RESEARCH: PEPTIDE-CONFORMATION-MODELING
FOLDING PROJECT #12110 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 figuring out how small pieces of protein (peptides) fold into different shapes. These shapes are important because they determine what the peptides can do. By using computer models and machine learning, scientists hope to understand all the possible shapes that peptides can take. This knowledge can then be used to design new peptides for use 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

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 / Peptidomics

A peptide is a small molecule made up of several amino acids joined together. They are important building blocks in proteins and have various functions in the body, such as signaling and transporting molecules.


Conformations

The different 3D shapes a molecule can adopt.

Scientific: Pharmaceutical
Biotechnology / Structural Biology

Conformations refer to the various three-dimensional shapes that a molecule can take. These shapes are important because they influence how a molecule interacts with other molecules and performs its function.


Simulations

Computer-based models that mimic real-world processes.

Scientific: Pharmaceutical
Biotechnology / Computational Biology

Simulations are computer programs that imitate complex systems and processes. They are used in many fields, including science and engineering, to study and predict how things work.


Machine Learning

A type of artificial intelligence that allows computers to learn from data.

Technical: Pharmaceutical
Biotechnology / Data Science

Machine learning is a subset of artificial intelligence where computers are trained on large datasets to identify patterns and make predictions. It's used in various applications, including image recognition, speech synthesis, and drug discovery.


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 with desired properties. It involves understanding the structure-function relationships of target molecules and using this knowledge to design novel compounds.


Biologically-Inspired Materials

Materials that are designed based on biological structures or processes.

Scientific: Biotechnology
Materials Science / Biomimicry

Biologically-inspired materials draw inspiration from nature's designs and functionalities. These materials aim to mimic the properties of biological systems, such as strength, flexibility, and self-healing capabilities.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Tuesday, 14 April 2026 06:35:51
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 5,848,552 66,640 87.76 0 hrs 16 mins
2 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 5,715,977 65,721 86.97 0 hrs 17 mins
3 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 5,184,000 6,000 864.00 0 hrs 2 mins
4 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 5,163,683 25,759 200.46 0 hrs 7 mins
5 TITAN X
GP102 [TITAN X] 6144
Nvidia GP102 4,357,293 61,614 70.72 0 hrs 20 mins
6 TITAN Xp
GP102 [TITAN Xp] 12150
Nvidia GP102 4,342,519 61,841 70.22 0 hrs 21 mins
7 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 4,215,936 59,565 70.78 0 hrs 20 mins
8 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 4,082,169 56,648 72.06 0 hrs 20 mins
9 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 3,858,605 58,747 65.68 0 hrs 22 mins
10 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 3,311,461 55,698 59.45 0 hrs 24 mins
11 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 2,879,914 52,158 55.22 0 hrs 26 mins
12 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 2,227,497 46,146 48.27 0 hrs 30 mins
13 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 1,728,598 44,510 38.84 0 hrs 37 mins
14 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 1,355,586 6,000 225.93 0 hrs 6 mins
15 Radeon 660M-680M
Rembrandt [Radeon 660M-680M]
AMD Rembrandt 915,947 36,831 24.87 0 hrs 58 mins
16 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 617,760 6,000 102.96 0 hrs 14 mins
17 RX 470/480/570/580/590
Ellesmere XT [RX 470/480/570/580/590]
AMD Ellesmere XT 290,310 23,361 12.43 1 hrs 56 mins
18 Vega Mobile 5000 series APU
Cezanne [Vega Mobile 5000 series APU]
AMD Cezanne 209,758 13,863 15.13 1 hrs 35 mins
19 Radeon R9 285/380
Tonga PRO [Radeon R9 285/380]
AMD Tonga PRO 190,684 21,526 8.86 2 hrs 43 mins
20 R7 370/R9 270/370 OEM
Curacao Pro [R7 370/R9 270/370 OEM]
AMD Curacao Pro 174,898 19,014 9.20 2 hrs 37 mins
21 Ryzen 7000 Series iGPU
Raphael [Ryzen 7000 Series iGPU]
AMD Raphael 132,026 18,317 7.21 3 hrs 20 mins
22 R7 370/R9 270X/370X
Curacao XT/Trinidad XT [R7 370/R9 270X/370X]
AMD Curacao XT/Trinidad XT 124,081 18,794 6.60 3 hrs 38 mins
23 HD 7850/R7 265/R9 270 1024SP
Pitcairn PRO [HD 7850/R7 265/R9 270 1024SP]
AMD Pitcairn PRO 114,196 17,371 6.57 3 hrs 39 mins
24 R9 280X/HD 7900/8970 OEM
Tahiti XT [R9 280X/HD 7900/8970 OEM]
AMD Tahiti XT 69,456 15,318 4.53 5 hrs 18 mins
25 Radeon 540/540X/550/550X/RX 540X/550/550X
Lexa PRO [Radeon 540/540X/550/550X/RX 540X/550/550X]
AMD Lexa PRO 61,433 14,930 4.11 5 hrs 50 mins
26 R7 240/340/520/HD8570
Hawaii [R7 240/340/520/HD8570]
AMD Hawaii 25,298 11,051 2.29 10 hrs 29 mins
27 Ryzen 4900HS mobile
Renoir [Ryzen 4900HS mobile]
AMD Renoir 21,777 9,077 2.40 10 hrs 0 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Tuesday, 14 April 2026 06:35:51
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make