RESEARCH: IL-2-DYNAMICS-SIMULATIONS
FOLDING PROJECT #18278 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 24,777
Core: 0x27
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project compares different computer models of human IL-2, a protein that controls immune responses. By using these models to simulate how IL-2 interacts with its receptors, researchers hope to better understand how it works and design improved versions for treating diseases.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL 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.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

interleukin-2

A cytokine that regulates T cell activity.

Scientific: Biotechnology
Immunology / Cytokines

Interleukin-2 (IL-2) is a crucial signaling protein in the immune system. It's essential for the growth and development of T cells, which are vital for fighting infections and diseases. IL-2 can either stimulate or suppress the activity of immune cells depending on the specific receptors it binds to. Researchers are exploring ways to modify IL-2 to enhance its therapeutic potential in treating various conditions.


cytokine

Small proteins that regulate immune responses.

Scientific: Biotechnology
Immunology / Signaling molecules

Cytokines are a type of signaling molecule produced by cells in the immune system. They act like messengers, communicating with other cells to coordinate immune responses. Cytokines can have various effects, such as stimulating inflammation, promoting cell growth, or suppressing immune activity. Understanding cytokines is crucial for developing treatments for immune-related diseases.


T-cell

White blood cells that play a key role in the adaptive immune response.

Scientific: Biotechnology
Immunology / Immune cells

T cells are a type of white blood cell that plays a vital role in the body's immune system. They recognize and attack specific pathogens, such as viruses and bacteria. T cells come in different types, including helper T cells, which activate other immune cells, and cytotoxic T cells, which directly kill infected cells. Understanding T cell function is crucial for developing treatments for infectious diseases, autoimmune disorders, and cancer.


Nuclear Magnetic Resonance (NMR)

A technique used to study the structure and dynamics of molecules.

Scientific: Pharmaceutical Research
Biochemistry / Spectroscopy

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique used to study the structure, dynamics, and interactions of molecules. It involves applying magnetic fields and radio waves to atomic nuclei in a sample, which causes them to resonate at specific frequencies. These resonance frequencies provide information about the chemical environment and connectivity of atoms within the molecule.


force field

A mathematical model used to describe the interactions between atoms in a molecule.

Technical: Biotechnology
Computational Chemistry / Molecular Simulations

A force field is a set of mathematical equations that describes how atoms interact with each other in a molecule. These equations are used in computer simulations to predict the behavior of molecules, such as their structure, dynamics, and reactivity. Force fields are essential tools for drug discovery, materials science, and other areas where understanding molecular interactions is crucial.


Simulation

A computer model that mimics the behavior of a system over time.

Technical: Biotechnology
Computational Chemistry / Molecular Dynamics

Simulation involves creating a computer model that represents a real-world system and using it to predict how the system will behave over time. Simulations are widely used in science, engineering, and business to understand complex processes, test hypotheses, and design new products or systems. In computational chemistry, simulations are used to study the behavior of molecules at the atomic level.


amber

A popular software package for molecular simulations.

Technical: Biotechnology
Computational Chemistry / Software

AMBER (Assisted Model Building with Energy Refinement) is a widely used suite of software tools for performing molecular dynamics simulations and other computational chemistry tasks. It's known for its accuracy, flexibility, and extensive capabilities in modeling biomolecules such as proteins and nucleic acids.


tip3p

A widely used model for simulating water molecules.

Technical: Biotechnology
Computational Chemistry / Water Model

TIP3P (Transferable Intermolecular Potential 3-Point) is a commonly employed force field parameter set for describing the interaction between water molecules in molecular simulations. It's known for its good performance in capturing the essential properties of liquid water.


opc

A water model used in molecular simulations.

Technical: Biotechnology
Computational Chemistry / Water Model

OPC (Optimized Potential for Conjugated Systems) is a water model used in molecular dynamics simulations to represent the interaction between water molecules. It's designed to be particularly suitable for simulating systems involving conjugated molecules, such as those found in biological systems.


charmm

A software package for molecular simulations.

Technical: Biotechnology
Computational Chemistry / Software

CHARMM (Chemistry at HARvard Molecular Mechanics) is a widely used software package for performing molecular dynamics simulations and other computational chemistry tasks. It's known for its accuracy, versatility, and extensive capabilities in modeling biomolecules.


aadisp

A water model used in molecular simulations.

Technical: Biotechnology
Computational Chemistry / Water Model

AA-DISP (Atomically Accurate Dispersive Interactions) is a water model developed to accurately represent the dispersion interactions between water molecules. Dispersion interactions are weak forces that arise from instantaneous fluctuations in electron distribution and can significantly influence the behavior of water in complex systems.


tip4p

A water model used in molecular simulations.

Technical: Biotechnology
Computational Chemistry / Water Model

TIP4P (Transferable Intermolecular Potential 4-Point) is a widely used force field parameter set for describing the interaction between water molecules in molecular simulations. It's known for its good performance in capturing the essential properties of liquid water and has been extensively used in various applications.


tip4pd

A water model used in molecular simulations.

Technical: Biotechnology
Computational Chemistry / Water Model

TIP4P-D (Transferable Intermolecular Potential 4-Point with Dispersion Corrections) is an improved version of the TIP4P water model that incorporates dispersion corrections. These corrections account for weak van der Waals interactions between water molecules, leading to a more accurate representation of their behavior in simulations.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 03:30:40
Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 GeForce GTX 1650
TU106 [GeForce GTX 1650]
Nvidia TU106 10,612,941 11,500 922.86 0 hrs 2 mins
2 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 2,949,460 11,500 256.47 0 hrs 6 mins
3 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,105,515 11,500 183.09 0 hrs 8 mins
4 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,994,529 74,269 26.86 0 hrs 54 mins
5 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,839,320 11,500 159.94 0 hrs 9 mins
6 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,727,424 79,551 21.71 1 hrs 6 mins
7 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,726,681 45,703 37.78 0 hrs 38 mins
8 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,575,159 14,780 106.57 0 hrs 14 mins
9 P102-100
GP102 [P102-100]
Nvidia GP102 1,572,432 11,500 136.73 0 hrs 11 mins
10 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,400,045 24,692 56.70 0 hrs 25 mins
11 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,376,429 49,485 27.82 0 hrs 52 mins
12 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,367,935 11,500 118.95 0 hrs 12 mins
13 Radeon Pro W5700
Navi 10 [Radeon Pro W5700]
AMD Navi 10 1,106,434 11,500 96.21 0 hrs 15 mins
14 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,089,062 81,164 13.42 1 hrs 47 mins
15 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,081,152 16,169 66.87 0 hrs 22 mins
16 P104-100
GP104 [P104-100]
Nvidia GP104 1,054,593 11,500 91.70 0 hrs 16 mins
17 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 1,051,701 11,500 91.45 0 hrs 16 mins
18 RX 5600 OEM/5600XT/5700(XT)
Navi 10 [RX 5600 OEM/5600XT/5700(XT)]
AMD Navi 10 1,026,835 11,500 89.29 0 hrs 16 mins
19 P106-100
GP106 [P106-100]
Nvidia GP106 982,201 11,500 85.41 0 hrs 17 mins
20 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 891,076 19,260 46.27 0 hrs 31 mins
21 Tesla P4
GP104GL [Tesla P4] 5704
Nvidia GP104GL 841,578 74,099 11.36 2 hrs 7 mins
22 GeForce GTX 1650 SUPER
TU116 [GeForce GTX 1650 SUPER]
Nvidia TU116 832,643 11,500 72.40 0 hrs 20 mins
23 CMP 30HX
TU116 [CMP 30HX]
Nvidia TU116 775,525 11,500 67.44 0 hrs 21 mins
24 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 590,831 11,500 51.38 0 hrs 28 mins
25 GeForce RTX 3050 6GB Laptop GPU
GN20-P0-R-K2 [GeForce RTX 3050 6GB Laptop GPU]
Nvidia GN20-P0-R-K2 506,758 11,500 44.07 0 hrs 33 mins
26 GeForce GTX 1650
TU116 [GeForce GTX 1650] 3091
Nvidia TU116 457,030 60,842 7.51 3 hrs 12 mins
27 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 452,847 11,500 39.38 0 hrs 37 mins
28 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 412,556 11,500 35.87 0 hrs 40 mins
29 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 402,622 53,241 7.56 3 hrs 10 mins
30 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 373,002 11,500 32.43 0 hrs 44 mins
31 Radeon RX 6400/6500XT
Navi 24 [Radeon RX 6400/6500XT]
AMD Navi 24 370,082 56,484 6.55 3 hrs 40 mins
32 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 356,287 11,500 30.98 0 hrs 46 mins
33 Quadro M5000
GM204GL [Quadro M5000]
Nvidia GM204GL 350,010 11,500 30.44 0 hrs 47 mins
34 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 294,057 42,069 6.99 3 hrs 26 mins
35 Radeon PRO W6400
Navi 24 [Radeon PRO W6400]
AMD Navi 24 281,897 11,500 24.51 0 hrs 59 mins
36 GeForce GTX 1070 Mobile
GP104BM [GeForce GTX 1070 Mobile] 6463
Nvidia GP104BM 281,870 11,500 24.51 0 hrs 59 mins
37 GeForce GTX Titan X
GM200 [GeForce GTX Titan X] 6144
Nvidia GM200 264,722 46,820 5.65 4 hrs 15 mins
38 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 264,686 50,357 5.26 4 hrs 34 mins
39 Quadro K5200
GK110 [Quadro K5200]
Nvidia GK110 261,853 11,500 22.77 1 hrs 3 mins
40 Quadro T400 Mobile
TU117GLM [Quadro T400 Mobile]
Nvidia TU117GLM 234,917 11,500 20.43 1 hrs 10 mins
41 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 172,273 11,500 14.98 1 hrs 36 mins
42 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 101,339 36,855 2.75 8 hrs 44 mins
43 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 86,942 29,954 2.90 8 hrs 16 mins
44 Quadro K620
GM107GL [Quadro K620]
Nvidia GM107GL 73,618 33,275 2.21 10 hrs 51 mins
45 GeForce GT 710
GK208B [GeForce GT 710] 366
Nvidia GK208B 8,015 12,611 0.64 37 hrs 46 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 03:30:40
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make