Folding project #18282, IL-2-MOLECULAR-DYNAMICS research

Research section Research projects and references

Project #18282 overview

Project Summary AI Beta

This project studies how different computer models simulate the human protein IL-2, which helps regulate our immune system. By comparing these simulations to real-world data, researchers hope to better understand how IL-2 works and design new versions that could be used as medicines.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Justin Miller
Institution: University of Pennsylvania

Work unit

Atoms: 32,721
Core: 0x27
Status: Public

Source material

Official Project Description

As part of our ongoing effort to benchmark the most popular force field and water combinations, this project series focuses on human interleukin-2 (IL-2).

Our aim is to compare the results of these simulations to experimental nuclear magnetic resonance (NMR) spectroscopy data. Human interleukin-2 (IL-2) is an important signaling molecule, or cytokine, for the regulation of T-cell activity.

IL-2 can act as a promotor or inhibitor in immune cells depending on which of its receptors are bound.

There have been efforts to modify IL-2’s receptor binding sites to bias its activity towards either promoting or inhibiting immune cells.

However, the dynamics underlying IL-2 receptor recognition and binding are still not fully understood.

In addition to quantifying force field accuracy, a better understanding of these dynamics will help design IL-2 variants with more specific activity, thus improving its potential as a therapeutic.
p18278- amber03 with tip3p water 18279 - amber19sb with opc water p18280- amber99sb-disp with aadisp water p18281- charmm36m with tip3p water p18282- amber99sb-star-ILDN with tip4p water p18283- amber99sb-star-ILDN with tip4pd water.

Reading reference

Related Terms Glossary AI Beta

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

interleukin-2

A cytokine that regulates T-cell activity.

Scientific: PharmaceuticalsBiotechnology / Immunology

Interleukin-2 (IL-2) is a signaling molecule crucial for the immune system. It stimulates the growth and activity of T cells, which are essential for fighting infections and diseases. Research focuses on understanding IL-2's role in immune responses and developing therapies that harness its power.

cytokine

A type of signaling protein produced by cells.

Scientific: PharmaceuticalsBiotechnology / Immunology

Cytokines are small proteins that act as messengers between cells, particularly in the immune system. They regulate various immune responses, such as inflammation, cell growth, and differentiation. Cytokines play a vital role in fighting infections and maintaining immune homeostasis.

T-cell

A type of white blood cell involved in immune responses.

Scientific: PharmaceuticalsBiotechnology / Immunology

T cells are a crucial part of the adaptive immune system. They recognize and destroy infected or abnormal cells, regulate other immune cells, and provide long-lasting immunity against specific pathogens. There are different types of T cells, each with specialized functions.

receptor

A protein that binds to a specific molecule, triggering a cellular response.

Scientific: PharmaceuticalsBiotechnology / Molecular Biology

Receptors are proteins found on the surface of cells that bind to specific molecules, such as hormones, neurotransmitters, or drugs. This binding triggers a cascade of events inside the cell, leading to a specific response. Receptors play a vital role in cell communication and signal transduction.

NMR spectroscopy

Nuclear Magnetic Resonance Spectroscopy

Scientific: PharmaceuticalsBiotechnology / Structural Biology

NMR spectroscopy is a powerful technique used to study the structure and dynamics of molecules. It relies on the magnetic properties of atomic nuclei to determine the spatial arrangement and interactions within a molecule.

force field

A set of mathematical equations that describe the interactions between atoms in a molecule.

Scientific: PharmaceuticalsBiotechnology / Computational Biology

Force fields are essential tools in computational biology and drug discovery. They allow scientists to simulate molecular behavior and predict how molecules will interact with each other. By refining force fields, researchers can improve the accuracy of their simulations.

simulation

A computer-based model of a real-world process.

Scientific: PharmaceuticalsBiotechnology / Computational Biology

Simulations are powerful tools used in various fields, including biotechnology and drug discovery. They allow researchers to study complex systems and predict their behavior under different conditions. By running simulations, scientists can gain insights into biological processes and test hypotheses.

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	3,310,987	18,400	179.94	0 ^hrs 8 ^mins
2	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super]	Nvidia	TU106	2,312,332	32,862	70.36	0 ^hrs 20 ^mins
3	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,223,441	18,400	120.84	0 ^hrs 12 ^mins
4	Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics]	Intel	Battlemage G21	2,168,334	23,259	93.23	0 ^hrs 15 ^mins
5	TITAN Xp GP102 [TITAN Xp] 12150	Nvidia	GP102	1,970,362	18,400	107.08	0 ^hrs 13 ^mins
6	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	1,920,246	27,852	68.94	0 ^hrs 21 ^mins
7	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	1,776,454	18,400	96.55	0 ^hrs 15 ^mins
8	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,759,658	130,404	13.49	1 ^hrs 47 ^mins
9	Quadro RTX 4000 TU104GL [Quadro RTX 4000]	Nvidia	TU104GL	1,722,671	117,961	14.60	1 ^hrs 39 ^mins
10	P102-100 GP102 [P102-100]	Nvidia	GP102	1,678,676	18,400	91.23	0 ^hrs 16 ^mins
11	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,571,081	117,957	13.32	1 ^hrs 48 ^mins
12	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,377,048	18,400	74.84	0 ^hrs 19 ^mins
13	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,297,495	86,156	15.06	1 ^hrs 36 ^mins
14	P104-100 GP104 [P104-100]	Nvidia	GP104	1,217,417	18,400	66.16	0 ^hrs 22 ^mins
15	GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q]	Nvidia	TU106M	1,214,067	18,400	65.98	0 ^hrs 22 ^mins
16	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,194,401	22,328	53.49	0 ^hrs 27 ^mins
17	GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile]	Nvidia	TU106M	1,170,329	18,400	63.60	0 ^hrs 23 ^mins
18	RX 5600 OEM/5600XT/5700(XT) Navi 10 [RX 5600 OEM/5600XT/5700(XT)]	AMD	Navi 10	985,859	18,400	53.58	0 ^hrs 27 ^mins
19	P106-100 GP106 [P106-100]	Nvidia	GP106	977,465	18,400	53.12	0 ^hrs 27 ^mins

20	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	936,633	18,400	50.90	0 ^hrs 28 ^mins
21	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	892,915	18,400	48.53	0 ^hrs 30 ^mins
22	GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB]	Nvidia	GA107	842,909	18,400	45.81	0 ^hrs 31 ^mins
23	Tesla P4 GP104GL [Tesla P4] 5704	Nvidia	GP104GL	832,387	101,131	8.23	2 ^hrs 55 ^mins
24	CMP 30HX TU116 [CMP 30HX]	Nvidia	TU116	830,185	18,400	45.12	0 ^hrs 32 ^mins
25	GeForce GTX 980 GM204 [GeForce GTX 980] 4612	Nvidia	GM204	705,817	18,400	38.36	0 ^hrs 38 ^mins
26	Radeon Pro W5700 Navi 10 [Radeon Pro W5700]	AMD	Navi 10	690,585	18,400	37.53	0 ^hrs 38 ^mins
27	GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091	Nvidia	TU116	665,346	34,299	19.40	1 ^hrs 14 ^mins
28	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	592,145	18,400	32.18	0 ^hrs 45 ^mins
29	GeForce RTX 3050 Ti Mobile GA107BM [GeForce RTX 3050 Ti Mobile]	Nvidia	GA107BM	569,439	18,400	30.95	0 ^hrs 47 ^mins
30	GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463	Nvidia	GP104BM	398,233	18,400	21.64	1 ^hrs 7 ^mins
31	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	396,201	18,400	21.53	1 ^hrs 7 ^mins
32	GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862	Nvidia	GP107	395,964	18,400	21.52	1 ^hrs 7 ^mins
33	Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile]	Nvidia	TU117GLM	369,832	75,404	4.90	4 ^hrs 54 ^mins
34	Radeon PRO W6400 Navi 24 [Radeon PRO W6400]	AMD	Navi 24	355,166	18,400	19.30	1 ^hrs 15 ^mins
35	RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M]	AMD	Navi 14	335,369	18,400	18.23	1 ^hrs 19 ^mins
36	Quadro M5000 GM204GL [Quadro M5000]	Nvidia	GM204GL	333,664	18,400	18.13	1 ^hrs 19 ^mins
37	Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT]	AMD	Navi 24	304,214	64,696	4.70	5 ^hrs 6 ^mins
38	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	266,830	28,197	9.46	2 ^hrs 32 ^mins
39	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	257,200	68,567	3.75	6 ^hrs 24 ^mins

40	GeForce RTX 3050 6GB Laptop GPU GN20-P0-R-K2 [GeForce RTX 3050 6GB Laptop GPU]	Nvidia	GN20-P0-R-K2	222,044	18,400	12.07	1 ^hrs 59 ^mins
41	Quadro T400 Mobile TU117GLM [Quadro T400 Mobile]	Nvidia	TU117GLM	214,823	18,400	11.68	2 ^hrs 3 ^mins
42	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	114,745	18,400	6.24	3 ^hrs 51 ^mins
43	Quadro K1200 GM107GL [Quadro K1200]	Nvidia	GM107GL	94,183	49,892	1.89	12 ^hrs 43 ^mins
44	Radeon 760M/780M Phoenix/Hawk Point [Radeon 760M/780M]	AMD	Phoenix/Hawk Point	64,315	18,400	3.50	6 ^hrs 52 ^mins
45	GeForce GT 1030 GP108 [GeForce GT 1030]	Nvidia	GP108	62,154	29,703	2.09	11 ^hrs 28 ^mins
46	GeForce GT 730 GK208B [GeForce GT 730] 692.7	Nvidia	GK208B	21,436	18,400	1.17	20 ^hrs 36 ^mins

Research: IL-2-MOLECULAR-DYNAMICS Folding Project #18282

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 18282

GPU PPD Averages

Research: IL-2-MOLECULAR-DYNAMICS Folding Project #18282

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages