RESEARCH: CANCER
FOLDING PROJECT #17791 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 117,169Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ions to move molecules across cell membranes. They work in all living things and are important targets for treating diseases like cancer and diabetes. This project uses simulations to understand how these proteins work.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
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 diseases like cancer, diabetes, and neurological disorders.
The simulations in this project will allow us to understand a universal role of ion-coupling across different families of proteins.
RELATED TERMS GLOSSARY AI BETA
Molecular basis
The fundamental structure and function of molecules.
This refers to the underlying principles governing how molecules work at a chemical level. In this context, it likely focuses on how the structure of secondary active transporter proteins enables them to move molecules across cell membranes.
Secondary active transporters
Proteins that use an ion gradient to move molecules across cell membranes.
These specialized proteins act like cellular pumps, utilizing the energy stored in an electrochemical gradient of ions (like sodium or potassium) to drive the movement of other molecules (like nutrients or drugs) across cell membranes. This process is crucial for many biological functions and is often targeted by medications.
Ion gradient
A difference in concentration of ions across a membrane.
This refers to the uneven distribution of electrically charged atoms (ions) across a cell membrane. The movement of these ions creates an electrochemical gradient that can be harnessed by proteins like secondary active transporters to power the transport of other molecules.
Drug targets
Molecules or biological processes that are potential therapeutic targets.
Drug targets are specific molecules or pathways within the body that can be manipulated by medications to treat diseases. Secondary active transporters often serve as drug targets because their function is essential for many physiological processes and their disruption can have therapeutic effects.
Simulations
Computer-based models used to study biological systems.
Simulations are powerful tools that allow researchers to virtually experiment and explore the behavior of complex biological systems like secondary active transporters. They can help predict how changes in molecular structure or environment might affect protein function.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:11|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,392,497 | 200,977 | 36.78 | 0 hrs 39 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,986,817 | 197,052 | 35.46 | 0 hrs 41 mins |
| 3 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,353,841 | 188,145 | 33.77 | 0 hrs 43 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,590,040 | 171,730 | 26.73 | 0 hrs 54 mins |
| 5 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,396,229 | 167,912 | 26.18 | 0 hrs 55 mins |
| 6 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,070,741 | 149,963 | 20.48 | 1 hrs 10 mins |
| 7 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,503,775 | 139,951 | 17.89 | 1 hrs 20 mins |
| 8 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,089,975 | 132,200 | 15.81 | 1 hrs 31 mins |
| 9 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,868,509 | 129,167 | 14.47 | 1 hrs 40 mins |
| 10 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,764,530 | 125,249 | 14.09 | 1 hrs 42 mins |
| 11 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,473,344 | 117,663 | 12.52 | 1 hrs 55 mins |
| 12 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,221,840 | 109,775 | 11.13 | 2 hrs 9 mins |
| 13 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,103,580 | 106,267 | 10.38 | 2 hrs 19 mins |
| 14 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 809,808 | 96,337 | 8.41 | 2 hrs 51 mins |
| 15 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 679,022 | 91,126 | 7.45 | 3 hrs 13 mins |
| 16 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 428,448 | 78,101 | 5.49 | 4 hrs 22 mins |
| 17 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 427,903 | 77,915 | 5.49 | 4 hrs 22 mins |
| 18 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 372,083 | 66,773 | 5.57 | 4 hrs 18 mins |
| 19 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 313,848 | 70,215 | 4.47 | 5 hrs 22 mins |
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| 20 | Radeon RX 460 Baffin XT [Radeon RX 460] |
AMD | Baffin XT | 130,639 | 52,467 | 2.49 | 9 hrs 38 mins |
| 21 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 114,894 | 50,073 | 2.29 | 10 hrs 28 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:11|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|