RESEARCH: IL-2-FORCE-FIELD-BENCHMARKING
FOLDING PROJECT #18279 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 43,881Core: 0x27
Status: Public
Related Projects
TLDR; 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.
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 signaling molecule that regulates T cell activity.
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.
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.
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.
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
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.
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.
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.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:30:38|
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 | 2,587,591 | 27,300 | 94.78 | 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 GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 2,246,406 | 34,480 | 65.15 | 0 hrs 22 mins |
| 5 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super] |
Nvidia | TU106 | 2,159,442 | 148,709 | 14.52 | 1 hrs 39 mins |
| 6 | 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 |
| 7 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,132,988 | 27,300 | 78.13 | 0 hrs 18 mins |
| 8 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,079,925 | 36,332 | 57.25 | 0 hrs 25 mins |
| 9 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 2,002,991 | 27,300 | 73.37 | 0 hrs 20 mins |
| 10 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,972,446 | 131,800 | 14.97 | 1 hrs 36 mins |
| 11 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,880,505 | 119,599 | 15.72 | 1 hrs 32 mins |
| 12 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,487,959 | 110,613 | 13.45 | 1 hrs 47 mins |
| 13 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,464,939 | 27,300 | 53.66 | 0 hrs 27 mins |
| 14 | GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107M | 1,337,786 | 155,468 | 8.60 | 2 hrs 47 mins |
| 15 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,288,986 | 152,975 | 8.43 | 2 hrs 51 mins |
| 16 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,208,803 | 34,220 | 35.32 | 0 hrs 41 mins |
| 17 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,148,928 | 27,300 | 42.09 | 0 hrs 34 mins |
| 18 | RX 5600 OEM/5600XT/5700(XT) Navi 10 [RX 5600 OEM/5600XT/5700(XT)] |
AMD | Navi 10 | 1,040,957 | 27,300 | 38.13 | 0 hrs 38 mins |
| 19 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 914,391 | 27,300 | 33.49 | 0 hrs 43 mins |
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| 20 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 907,123 | 27,300 | 33.23 | 0 hrs 43 mins |
| 21 | CMP 30HX TU116 [CMP 30HX] |
Nvidia | TU116 | 855,378 | 27,300 | 31.33 | 0 hrs 46 mins |
| 22 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 803,723 | 40,896 | 19.65 | 1 hrs 13 mins |
| 23 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 791,442 | 129,243 | 6.12 | 3 hrs 55 mins |
| 24 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 724,821 | 27,300 | 26.55 | 0 hrs 54 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 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 607,277 | 27,300 | 22.24 | 1 hrs 5 mins |
| 28 | GeForce GTX Titan X GM200 [GeForce GTX Titan X] 6144 |
Nvidia | GM200 | 499,273 | 103,112 | 4.84 | 4 hrs 57 mins |
| 29 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 475,238 | 27,300 | 17.41 | 1 hrs 23 mins |
| 30 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 392,542 | 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 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 375,292 | 100,652 | 3.73 | 6 hrs 26 mins |
| 33 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 370,050 | 27,300 | 13.55 | 1 hrs 46 mins |
| 34 | Quadro M5000 GM204GL [Quadro M5000] |
Nvidia | GM204GL | 338,887 | 27,300 | 12.41 | 1 hrs 56 mins |
| 35 | Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT] |
AMD | Navi 24 | 330,345 | 96,552 | 3.42 | 7 hrs 1 mins |
| 36 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 303,028 | 49,637 | 6.10 | 3 hrs 56 mins |
| 37 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 290,306 | 27,300 | 10.63 | 2 hrs 15 mins |
| 38 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 256,539 | 89,205 | 2.88 | 8 hrs 21 mins |
| 39 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 249,625 | 27,300 | 9.14 | 2 hrs 37 mins |
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| 40 | Quadro K5200 GK110 [Quadro K5200] |
Nvidia | GK110 | 239,020 | 27,300 | 8.76 | 2 hrs 44 mins |
| 41 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 186,700 | 27,300 | 6.84 | 3 hrs 31 mins |
| 42 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 64,278 | 39,015 | 1.65 | 14 hrs 34 mins |
| 43 | GeForce GT 710 GK208B [GeForce GT 710] 366 |
Nvidia | GK208B | 4,940 | 27,300 | 0.18 | 132 hrs 38 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:30:38|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|