Folding project #18279, IL-2-FORCE-FIELD-BENCHMARKING research

Research section Research projects and references

Project #18279 overview

Project Summary AI Beta

This project compares computer simulations of human interleukin-2 (IL-2), a molecule that controls immune cells, to real-world data. IL-2 can either boost or weaken the immune system, and scientists are trying to understand how it works so they can design better treatments.

Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms: 43,881
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 signaling molecule that regulates T cell activity.

Scientific: BiotechnologyImmunology / Cytokines

Interleukin-2 (IL-2) is a crucial protein involved in the immune system. It helps control how T cells, a type of white blood cell, behave. IL-2 can either stimulate or suppress T cell activity depending on the receptors it binds to. Researchers are exploring ways to modify IL-2 to make it more specific in its actions, potentially leading to better treatments for diseases.

cytokine

A type of protein that regulates immune responses.

Scientific: BiotechnologyImmunology / Signaling Molecules

Cytokines are small proteins that act as messengers in the immune system. They help cells communicate with each other and coordinate immune responses to infections or injuries. There are many different types of cytokines, each with specific functions. For example, some cytokines promote inflammation, while others suppress it.

T-cell

A type of white blood cell that plays a key role in the immune response.

Scientific: BiotechnologyImmunology / Lymphocytes

T cells are a crucial part of the adaptive immune system. They recognize and destroy infected or cancerous cells. There are different types of T cells, each with specific functions. For example, helper T cells coordinate the immune response, while cytotoxic T cells directly kill infected cells.

receptor

A protein that binds to a specific molecule (ligand) and triggers a cellular response.

Scientific: BiotechnologyBiology / Cellular Signaling

Receptors are proteins found on the surface of cells. They act like antennas, receiving signals from outside the cell. When a receptor binds to its specific ligand, it initiates a series of events inside the cell, leading to a particular response. For example, hormone receptors trigger changes in gene expression, while neurotransmitter receptors control nerve impulses.

NMR spectroscopy

Nuclear Magnetic Resonance Spectroscopy

Scientific: BiotechnologyBiochemistry / Structural Analysis

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 provide information about the arrangement of atoms within a molecule. NMR spectroscopy is widely used in biochemistry, drug discovery, and materials science.

force field

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

Scientific: BiotechnologyComputational Chemistry / Molecular Simulations

Force fields are essential tools for molecular simulations. They provide a simplified model of how atoms interact with each other. By using force fields, scientists can simulate the behavior of molecules over time and study their properties. For example, force fields can be used to predict the shape of a protein or the binding affinity of a drug molecule.

simulations

Computer-based models that mimic the behavior of molecules.

Scientific: BiotechnologyComputational Chemistry / Molecular Dynamics

Simulations are a powerful tool for studying complex systems. In computational chemistry, simulations are used to model the behavior of molecules over time. By running simulations, scientists can gain insights into molecular structure, dynamics, and interactions. For example, simulations can be used to study protein folding, drug binding, or chemical reactions.

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	3,859,647	27,300	141.38	0 ^hrs 10 ^mins
2	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,599,005	27,300	95.20	0 ^hrs 15 ^mins
3	TITAN Xp GP102 [TITAN Xp] 12150	Nvidia	GP102	2,341,965	27,300	85.79	0 ^hrs 17 ^mins
4	GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh]	Nvidia	TU106M	2,144,954	27,300	78.57	0 ^hrs 18 ^mins
5	Quadro RTX 4000 TU104GL [Quadro RTX 4000]	Nvidia	TU104GL	2,132,988	27,300	78.13	0 ^hrs 18 ^mins
6	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super]	Nvidia	TU106	2,081,022	124,777	16.68	1 ^hrs 26 ^mins
7	GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q]	Nvidia	GA106M	2,053,890	48,971	41.94	0 ^hrs 34 ^mins
8	P102-100 GP102 [P102-100]	Nvidia	GP102	2,002,991	27,300	73.37	0 ^hrs 20 ^mins
9	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,911,816	166,895	11.46	2 ^hrs 6 ^mins
10	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,769,524	143,092	12.37	1 ^hrs 56 ^mins
11	GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti]	Nvidia	TU116	1,464,939	27,300	53.66	0 ^hrs 27 ^mins
12	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,351,694	128,185	10.54	2 ^hrs 17 ^mins
13	GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile]	Nvidia	GA107M	1,337,786	155,468	8.60	2 ^hrs 47 ^mins
14	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,197,692	146,751	8.16	2 ^hrs 56 ^mins
15	P104-100 GP104 [P104-100]	Nvidia	GP104	1,148,928	27,300	42.09	0 ^hrs 34 ^mins
16	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	1,114,779	32,118	34.71	0 ^hrs 41 ^mins
17	RX 5600 OEM/5600XT/5700(XT) Navi 10 [RX 5600 OEM/5600XT/5700(XT)]	AMD	Navi 10	1,052,402	27,300	38.55	0 ^hrs 37 ^mins
18	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,019,062	39,113	26.05	0 ^hrs 55 ^mins
19	P106-100 GP106 [P106-100]	Nvidia	GP106	947,578	27,300	34.71	0 ^hrs 41 ^mins

20	GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER]	Nvidia	TU116	914,391	27,300	33.49	0 ^hrs 43 ^mins
21	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	907,123	27,300	33.23	0 ^hrs 43 ^mins
22	CMP 30HX TU116 [CMP 30HX]	Nvidia	TU116	855,378	27,300	31.33	0 ^hrs 46 ^mins
23	Tesla P4 GP104GL [Tesla P4] 5704	Nvidia	GP104GL	791,442	129,243	6.12	3 ^hrs 55 ^mins
24	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	756,713	56,657	13.36	1 ^hrs 48 ^mins
25	GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile]	Nvidia	TU106M	669,669	27,300	24.53	0 ^hrs 59 ^mins
26	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	619,857	27,300	22.71	1 ^hrs 3 ^mins
27	GeForce GTX Titan X GM200 [GeForce GTX Titan X] 6144	Nvidia	GM200	499,273	103,112	4.84	4 ^hrs 57 ^mins
28	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	475,238	27,300	17.41	1 ^hrs 23 ^mins
29	GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862	Nvidia	GP107	393,597	27,300	14.42	1 ^hrs 40 ^mins
30	Radeon PRO W6400 Navi 24 [Radeon PRO W6400]	AMD	Navi 24	392,658	27,300	14.38	1 ^hrs 40 ^mins
31	GeForce GTX 1650 TU106 [GeForce GTX 1650]	Nvidia	TU106	391,328	27,300	14.33	1 ^hrs 40 ^mins
32	RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M]	AMD	Navi 14	387,442	27,300	14.19	1 ^hrs 41 ^mins
33	Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile]	Nvidia	TU117GLM	375,292	100,652	3.73	6 ^hrs 26 ^mins
34	Quadro M5000 GM204GL [Quadro M5000]	Nvidia	GM204GL	337,708	27,300	12.37	1 ^hrs 56 ^mins
35	Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT]	AMD	Navi 24	321,440	95,901	3.35	7 ^hrs 10 ^mins
36	Radeon RX 6400 / 6500 XT Navi 24 [Radeon RX 6400 / 6500 XT]	AMD	Navi 24	300,823	27,300	11.02	2 ^hrs 11 ^mins
37	GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463	Nvidia	GP104BM	298,003	27,300	10.92	2 ^hrs 12 ^mins
38	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	285,781	52,187	5.48	4 ^hrs 23 ^mins
39	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	257,818	81,260	3.17	7 ^hrs 34 ^mins

40	Quadro K5200 GK110 [Quadro K5200]	Nvidia	GK110	238,678	27,300	8.74	2 ^hrs 45 ^mins
41	Quadro T400 Mobile TU117GLM [Quadro T400 Mobile]	Nvidia	TU117GLM	206,402	27,300	7.56	3 ^hrs 10 ^mins
42	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	147,359	27,300	5.40	4 ^hrs 27 ^mins
43	GeForce GT 1030 GP108 [GeForce GT 1030]	Nvidia	GP108	70,235	43,862	1.60	14 ^hrs 59 ^mins
44	GeForce GT 710 GK208B [GeForce GT 710] 366	Nvidia	GK208B	5,001	27,300	0.18	131 ^hrs 1 ^mins

Research: IL-2-FORCE-FIELD-BENCHMARKING Folding Project #18279

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 18279

GPU PPD Averages

Research: IL-2-FORCE-FIELD-BENCHMARKING Folding Project #18279

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages