RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12119 PROFILE

PROJECT TEAM

Manager(s): Hassan Nadeem
Institution: University of Illinois at Urbana-Champaign

WORK UNIT INFO

Atoms: 40,000
Core: 0x22
Status: Public

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

Note: Glossary items are a high level summary and may not be 100% accurate.

Peptide

A short chain of amino acids linked together.

Scientific: Pharmaceutical
Biotechnology / Peptides & Proteins

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.

Scientific: Pharmaceutical
Biotechnology / Structural Biology

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.

Scientific: Pharmaceutical
Biotechnology / Computational Biology

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.

Scientific: Pharmaceutical
Biotechnology / Data Science

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.

Scientific: Pharmaceutical
Biotechnology / Drug Discovery

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.

Scientific: Pharmaceutical
Biotechnology / Material Science

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
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