Folding project #12129, PEPTIDE-CONFORMATION-MODELING research

Research section Research projects and references

Project #12129 overview

Project Summary AI Beta

This project relates to studying how short chains of amino acids (peptides) fold into different shapes. Understanding these shapes helps us figure out what the peptides do. By using computer models, we can learn about all possible shapes and use that knowledge to design new peptides for things like building materials inspired by nature.

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: 285,000
Core: 0x24
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 by peptide bonds.

Scientific: PharmaceuticalBiotechnology / Molecular Biology

Peptides are small chains of amino acids that play various roles in biological processes. They can act as hormones, enzymes, or structural components. Studying their conformations helps understand their functions and design new peptides for applications like medicine and materials science.

Conformations

The different 3-dimensional shapes that a molecule can adopt.

Scientific: PharmaceuticalBiotechnology / Structural Biology

Molecules like peptides can exist in various shapes called conformations. These shapes influence how molecules interact and function. Understanding peptide conformations is crucial for designing drugs and materials.

Machine Learning

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

Technical: PharmaceuticalBiotechnology / Computational Biology

Machine learning uses algorithms to analyze large datasets and identify patterns. In biotechnology, it's used for tasks like predicting protein structures, designing drugs, and understanding biological processes.

Rational Design

The process of designing molecules with specific properties using scientific knowledge.

Technical: PharmaceuticalBiotechnology / Drug Discovery

Rational design involves understanding the structure and function of target molecules to create new molecules with desired effects. It's widely used in drug development to design more effective and safer medications.

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	GeForce RTX 5090 GB202 [GeForce RTX 5090]	Nvidia	GB202	31,713,413	476,300	66.58	0 ^hrs 22 ^mins
2	RTX PRO 6000 Blackwell Server Edition GB202GL [RTX PRO 6000 Blackwell Server Edition]	Nvidia	GB202GL	27,523,427	476,300	57.79	0 ^hrs 25 ^mins
3	GeForce RTX 4090 AD102 [GeForce RTX 4090]	Nvidia	AD102	21,482,672	1,017,711	21.11	1 ^hrs 8 ^mins
4	GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile]	Nvidia	AD106M	19,769,742	476,300	41.51	0 ^hrs 35 ^mins
5	GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER]	Nvidia	AD103	16,302,849	1,172,932	13.90	1 ^hrs 44 ^mins
6	GeForce RTX 4080 AD103 [GeForce RTX 4080]	Nvidia	AD103	15,618,215	2,005,367	7.79	3 ^hrs 5 ^mins
7	GeForce RTX 5080 GB203 [GeForce RTX 5080]	Nvidia	GB203	15,059,983	476,300	31.62	0 ^hrs 46 ^mins
8	GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti]	Nvidia	GB203	14,751,150	713,213	20.68	1 ^hrs 10 ^mins
9	GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti]	Nvidia	AD104	12,649,092	1,692,277	7.47	3 ^hrs 13 ^mins
10	GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER]	Nvidia	AD104	11,045,642	684,278	16.14	1 ^hrs 29 ^mins
11	Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT]	AMD	Navi 21	11,041,217	1,003,941	11.00	2 ^hrs 11 ^mins
12	GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU]	Nvidia	AD103M / GN21-X11	10,911,801	476,300	22.91	1 ^hrs 3 ^mins
13	GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER]	Nvidia	AD103	9,757,712	476,300	20.49	1 ^hrs 10 ^mins
14	GeForce RTX 5070 GB205 [GeForce RTX 5070]	Nvidia	GB205	9,316,349	476,300	19.56	1 ^hrs 14 ^mins
15	GeForce RTX 3090 GA102 [GeForce RTX 3090]	Nvidia	GA102	8,998,671	1,856,670	4.85	4 ^hrs 57 ^mins
16	GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti]	Nvidia	GA102	8,933,386	1,644,393	5.43	4 ^hrs 25 ^mins
17	Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE]	AMD	Navi 31	8,009,783	476,300	16.82	1 ^hrs 26 ^mins
18	RTX PRO 6000 Blackwell Max-Q Workstation Edition GB202GL [RTX PRO 6000 Blackwell Max-Q Workstation Edition]	Nvidia	GB202GL	7,891,162	476,300	16.57	1 ^hrs 27 ^mins
19	GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate]	Nvidia	GA102	7,859,000	1,884,142	4.17	5 ^hrs 45 ^mins

20	GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB]	Nvidia	GA102	7,524,207	476,300	15.80	1 ^hrs 31 ^mins
21	GeForce RTX 5060 GB206 [GeForce RTX 5060]	Nvidia	GB206	6,100,035	476,300	12.81	1 ^hrs 52 ^mins
22	GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti]	Nvidia	GB206	6,036,873	476,300	12.67	1 ^hrs 54 ^mins
23	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	5,617,165	1,091,621	5.15	4 ^hrs 40 ^mins
24	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	5,316,523	476,300	11.16	2 ^hrs 9 ^mins
25	GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti]	Nvidia	GA104	5,252,154	1,349,509	3.89	6 ^hrs 10 ^mins
26	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	5,150,105	476,300	10.81	2 ^hrs 13 ^mins
27	TITAN V GV100 [TITAN V] M 12288	Nvidia	GV100	4,955,727	525,945	9.42	2 ^hrs 33 ^mins
28	Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)]	AMD	Navi 48	4,926,711	781,163	6.31	3 ^hrs 48 ^mins
29	GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate]	Nvidia	GA104	4,826,856	925,297	5.22	4 ^hrs 36 ^mins
30	GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti]	Nvidia	AD106	4,725,616	1,511,608	3.13	7 ^hrs 41 ^mins
31	Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT]	AMD	Navi 21	4,622,831	476,300	9.71	2 ^hrs 28 ^mins
32	Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT]	AMD	Navi 32	3,940,560	476,300	8.27	2 ^hrs 54 ^mins
33	GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068	Nvidia	TU104	3,632,103	1,453,698	2.50	9 ^hrs 36 ^mins
34	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	3,608,496	1,254,993	2.88	8 ^hrs 21 ^mins
35	Tesla V100 PCIe 16GB GV100GL [Tesla V100 PCIe 16GB] M 14028	Nvidia	GV100GL	3,429,474	476,300	7.20	3 ^hrs 20 ^mins
36	GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A]	Nvidia	TU106	3,096,972	476,300	6.50	3 ^hrs 41 ^mins
37	GeForce RTX 4060 AD107 [GeForce RTX 4060]	Nvidia	AD107	2,992,729	476,300	6.28	3 ^hrs 49 ^mins
38	GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti]	Nvidia	GA104	2,933,602	1,331,611	2.20	10 ^hrs 54 ^mins
39	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	2,812,675	1,335,586	2.11	11 ^hrs 24 ^mins

40	Radeon RX 9060(XT) Navi 44 [Radeon RX 9060(XT)]	AMD	Navi 44	2,764,159	476,300	5.80	4 ^hrs 8 ^mins
41	GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate]	Nvidia	GA104	2,752,864	531,622	5.18	4 ^hrs 38 ^mins
42	GeForce RTX 4070 AD104 [GeForce RTX 4070]	Nvidia	AD104	2,562,042	1,273,249	2.01	11 ^hrs 56 ^mins
43	TITAN Xp GP102 [TITAN Xp] 12150	Nvidia	GP102	2,441,607	476,300	5.13	4 ^hrs 41 ^mins
44	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,294,275	1,264,414	1.81	13 ^hrs 14 ^mins
45	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super]	Nvidia	TU106	2,291,891	985,449	2.33	10 ^hrs 19 ^mins
46	GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate]	Nvidia	GA106	2,170,618	703,813	3.08	7 ^hrs 47 ^mins
47	Quadro RTX 4000 TU104GL [Quadro RTX 4000]	Nvidia	TU104GL	2,161,099	476,300	4.54	5 ^hrs 17 ^mins
48	GeForce RTX 3060 GA104 [GeForce RTX 3060]	Nvidia	GA104	2,037,007	476,300	4.28	5 ^hrs 37 ^mins
49	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,955,057	792,351	2.47	9 ^hrs 44 ^mins
50	GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER]	Nvidia	TU104	1,934,642	1,170,216	1.65	14 ^hrs 31 ^mins
51	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	1,889,990	1,202,341	1.57	15 ^hrs 16 ^mins
52	Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]	AMD	Navi 22 XT-XL	1,801,099	476,300	3.78	6 ^hrs 21 ^mins
53	Radeon RX 6600(XT/M) Navi 23 XT-XL [Radeon RX 6600(XT/M)]	AMD	Navi 23 XT-XL	1,568,242	476,300	3.29	7 ^hrs 17 ^mins
54	GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q]	Nvidia	TU106M	1,558,469	476,300	3.27	7 ^hrs 20 ^mins
55	RTX A2000 GA106 [RTX A2000]	Nvidia	GA106	1,376,474	476,300	2.89	8 ^hrs 18 ^mins
56	P104-100 GP104 [P104-100]	Nvidia	GP104	1,137,963	476,300	2.39	10 ^hrs 3 ^mins
57	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,130,428	476,300	2.37	10 ^hrs 7 ^mins
58	GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile]	Nvidia	TU106M	1,119,657	476,300	2.35	10 ^hrs 13 ^mins
59	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,105,676	917,589	1.20	19 ^hrs 55 ^mins

60	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,089,589	476,300	2.29	10 ^hrs 29 ^mins
61	RTX A1000 GA107GL [RTX A1000]	Nvidia	GA107GL	1,061,336	476,300	2.23	10 ^hrs 46 ^mins
62	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	874,046	865,125	1.01	23 ^hrs 45 ^mins
63	RTX A2000 Mobile GA107GLM [RTX A2000 Mobile]	Nvidia	GA107GLM	754,359	476,300	1.58	15 ^hrs 9 ^mins
64	GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER]	Nvidia	TU116	646,101	814,184	0.79	30 ^hrs 15 ^mins
65	GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile]	Nvidia	TU116M	531,441	895,579	0.59	40 ^hrs 27 ^mins
66	RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M]	AMD	Navi 14	523,265	476,300	1.10	21 ^hrs 51 ^mins
67	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	389,804	583,585	0.67	35 ^hrs 56 ^mins
68	Radeon 740M/760M/780M Phoenix [Radeon 740M/760M/780M]	AMD	Phoenix	254,186	476,300	0.53	44 ^hrs 58 ^mins
69	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	243,774	476,300	0.51	46 ^hrs 54 ^mins
70	GeForce GTX 1650 TU106 [GeForce GTX 1650]	Nvidia	TU106	178,311	476,300	0.37	64 ^hrs 6 ^mins

Research: PEPTIDE-CONFORMATION-MODELING Folding Project #12129

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 12129

GPU PPD Averages

Research: PEPTIDE-CONFORMATION-MODELING Folding Project #12129

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages