RESEARCH: IL-2-DYNAMICS-SIMULATIONS
FOLDING PROJECT #18278 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 24,777Core: 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
interleukin-2
A cytokine that regulates T cell activity.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 |
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| 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 |
|
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| 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 |
|---|