RESEARCH: IL-2-RECEPTOR-DYNAMICS
FOLDING PROJECT #18281 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 24,774Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how well different computer models can simulate the behavior of a molecule called interleukin-2 (IL-2), which is important for the immune system. By comparing the model results to real-world experiments, scientists 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
Human interleukin-2 (IL-2) is a cytokine that regulates T-cell activity.
Interleukin-2 (IL-2) is a crucial protein in the immune system. It acts as a signal between cells to help them fight infections and diseases. IL-2 can either boost or suppress the activity of certain immune cells, depending on which receptors it binds to. Scientists are studying IL-2 to develop new treatments for various conditions, including cancer and autoimmune disorders.
nuclear magnetic resonance
NMR
Nuclear Magnetic Resonance (NMR) is a powerful analytical technique used to study the structure and dynamics of molecules. It works by exposing a sample to a strong magnetic field and measuring how the atomic nuclei respond. This information can reveal details about the molecule's shape, composition, and interactions with other molecules.
cytokine
A cytokine is a type of small protein that cells use to communicate with each other.
Cytokines are messenger molecules that play a vital role in the immune system. They allow different types of immune cells to communicate and coordinate their responses to infections, injuries, and other threats. Cytokines can stimulate inflammation, promote cell growth, or suppress immune activity, depending on their specific function.
T-cell
A T-cell is a type of white blood cell that plays a crucial role in the immune system.
T-cells are specialized lymphocytes that defend the body against infections and diseases. They recognize specific antigens (foreign substances) on infected cells or pathogens and eliminate them through various mechanisms, such as releasing cytotoxic chemicals or activating other immune cells.
receptor
A receptor is a protein that binds to a specific molecule, triggering a cellular response.
Receptors are specialized proteins located on the surface or inside of cells. They act as binding sites for specific molecules, such as hormones, neurotransmitters, or drugs. When a molecule binds to its receptor, it initiates a cascade of events within the cell, leading to various responses, including changes in gene expression, cell growth, or movement.
force field
A force field is a set of mathematical equations that describes the interactions between atoms in a molecule.
Force fields are essential tools for molecular simulations, which are computer-based models that simulate the behavior of molecules. They allow scientists to predict how molecules will interact with each other and their environment, providing insights into various biological processes.
simulation
A simulation is a computer-based model that replicates a real-world system.
Simulations are powerful tools used in various scientific disciplines to understand complex systems. They allow researchers to explore different scenarios, test hypotheses, and gain insights into the behavior of systems that are difficult or impossible to study experimentally.
therapeutic
A therapeutic refers to a treatment or medication intended to alleviate a disease or condition.
Therapies are medical interventions designed to treat and manage diseases or health conditions. They can range from medications and surgery to physical therapy and lifestyle changes.
variant
A variant is a form of a gene or protein that differs from the original sequence.
Variants are alterations in the DNA sequence that can result in different forms of genes or proteins. They can arise through mutations or genetic recombination and may have various effects on an organism's traits or health.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:30:35|
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 | 3,504,418 | 20,300 | 172.63 | 0 hrs 8 mins |
| 2 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 3,074,020 | 22,240 | 138.22 | 0 hrs 10 mins |
| 3 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,232,220 | 20,300 | 109.96 | 0 hrs 13 mins |
| 4 | Tesla P100 16GB GP100GL [Tesla P100 16GB] 9340 |
Nvidia | GP100GL | 2,166,492 | 20,300 | 106.72 | 0 hrs 13 mins |
| 5 | Quadro RTX 4000 Mobile / Max-Q TU104GLM [Quadro RTX 4000 Mobile / Max-Q] |
Nvidia | TU104GLM | 2,137,592 | 20,300 | 105.30 | 0 hrs 14 mins |
| 6 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,917,594 | 124,175 | 15.44 | 1 hrs 33 mins |
| 7 | Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics] |
Intel | Battlemage G21 | 1,905,752 | 20,300 | 93.88 | 0 hrs 15 mins |
| 8 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,789,587 | 37,824 | 47.31 | 0 hrs 30 mins |
| 9 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,788,754 | 76,133 | 23.50 | 1 hrs 1 mins |
| 10 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,775,137 | 109,071 | 16.28 | 1 hrs 28 mins |
| 11 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,684,912 | 20,300 | 83.00 | 0 hrs 17 mins |
| 12 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,219,929 | 113,883 | 10.71 | 2 hrs 14 mins |
| 13 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,119,371 | 23,013 | 48.64 | 0 hrs 30 mins |
| 14 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,087,708 | 117,331 | 9.27 | 2 hrs 35 mins |
| 15 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 952,862 | 20,300 | 46.94 | 0 hrs 31 mins |
| 16 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 805,886 | 106,371 | 7.58 | 3 hrs 10 mins |
| 17 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 742,877 | 33,258 | 22.34 | 1 hrs 4 mins |
| 18 | CMP 30HX TU116 [CMP 30HX] |
Nvidia | TU116 | 708,315 | 20,300 | 34.89 | 0 hrs 41 mins |
| 19 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 609,648 | 20,300 | 30.03 | 0 hrs 48 mins |
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|||||||
| 20 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 503,806 | 20,300 | 24.82 | 0 hrs 58 mins |
| 21 | Quadro M5000 GM204GL [Quadro M5000] |
Nvidia | GM204GL | 356,561 | 20,300 | 17.56 | 1 hrs 22 mins |
| 22 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 337,885 | 20,300 | 16.64 | 1 hrs 27 mins |
| 23 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 303,365 | 20,300 | 14.94 | 1 hrs 36 mins |
| 24 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 296,084 | 20,300 | 14.59 | 1 hrs 39 mins |
| 25 | GeForce GTX 1650 TU106 [GeForce GTX 1650] |
Nvidia | TU106 | 278,574 | 20,300 | 13.72 | 1 hrs 45 mins |
| 26 | Quadro K5200 GK110 [Quadro K5200] |
Nvidia | GK110 | 268,550 | 20,300 | 13.23 | 1 hrs 49 mins |
| 27 | Radeon RX 6400/6500XT Navi 24 [Radeon RX 6400/6500XT] |
AMD | Navi 24 | 265,729 | 73,353 | 3.62 | 6 hrs 38 mins |
| 28 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 238,271 | 61,867 | 3.85 | 6 hrs 14 mins |
| 29 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 210,952 | 68,388 | 3.08 | 7 hrs 47 mins |
| 30 | GeForce GTX 760 GK104 [GeForce GTX 760] 2258 |
Nvidia | GK104 | 140,825 | 20,300 | 6.94 | 3 hrs 28 mins |
| 31 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 136,221 | 20,300 | 6.71 | 3 hrs 35 mins |
| 32 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 79,255 | 35,216 | 2.25 | 10 hrs 40 mins |
| 33 | GeForce GT 710 GK208B [GeForce GT 710] 366 |
Nvidia | GK208B | 4,604 | 20,300 | 0.23 | 105 hrs 49 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:30:35|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|