Folding project #17654, PROTEIN-DYNAMICS-MODELING research

Research section Research projects and references

Project #17654 overview

Project Summary AI Beta

The project aims to create a huge dataset of how proteins move and change shape. This data will help scientists develop new AI tools that can predict protein behavior, leading to advances in medicine and biotechnology. The dataset includes proteins of different sizes, from small (20,000 atoms) to large (70,000 atoms).

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Sukrit Singh
Institution: Memorial Sloan-Kettering Cancer-Center
Website: Open project site

Work unit

Atoms: 40,000
Core: 0x26
Status: Public

Source material

Official Project Description

With the explosion of AI-based models and architectures, a ripe opportunity presents itself to use these statistical models to help us bridge the gap large scale simulations and functional insight.

In particular, with the explosion of methods like AlphaFold2, there is a clear potential for these models to potentially predict dynamics, or use them to predict different conformations of a system at extremely large scales for a diverse set of sequences. However, for folks to be able to generate those kinds of models, a broad set of training data is needed that captures dynamics across a variety of different protein topologies.

This project seeks to generate that dataset - capturing dynamics of systems across a variety of different protein sizes and topologies.

17651: Small proteins (20,000 atoms)
17652 and 17654: medium sized proteins (40,000 atoms)
17653 and 17655: Large sized proteins (70,000 atoms).

Reading reference

Related Terms Glossary AI Beta

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

AI

Artificial Intelligence

Technical: TechnologyComputer Science / Machine Learning

AI refers to computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Models

Mathematical representations used to understand and predict phenomena.

Technical: TechnologyComputer Science / Machine Learning

Models in computer science are simplified representations of complex systems. They are used to simulate behavior, make predictions, and gain insights into how things work.

AlphaFold2

A deep learning algorithm for predicting protein structures.

Technical: HealthcareBiotechnology / Protein Structure Prediction

AlphaFold2 is a groundbreaking AI system that uses machine learning to accurately predict the three-dimensional structure of proteins. This has immense implications for understanding diseases and developing new drugs.

Protein

Large biomolecules essential for all living organisms.

Scientific: HealthcareBiochemistry / Molecular Biology

Proteins are complex molecules that play crucial roles in almost every biological process. They are involved in building tissues, transporting molecules, catalyzing reactions, and much more.

Dynamics

The movement and interactions of molecules over time.

Scientific: HealthcareBiochemistry / Structural Biology

Dynamics refers to the constant motion and changes in the structure of molecules. In biochemistry, it is particularly important for understanding how proteins function and interact with their environment.

Conformations

Different shapes that a molecule can adopt.

Scientific: HealthcareBiochemistry / Structural Biology

Conformations describe the various 3-dimensional arrangements that a molecule can take. For proteins, different conformations can affect their function and interactions.

Topology

The arrangement of atoms or molecules in a 3-dimensional structure.

Scientific: HealthcareBiochemistry / Structural Biology

Topology refers to the overall shape and connectivity of molecules. In protein science, it is important for understanding how proteins fold and interact.

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 4080 SUPER AD103 [GeForce RTX 4080 SUPER]	Nvidia	AD103	14,418,603	6,947	2075.52	0 ^hrs 1 ^mins
2	GeForce RTX 4080 AD103 [GeForce RTX 4080]	Nvidia	AD103	13,848,301	9,200	1505.25	0 ^hrs 1 ^mins
3	GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti]	Nvidia	AD104	11,819,633	21,205	557.40	0 ^hrs 3 ^mins
4	GeForce RTX 5090 GB202 [GeForce RTX 5090]	Nvidia	GB202	11,419,160	6,947	1643.75	0 ^hrs 1 ^mins
5	GeForce RTX 5080 GB203 [GeForce RTX 5080]	Nvidia	GB203	10,396,163	6,947	1496.50	0 ^hrs 1 ^mins
6	GeForce RTX 4090 AD102 [GeForce RTX 4090]	Nvidia	AD102	9,520,159	61,064	155.90	0 ^hrs 9 ^mins
7	GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti]	Nvidia	GB203	8,760,331	6,947	1261.02	0 ^hrs 1 ^mins
8	GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER]	Nvidia	AD104	8,085,605	62,806	128.74	0 ^hrs 11 ^mins
9	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	5,976,291	6,947	860.27	0 ^hrs 2 ^mins
10	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	5,898,762	6,947	849.11	0 ^hrs 2 ^mins
11	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	5,770,310	6,947	830.62	0 ^hrs 2 ^mins
12	GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti]	Nvidia	AD106	5,413,222	102,066	53.04	0 ^hrs 27 ^mins
13	RTX 4000 SFF Ada Generation AD104GL [RTX 4000 SFF Ada Generation]	Nvidia	AD104GL	5,153,555	6,947	741.84	0 ^hrs 2 ^mins
14	GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate]	Nvidia	GA104	4,754,969	93,805	50.69	0 ^hrs 28 ^mins
15	GeForce RTX 4070 AD104 [GeForce RTX 4070]	Nvidia	AD104	4,748,965	46,445	102.25	0 ^hrs 14 ^mins
16	TITAN V GV100 [TITAN V] M 12288	Nvidia	GV100	4,354,105	6,947	626.76	0 ^hrs 2 ^mins
17	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	4,267,326	6,947	614.27	0 ^hrs 2 ^mins
18	Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE]	AMD	Navi 31	3,294,840	68,950	47.79	0 ^hrs 30 ^mins
19	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	3,024,572	80,118	37.75	0 ^hrs 38 ^mins

20	Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT]	AMD	Navi 32	2,999,782	6,947	431.81	0 ^hrs 3 ^mins
21	Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)]	AMD	Navi 48	2,952,501	6,947	425.00	0 ^hrs 3 ^mins
22	GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate]	Nvidia	GA104	2,697,070	6,947	388.24	0 ^hrs 4 ^mins
23	GeForce RTX 2060 12GB TU106 [GeForce RTX 2060 12GB]	Nvidia	TU106	2,649,588	6,947	381.40	0 ^hrs 4 ^mins
24	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,445,349	31,084	78.67	0 ^hrs 18 ^mins
25	GeForce RTX 2070 TU106 [GeForce RTX 2070]	Nvidia	TU106	2,441,394	6,947	351.43	0 ^hrs 4 ^mins
26	Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT]	AMD	Navi 21	2,349,291	45,295	51.87	0 ^hrs 28 ^mins
27	Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]	AMD	Navi 22 XT-XL	2,173,041	6,947	312.80	0 ^hrs 5 ^mins
28	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	2,166,589	39,997	54.17	0 ^hrs 27 ^mins
29	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	2,162,713	72,399	29.87	0 ^hrs 48 ^mins
30	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	2,071,786	72,450	28.60	0 ^hrs 50 ^mins
31	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	2,070,747	6,947	298.08	0 ^hrs 5 ^mins
32	GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]	Nvidia	TU106M	2,049,305	70,105	29.23	0 ^hrs 49 ^mins
33	GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB]	Nvidia	GA107	2,046,693	70,698	28.95	0 ^hrs 50 ^mins
34	GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate]	Nvidia	GA106	1,920,922	68,381	28.09	0 ^hrs 51 ^mins
35	RTX A2000 12GB GA106 [RTX A2000 12GB]	Nvidia	GA106	1,752,188	6,947	252.22	0 ^hrs 6 ^mins
36	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	1,559,489	61,468	25.37	0 ^hrs 57 ^mins
37	Radeon RX 7700S/7600S Navi 33 [Radeon RX 7700S/7600S]	AMD	Navi 33	1,432,483	6,947	206.20	0 ^hrs 7 ^mins
38	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,387,118	38,680	35.86	0 ^hrs 40 ^mins
39	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,384,029	62,809	22.04	1 ^hrs 5 ^mins

40	Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]	AMD	Navi 22 XT-XL	1,356,856	17,062	79.53	0 ^hrs 18 ^mins
41	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,310,058	37,351	35.07	0 ^hrs 41 ^mins
42	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,229,065	6,947	176.92	0 ^hrs 8 ^mins
43	GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB]	Nvidia	GA107	1,229,001	6,947	176.91	0 ^hrs 8 ^mins
44	P106-100 GP106 [P106-100]	Nvidia	GP106	1,126,640	54,603	20.63	1 ^hrs 10 ^mins
45	Radeon RX 6600/6600 XT/6600M Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M]	AMD	Navi 23 XT-XL	1,033,479	33,530	30.82	0 ^hrs 47 ^mins
46	Quadro P3200 Mobile GP104GLM [Quadro P3200 Mobile]	Nvidia	GP104GLM	1,009,644	6,947	145.34	0 ^hrs 10 ^mins
47	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	960,245	6,947	138.22	0 ^hrs 10 ^mins
48	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	940,680	6,947	135.41	0 ^hrs 11 ^mins
49	GeForce GTX 980 GM204 [GeForce GTX 980] 4612	Nvidia	GM204	784,268	6,947	112.89	0 ^hrs 13 ^mins
50	RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT]	AMD	Navi 10	779,195	51,411	15.16	1 ^hrs 35 ^mins
51	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	749,146	50,068	14.96	1 ^hrs 36 ^mins
52	Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile]	Nvidia	TU117GLM	738,766	6,947	106.34	0 ^hrs 14 ^mins
53	Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64]	AMD	Vega 10 XL/XT	731,346	6,947	105.28	0 ^hrs 14 ^mins
54	GeForce GTX Titan X GM200 [GeForce GTX Titan X] 6144	Nvidia	GM200	723,315	6,947	104.12	0 ^hrs 14 ^mins
55	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	718,450	6,947	103.42	0 ^hrs 14 ^mins
56	RX 5500/5500M/Pro 5500M Navi 14 [RX 5500/5500M/Pro 5500M]	AMD	Navi 14	642,384	48,637	13.21	1 ^hrs 49 ^mins
57	Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT]	AMD	Navi 24	501,205	6,947	72.15	0 ^hrs 20 ^mins
58	RX 470/480/570/580/590 Ellesmere XT [RX 470/480/570/580/590]	AMD	Ellesmere XT	451,824	43,682	10.34	2 ^hrs 19 ^mins
59	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	433,191	42,196	10.27	2 ^hrs 20 ^mins

60	R9 Fury X/NANO Fiji XT [R9 Fury X/NANO]	AMD	Fiji XT	398,882	6,947	57.42	0 ^hrs 25 ^mins
61	GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862	Nvidia	GP107	386,732	41,087	9.41	2 ^hrs 33 ^mins
62	Radeon 660M-680M Rembrandt [Radeon 660M-680M]	AMD	Rembrandt	254,717	6,947	36.67	0 ^hrs 39 ^mins
63	RX Vega M GL Polaris 22 XL [RX Vega M GL]	AMD	Polaris 22 XL	177,855	6,947	25.60	0 ^hrs 56 ^mins
64	GeForce GTX 670/GTX 760Ti OEM GK104 [GeForce GTX 670/GTX 760Ti OEM] 2634	Nvidia	GK104	169,510	6,947	24.40	0 ^hrs 59 ^mins
65	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	167,658	30,684	5.46	4 ^hrs 24 ^mins
66	GeForce GT 1030 GP108 [GeForce GT 1030]	Nvidia	GP108	123,177	6,947	17.73	1 ^hrs 21 ^mins
67	Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU]	AMD	Raphael	121,863	16,394	7.43	3 ^hrs 14 ^mins
68	Quadro K1200 GM107GL [Quadro K1200]	Nvidia	GM107GL	121,169	27,713	4.37	5 ^hrs 29 ^mins
69	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	98,560	11,863	8.31	2 ^hrs 53 ^mins

CPU PPD Averages Beta

Rank Project	CPU Model	Logical Processors (LP)	PPD-PLP AVG PPD per 1 LP	ALL LP-PPD (Estimated)	Make
1	RYZEN 7 7700X 8-CORE	16	31,690	507,040	AMD

Research: PROTEIN-DYNAMICS-MODELING Folding Project #17654

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 17654

GPU PPD Averages

CPU PPD Averages Beta

Research: PROTEIN-DYNAMICS-MODELING Folding Project #17654

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages

CPU PPD Averages Beta