Folding project #12103, PEPTIDE-CONFORMATION-MODELING research

Research section Research projects and references

Project #12103 overview

Project Summary AI Beta

This project explores the different shapes short chains of amino acids (peptides) can take. By using computer models and machine learning, we aim to understand how these shapes influence their function. This knowledge could be used to design new peptides for applications like creating materials inspired by biology.

Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms: 41,000
Core: 0x22
Status: Public

Source material

Official 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.

Reading reference

Related Terms Glossary AI Beta

Automated high-level definitions; simplified and may not be fully accurate.

Peptide

A short chain of amino acids linked together.

Scientific: BiotechnologyBiotechnology / Peptides and Proteins

A peptide is a short chain of amino acids connected by chemical bonds. Peptides are important building blocks of proteins and have various functions in living organisms, such as signaling, transporting molecules, and defending against infections.

Conformations

The different shapes that a molecule can take.

Scientific: PharmaceuticalsBiotechnology / Structural Biology

Conformations refer to the various three-dimensional shapes that molecules can adopt. These shapes are crucial for a molecule's function, as they determine how it interacts with other molecules.

Simulations

Computer models that mimic real-world processes.

Scientific: BiotechnologyBiotechnology / Computational Biology

Simulations are computer programs that create virtual environments to study and predict how systems behave. In biotechnology, simulations are used to model biological processes, such as protein folding or drug interactions.

Machine Learning

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

Technical: BiotechnologyBiotechnology / Data Science

Machine learning is a branch of artificial intelligence where computers are trained on large datasets to identify patterns and make predictions. In biotechnology, machine learning is used for tasks such as drug discovery, disease diagnosis, and personalized medicine.

Rational Design

A method of designing molecules with specific properties.

Technical: PharmaceuticalsBiotechnology / Drug Discovery

Rational design is a process used in drug development to create molecules with desired properties. Scientists use their knowledge of biological targets and molecular interactions to design compounds that bind to specific proteins or enzymes.

Biologically-inspired Materials

Materials that are designed based on biological structures or processes.

Scientific: Materials ScienceMaterials Science / Biomimetics

Biologically-inspired materials are developed by mimicking the structure and function of natural materials. These materials often exhibit unique properties such as strength, flexibility, and self-healing capabilities.

GPU PPD Averages

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	3,186,660	406,216	7.84	3 ^hrs 4 ^mins
2	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,348,394	353,111	6.65	3 ^hrs 37 ^mins
3	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	2,031,761	317,786	6.39	3 ^hrs 45 ^mins
4	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	1,991,001	345,070	5.77	4 ^hrs 10 ^mins
5	GeForce RTX 2060 TU104 [GeForce RTX 2060]	Nvidia	TU104	1,597,512	326,165	4.90	4 ^hrs 54 ^mins
6	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	1,450,361	326,060	4.45	5 ^hrs 24 ^mins
7	P102-100 GP102 [P102-100]	Nvidia	GP102	1,383,466	301,612	4.59	5 ^hrs 14 ^mins
8	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	1,373,938	313,412	4.38	5 ^hrs 28 ^mins
9	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,296,716	304,902	4.25	5 ^hrs 39 ^mins
10	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,157,992	277,848	4.17	5 ^hrs 46 ^mins
11	Tesla M40 GM200GL [Tesla M40] 6844	Nvidia	GM200GL	1,135,093	288,060	3.94	6 ^hrs 5 ^mins
12	P104-100 GP104 [P104-100]	Nvidia	GP104	1,044,136	280,820	3.72	6 ^hrs 27 ^mins
13	Tesla P4 GP104GL [Tesla P4] 5704	Nvidia	GP104GL	990,621	296,717	3.34	7 ^hrs 11 ^mins
14	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	932,320	269,666	3.46	6 ^hrs 57 ^mins
15	GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER]	Nvidia	TU116	813,995	257,493	3.16	7 ^hrs 36 ^mins
16	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	785,972	248,214	3.17	7 ^hrs 35 ^mins
17	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	735,118	249,429	2.95	8 ^hrs 9 ^mins
18	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	626,612	235,279	2.66	9 ^hrs 1 ^mins
19	GeForce GTX 980 GM204 [GeForce GTX 980] 4612	Nvidia	GM204	605,163	254,280	2.38	10 ^hrs 5 ^mins

20	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	527,355	209,632	2.52	9 ^hrs 32 ^mins
21	GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091	Nvidia	TU116	516,832	230,693	2.24	10 ^hrs 43 ^mins
22	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	513,144	139,786	3.67	6 ^hrs 32 ^mins
23	GeForce GTX 1060 Mobile GP106M [GeForce GTX 1060 Mobile]	Nvidia	GP106M	504,666	219,973	2.29	10 ^hrs 28 ^mins
24	Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile]	Nvidia	TU117GLM	487,074	217,871	2.24	10 ^hrs 44 ^mins
25	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	443,719	234,043	1.90	12 ^hrs 40 ^mins
26	GeForce GTX 960 GM206 [GeForce GTX 960] 2308	Nvidia	GM206	358,857	196,833	1.82	13 ^hrs 10 ^mins
27	P106-090 GP106 [P106-090]	Nvidia	GP106	309,347	214,308	1.44	16 ^hrs 38 ^mins
28	GeForce GTX 950 GM206 [GeForce GTX 950] 1572	Nvidia	GM206	302,431	185,670	1.63	14 ^hrs 44 ^mins
29	RX Vega M GL Polaris 22 XL [RX Vega M GL]	AMD	Polaris 22 XL	179,211	155,907	1.15	20 ^hrs 53 ^mins
30	GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862	Nvidia	GP107	161,803	150,286	1.08	22 ^hrs 18 ^mins
31	GeForce MX110 GM108M [GeForce MX110]	Nvidia	GM108M	118,835	118,835	1.00	24 ^hrs 0 ^mins
32	GeForce GT 1030 GP108 [GeForce GT 1030]	Nvidia	GP108	114,049	128,167	0.89	26 ^hrs 58 ^mins

Research: PEPTIDE-CONFORMATION-MODELING Folding Project #12103

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 12103

GPU PPD Averages

Research: PEPTIDE-CONFORMATION-MODELING Folding Project #12103

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages