RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12111 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 3,900
Core: 0x22
Status: Public

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

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

Peptide

A short chain of amino acids linked together.

Scientific: Biotechnology
Biochemistry / Protein Structure

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.

Scientific: Biotechnology
Biochemistry / Protein Structure

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.

Scientific: Biotechnology
Biochemistry / Computational Biology

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.

Technical: Biotechnology
Computer Science / Artificial Intelligence

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.

Scientific: Biotechnology
Biotechnology / Drug Discovery

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.

Scientific: Biotechnology
Materials Science / Biomimicry

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