RESEARCH: CANCER
FOLDING PROJECT #17728 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 76,376Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how special proteins use energy from ions to move molecules across cell membranes. These proteins are found everywhere and are important for things like fighting cancer, diabetes, and brain disorders. By studying them, we can learn more about how cells work and how to develop new medicines.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17711-17724 Molecular basis of secondary active transporters. Secondary active membrane transporters are proteins that utilize ions to transport an assortment of molecules across cell membranes.
These proteins are found in all domains in life and surprisingly, despite vastly different structures, operate under the same mechanism by using an ion gradient to assist in small molecule transport.
Furthermore, many of these secondary active transporters are drug targets to treat disease like cancer, diabetes, and neurological disorders.
These simulations will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
secondary active transporter
A protein that uses an ion gradient to transport molecules across cell membranes.
Secondary active transporters are crucial proteins found in all living organisms. They utilize the energy stored in an ion gradient to move various molecules across cell membranes. This process is essential for numerous cellular functions, including nutrient uptake, waste removal, and signal transduction. These transporters are often targets for drugs used to treat diseases like cancer, diabetes, and neurological disorders.
ion gradient
A difference in concentration of ions across a membrane.
An ion gradient is the uneven distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that can be harnessed by cells to perform various functions. For example, secondary active transporters utilize ion gradients to move molecules against their concentration gradient.
proteins
Large, complex molecules that perform a wide range of functions in living organisms.
Proteins are the workhorses of the cell, carrying out essential tasks such as catalyzing reactions, transporting molecules, providing structural support, and regulating cellular processes. They are made up of chains of amino acids, folded into complex three-dimensional structures that determine their specific function.
cell membranes
Thin, flexible barriers that surround cells and regulate the passage of substances in and out.
Cell membranes are composed of a lipid bilayer with embedded proteins. They act as selective barriers, controlling the movement of molecules across them. This regulation is essential for maintaining cellular homeostasis and carrying out vital functions.
simulations
Computer models used to study and predict the behavior of complex systems.
Simulations are powerful tools used in various fields, including biotechnology, to understand complex phenomena. By creating computer models that mimic real-world processes, researchers can explore different scenarios and gain insights into how systems behave.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:36:31|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,079,934 | 108,143 | 65.47 | 0 hrs 22 mins |
| 2 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 5,442,392 | 100,687 | 54.05 | 0 hrs 27 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,080,065 | 97,751 | 51.97 | 0 hrs 28 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,277,246 | 93,273 | 45.86 | 0 hrs 31 mins |
| 5 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 3,999,472 | 90,565 | 44.16 | 0 hrs 33 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,319,251 | 85,176 | 38.97 | 0 hrs 37 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,209,296 | 84,486 | 37.99 | 0 hrs 38 mins |
| 8 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 3,167,339 | 84,275 | 37.58 | 0 hrs 38 mins |
| 9 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 3,008,498 | 82,318 | 36.55 | 0 hrs 39 mins |
| 10 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,909,995 | 81,614 | 35.66 | 0 hrs 40 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,490,907 | 77,271 | 32.24 | 0 hrs 45 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,365,421 | 76,877 | 30.77 | 0 hrs 47 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,365,363 | 76,404 | 30.96 | 0 hrs 47 mins |
| 14 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 2,291,196 | 75,122 | 30.50 | 0 hrs 47 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,112,241 | 73,532 | 28.73 | 0 hrs 50 mins |
| 16 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,961,424 | 73,504 | 26.68 | 0 hrs 54 mins |
| 17 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,959,935 | 71,926 | 27.25 | 0 hrs 53 mins |
| 18 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,779,723 | 69,810 | 25.49 | 0 hrs 56 mins |
| 19 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,548,249 | 66,302 | 23.35 | 1 hrs 2 mins |
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|||||||
| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,304,469 | 62,646 | 20.82 | 1 hrs 9 mins |
| 21 | GeForce RTX 2070 TU106 [GeForce RTX 2070] M 6497 |
Nvidia | TU106 | 1,256,190 | 60,806 | 20.66 | 1 hrs 10 mins |
| 22 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,249,228 | 62,422 | 20.01 | 1 hrs 12 mins |
| 23 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,184,168 | 61,355 | 19.30 | 1 hrs 15 mins |
| 24 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,137,758 | 60,128 | 18.92 | 1 hrs 16 mins |
| 25 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 1,132,427 | 60,291 | 18.78 | 1 hrs 17 mins |
| 26 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,113,101 | 59,709 | 18.64 | 1 hrs 17 mins |
| 27 | GeForce GTX 1650 SUPER TU116 [GeForce GTX 1650 SUPER] |
Nvidia | TU116 | 904,588 | 55,666 | 16.25 | 1 hrs 29 mins |
| 28 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 768,468 | 52,761 | 14.57 | 1 hrs 39 mins |
| 29 | Radeon RX 5600 OEM/5600 XT/5700/5700 XT Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT] |
AMD | Navi 10 | 759,641 | 52,669 | 14.42 | 1 hrs 40 mins |
| 30 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 663,019 | 50,252 | 13.19 | 1 hrs 49 mins |
| 31 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 590,678 | 48,508 | 12.18 | 1 hrs 58 mins |
| 32 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 520,515 | 46,566 | 11.18 | 2 hrs 9 mins |
| 33 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 468,212 | 44,855 | 10.44 | 2 hrs 18 mins |
| 34 | Quadro T2000 Mobile / Max-Q TU117GLM [Quadro T2000 Mobile / Max-Q] |
Nvidia | TU117GLM | 467,920 | 44,678 | 10.47 | 2 hrs 17 mins |
| 35 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 389,499 | 42,081 | 9.26 | 2 hrs 36 mins |
| 36 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 303,415 | 38,885 | 7.80 | 3 hrs 5 mins |
| 37 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 240,384 | 35,885 | 6.70 | 3 hrs 35 mins |
| 38 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 194,581 | 33,521 | 5.80 | 4 hrs 8 mins |
| 39 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 153,877 | 30,390 | 5.06 | 4 hrs 44 mins |
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| 40 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 113,427 | 27,631 | 4.11 | 5 hrs 51 mins |
| 41 | Quadro P620 GP107GL [Quadro P620] |
Nvidia | GP107GL | 113,055 | 27,964 | 4.04 | 5 hrs 56 mins |
| 42 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 86,408 | 25,620 | 3.37 | 7 hrs 7 mins |
| 43 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 74,237 | 24,309 | 3.05 | 7 hrs 52 mins |
| 44 | GTX 650 Ti Boost GK106 [GTX 650 Ti Boost] |
Nvidia | GK106 | 65,894 | 21,237 | 3.10 | 7 hrs 44 mins |
| 45 | Quadro K620 GM107GL [Quadro K620] |
Nvidia | GM107GL | 47,857 | 21,071 | 2.27 | 10 hrs 34 mins |
| 46 | GeForce MX130 GM108M [GeForce MX130] |
Nvidia | GM108M | 42,533 | 19,871 | 2.14 | 11 hrs 13 mins |
| 47 | GeForce GTX 670M GF114 [GeForce GTX 670M] |
Nvidia | GF114 | 10,109 | 10,544 | 0.96 | 25 hrs 2 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:36:31|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|