RESEARCH: IL-2-FORCE-FIELD-BENCHMARKING
FOLDING PROJECT #18283 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 29,665Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project compares computer simulations of human interleukin-2 (IL-2) with real lab results to see which simulation methods are most accurate. IL-2 is a protein that controls how our immune system works, and understanding how it interacts with other proteins could help us develop 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 cytokine that regulates T-cell activity.
Interleukin-2 (IL-2) is a crucial signaling molecule produced by immune cells. It plays a vital role in stimulating the growth and activity of T-cells, which are essential for fighting infections and diseases. IL-2 can either promote or inhibit immune responses depending on the specific receptors it binds to.
nuclear magnetic resonance
A technique used to determine 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 works by applying a magnetic field and radio waves to a sample, causing the atomic nuclei to resonate at specific frequencies. These resonance frequencies provide information about the chemical environment and bonding patterns within the molecule.
cytokine
A type of signaling molecule produced by immune cells.
Cytokines are small proteins that act as messengers between cells in the immune system. They play a crucial role in regulating immune responses, such as inflammation, cell growth, and differentiation. Examples of cytokines include interferons, interleukins, and chemokines.
T-cell
A type of white blood cell that plays a key role in the immune response.
T-cells are a type of lymphocyte, a specialized white blood cell involved in the adaptive immune system. They recognize and destroy infected or cancerous cells by releasing toxic substances or signaling other immune cells to attack. Different types of T-cells have specific functions, such as helper T-cells, which activate other immune cells, and cytotoxic T-cells, which directly kill target cells.
receptor
A protein that binds to a specific molecule.
Receptors are specialized proteins found on the surface or inside cells. They play a vital role in cell signaling by binding to specific molecules, called ligands. This binding triggers a cascade of events within the cell, leading to changes in gene expression, metabolism, or other cellular functions.
force field
A set of mathematical equations that describes the interactions between atoms.
A force field is a computational model used in molecular dynamics simulations to describe the interactions between atoms and molecules. It consists of a set of mathematical equations that define the potential energy of a system based on the distances between atoms. Force fields are essential for simulating the behavior of molecules at the atomic level.
molecular dynamics
A computational method used to simulate the movement of atoms and molecules over time.
Molecular dynamics (MD) is a powerful computational technique used to simulate the motion of atoms and molecules over time. By solving Newton's equations of motion for a system of interacting particles, MD simulations provide insights into the dynamic behavior of molecules, such as protein folding, ligand binding, and chemical reactions.
simulation
A computer-based representation of a real-world system.
Simulation is the process of creating a computer model that mimics the behavior of a real-world system. Simulations are widely used in various fields, such as science, engineering, and business, to study complex systems, predict outcomes, and optimize processes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:30:32|
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 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 11,497,846 | 18,000 | 638.77 | 0 hrs 2 mins |
| 2 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 2,951,132 | 18,000 | 163.95 | 0 hrs 9 mins |
| 3 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super] |
Nvidia | TU106 | 2,478,365 | 26,816 | 92.42 | 0 hrs 16 mins |
| 4 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,021,384 | 18,000 | 112.30 | 0 hrs 13 mins |
| 5 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,898,804 | 153,621 | 12.36 | 1 hrs 57 mins |
| 6 | TITAN Xp GP102 [TITAN Xp] 12150 |
Nvidia | GP102 | 1,865,121 | 18,000 | 103.62 | 0 hrs 14 mins |
| 7 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,826,621 | 21,202 | 86.15 | 0 hrs 17 mins |
| 8 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,802,666 | 113,029 | 15.95 | 1 hrs 30 mins |
| 9 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,736,046 | 123,646 | 14.04 | 1 hrs 43 mins |
| 10 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,705,306 | 18,000 | 94.74 | 0 hrs 15 mins |
| 11 | Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics] |
Intel | Battlemage G21 | 1,470,171 | 18,000 | 81.68 | 0 hrs 18 mins |
| 12 | P102-100 GP102 [P102-100] |
Nvidia | GP102 | 1,422,536 | 18,000 | 79.03 | 0 hrs 18 mins |
| 13 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,349,140 | 18,000 | 74.95 | 0 hrs 19 mins |
| 14 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,334,589 | 83,435 | 16.00 | 1 hrs 30 mins |
| 15 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 1,268,603 | 18,000 | 70.48 | 0 hrs 20 mins |
| 16 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,168,009 | 18,000 | 64.89 | 0 hrs 22 mins |
| 17 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,142,951 | 31,075 | 36.78 | 0 hrs 39 mins |
| 18 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 982,216 | 18,000 | 54.57 | 0 hrs 26 mins |
| 19 | RX 5600 OEM/5600XT/5700(XT) Navi 10 [RX 5600 OEM/5600XT/5700(XT)] |
AMD | Navi 10 | 977,660 | 18,000 | 54.31 | 0 hrs 27 mins |
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| 20 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 969,598 | 42,004 | 23.08 | 1 hrs 2 mins |
| 21 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 934,118 | 18,000 | 51.90 | 0 hrs 28 mins |
| 22 | GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107M | 901,213 | 18,000 | 50.07 | 0 hrs 29 mins |
| 23 | CMP 30HX TU116 [CMP 30HX] |
Nvidia | TU116 | 893,113 | 18,000 | 49.62 | 0 hrs 29 mins |
| 24 | GeForce GTX 1650 TU106 [GeForce GTX 1650] |
Nvidia | TU106 | 862,361 | 18,000 | 47.91 | 0 hrs 30 mins |
| 25 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 861,355 | 100,888 | 8.54 | 2 hrs 49 mins |
| 26 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 860,738 | 18,000 | 47.82 | 0 hrs 30 mins |
| 27 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 838,226 | 18,000 | 46.57 | 0 hrs 31 mins |
| 28 | GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB] |
Nvidia | GA107 | 723,375 | 18,000 | 40.19 | 0 hrs 36 mins |
| 29 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 622,410 | 61,393 | 10.14 | 2 hrs 22 mins |
| 30 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 553,572 | 18,000 | 30.75 | 0 hrs 47 mins |
| 31 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 426,328 | 18,000 | 23.68 | 1 hrs 1 mins |
| 32 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 420,361 | 78,094 | 5.38 | 4 hrs 28 mins |
| 33 | GeForce GTX 1050 LP GP107 [GeForce GTX 1050 LP] 1862 |
Nvidia | GP107 | 419,955 | 18,000 | 23.33 | 1 hrs 2 mins |
| 34 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 389,860 | 18,000 | 21.66 | 1 hrs 6 mins |
| 35 | RX 5500(M)/Pro 5500M Navi 14 [RX 5500(M)/Pro 5500M] |
AMD | Navi 14 | 366,203 | 18,000 | 20.34 | 1 hrs 11 mins |
| 36 | Radeon PRO W6400 Navi 24 [Radeon PRO W6400] |
AMD | Navi 24 | 348,361 | 18,000 | 19.35 | 1 hrs 14 mins |
| 37 | Quadro M5000 GM204GL [Quadro M5000] |
Nvidia | GM204GL | 346,586 | 18,000 | 19.25 | 1 hrs 15 mins |
| 38 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 272,541 | 68,766 | 3.96 | 6 hrs 3 mins |
| 39 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 256,056 | 37,038 | 6.91 | 3 hrs 28 mins |
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| 40 | Quadro K5200 GK110 [Quadro K5200] |
Nvidia | GK110 | 241,562 | 18,000 | 13.42 | 1 hrs 47 mins |
| 41 | GeForce GTX Titan X GM200 [GeForce GTX Titan X] 6144 |
Nvidia | GM200 | 206,194 | 63,919 | 3.23 | 7 hrs 26 mins |
| 42 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 205,054 | 18,000 | 11.39 | 2 hrs 6 mins |
| 43 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 102,823 | 50,004 | 2.06 | 11 hrs 40 mins |
| 44 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 79,917 | 32,929 | 2.43 | 9 hrs 53 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:30:32|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|