Research section Research projects and references

Research: IL-2-RECEPTOR-DYNAMICS Folding Project #18281

Project #18281 overview

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.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Justin Miller
Institution
University of Pennsylvania

Work unit

Atoms
24,774
Core
0x27
Status
Public
Source material

Official 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.

Performance data

Hardware Performance for Project 18281

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:52:30

GPU PPD Averages

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 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,288,589 20,300 112.74 0 hrs 13 mins
3 Tesla P100 16GB
GP100GL [Tesla P100 16GB] 9340
Nvidia GP100GL 2,166,492 20,300 106.72 0 hrs 13 mins
4 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
5 Intel Arc B580 Graphics
Battlemage G21 [Intel Arc B580 Graphics]
Intel Battlemage G21 1,989,803 20,300 98.02 0 hrs 15 mins
6 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 Super]
Nvidia TU106 1,914,713 123,832 15.46 1 hrs 33 mins
7 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,784,569 111,968 15.94 1 hrs 30 mins
8 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 1,777,431 45,083 39.43 0 hrs 37 mins
9 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,769,915 82,724 21.40 1 hrs 7 mins
10 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 1,684,912 20,300 83.00 0 hrs 17 mins
11 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,294,800 22,240 58.22 0 hrs 25 mins
12 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,225,051 114,850 10.67 2 hrs 15 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 1,047,339 116,348 9.00 2 hrs 40 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
20 GeForce GTX 1070 Mobile
GP104BM [GeForce GTX 1070 Mobile] 6463
Nvidia GP104BM 501,859 20,300 24.72 0 hrs 58 mins
21 Radeon PRO W6400
Navi 24 [Radeon PRO W6400]
AMD Navi 24 412,337 20,300 20.31 1 hrs 11 mins
22 Quadro M5000
GM204GL [Quadro M5000]
Nvidia GM204GL 356,121 20,300 17.54 1 hrs 22 mins
23 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 337,885 20,300 16.64 1 hrs 27 mins
24 Quadro T1000 Mobile
TU117GLM [Quadro T1000 Mobile]
Nvidia TU117GLM 303,365 20,300 14.94 1 hrs 36 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 268,336 20,300 13.22 1 hrs 49 mins
27 Radeon RX 6400/6500XT
Navi 24 [Radeon RX 6400/6500XT]
AMD Navi 24 267,564 73,798 3.63 6 hrs 37 mins
28 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 228,467 60,496 3.78 6 hrs 21 mins
29 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 210,674 68,314 3.08 7 hrs 47 mins
30 Quadro T400 Mobile
TU117GLM [Quadro T400 Mobile]
Nvidia TU117GLM 144,609 20,300 7.12 3 hrs 22 mins
31 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 140,825 20,300 6.94 3 hrs 28 mins
32 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 78,475 36,724 2.14 11 hrs 14 mins
33 GeForce GT 710
GK208B [GeForce GT 710] 366
Nvidia GK208B 4,488 20,300 0.22 108 hrs 33 mins