RESEARCH: IL-2-MOLECULAR-DYNAMICS
FOLDING PROJECT #18282 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 32,721Core: 0x27
Status: Public
Related Projects
TLDR; 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.
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 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.
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.
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.
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
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.
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.
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.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:30:34|
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 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,707,947 | 129,566 | 13.18 | 1 hrs 49 mins |
| 11 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,678,676 | 18,400 | 91.23 | 0 hrs 16 mins |
| 12 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,663,592 | 18,400 | 90.41 | 0 hrs 16 mins |
| 13 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,571,081 | 117,957 | 13.32 | 1 hrs 48 mins |
| 14 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,297,495 | 86,156 | 15.06 | 1 hrs 36 mins |
| 15 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,217,417 | 18,400 | 66.16 | 0 hrs 22 mins |
| 16 | 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 |
| 17 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,194,401 | 22,328 | 53.49 | 0 hrs 27 mins |
| 18 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,170,329 | 18,400 | 63.60 | 0 hrs 23 mins |
| 19 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 994,032 | 18,400 | 54.02 | 0 hrs 27 mins |
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| 20 | 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 |
| 21 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 977,465 | 18,400 | 53.12 | 0 hrs 27 mins |
| 22 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 892,915 | 18,400 | 48.53 | 0 hrs 30 mins |
| 23 | GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB] |
Nvidia | GA107 | 842,909 | 18,400 | 45.81 | 0 hrs 31 mins |
| 24 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 832,387 | 101,131 | 8.23 | 2 hrs 55 mins |
| 25 | CMP 30HX TU116 [CMP 30HX] |
Nvidia | TU116 | 830,185 | 18,400 | 45.12 | 0 hrs 32 mins |
| 26 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 705,817 | 18,400 | 38.36 | 0 hrs 38 mins |
| 27 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 690,585 | 18,400 | 37.53 | 0 hrs 38 mins |
| 28 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 665,346 | 34,299 | 19.40 | 1 hrs 14 mins |
| 29 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 592,145 | 18,400 | 32.18 | 0 hrs 45 mins |
| 30 | GeForce RTX 3050 Ti Mobile GA107BM [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107BM | 569,439 | 18,400 | 30.95 | 0 hrs 47 mins |
| 31 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 398,233 | 18,400 | 21.64 | 1 hrs 7 mins |
| 32 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 396,201 | 18,400 | 21.53 | 1 hrs 7 mins |
| 33 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 395,964 | 18,400 | 21.52 | 1 hrs 7 mins |
| 34 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 369,832 | 75,404 | 4.90 | 4 hrs 54 mins |
| 35 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 355,166 | 18,400 | 19.30 | 1 hrs 15 mins |
| 36 | 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 |
| 37 | Quadro M5000 GM204GL [Quadro M5000] |
Nvidia | GM204GL | 333,664 | 18,400 | 18.13 | 1 hrs 19 mins |
| 38 | Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT] |
AMD | Navi 24 | 304,214 | 64,696 | 4.70 | 5 hrs 6 mins |
| 39 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 266,830 | 28,197 | 9.46 | 2 hrs 32 mins |
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| 40 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 257,200 | 68,567 | 3.75 | 6 hrs 24 mins |
| 41 | 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 |
| 42 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 214,823 | 18,400 | 11.68 | 2 hrs 3 mins |
| 43 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 114,745 | 18,400 | 6.24 | 3 hrs 51 mins |
| 44 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 94,183 | 49,892 | 1.89 | 12 hrs 43 mins |
| 45 | Radeon 760M/780M Phoenix/Hawk Point [Radeon 760M/780M] |
AMD | Phoenix/Hawk Point | 64,315 | 18,400 | 3.50 | 6 hrs 52 mins |
| 46 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 62,154 | 29,703 | 2.09 | 11 hrs 28 mins |
| 47 | GeForce GT 730 GK208B [GeForce GT 730] 692.7 |
Nvidia | GK208B | 21,436 | 18,400 | 1.17 | 20 hrs 36 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:30:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|