning, scientists hope to understand how these shapes affect the peptides' functions. This knowledge can then be used to design new peptides for applications in things like bio-inspired materials.
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 chains of amino acids, the building blocks of proteins. They play various roles in biological systems, including signaling and enzymatic activity. Studying peptide conformations helps understand how they fold and function.
Conformations
The different 3-dimensional shapes that a molecule can adopt.
Conformations refer to the various ways a molecule can fold and arrange its atoms. Understanding these shapes is crucial for comprehending how molecules interact and function.
Simulations
Computer-based models that mimic real-world processes.
Simulations use computer programs to recreate and study complex systems. In biomedicine, they are used to model molecular interactions, protein folding, and drug effects.
Machine Learning
A type of artificial intelligence that allows computers to learn from data.
Machine learning algorithms enable computers to analyze vast datasets and identify patterns. In biotechnology, it's used for drug discovery, protein design, and disease prediction.
Rational Design
The process of designing molecules with specific properties.
Rational design involves using scientific knowledge and computational tools to create molecules that target specific biological pathways. It's a crucial approach for developing new drugs and therapies.
Biologically-Inspired Materials
Materials that mimic the structures and functions of biological systems.
Biologically-inspired materials draw inspiration from nature's designs to create innovative products. Examples include self-healing materials and biocompatible implants.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:35:54|
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 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,488,432 | 59,897 | 41.55 | 0 hrs 35 mins |
| 2 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,436,926 | 80,808 | 30.16 | 0 hrs 48 mins |
| 3 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,631,556 | 51,061 | 31.95 | 0 hrs 45 mins |
| 4 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,513,806 | 51,088 | 29.63 | 0 hrs 49 mins |
| 5 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 1,205,548 | 46,955 | 25.67 | 0 hrs 56 mins |
| 6 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 837,532 | 41,759 | 20.06 | 1 hrs 12 mins |
| 7 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 747,101 | 39,945 | 18.70 | 1 hrs 17 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:35:54|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|