RESEARCH: CANCER
FOLDING PROJECT #17795 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 65,600Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. They're found everywhere and help treat diseases like cancer and diabetes. The project uses simulations to understand how these proteins work, no matter their shape or type.
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
Relating to molecules.
Molecular refers to the study of molecules, which are the fundamental building blocks of all matter. In biotechnology, understanding the molecular basis of biological processes is crucial for developing new drugs and therapies.
Transporters
Proteins that move molecules across cell membranes.
Transporters are essential proteins found in cell membranes that facilitate the movement of various substances, such as nutrients, ions, and waste products, into and out of cells. They play a vital role in maintaining cellular homeostasis and enabling crucial biological processes.
Secondary Active Transporters
Transporters that use an ion gradient to move molecules across cell membranes.
Secondary active transporters are a type of membrane protein that utilize the electrochemical gradient of ions to drive the transport of other molecules. This process often involves coupling the movement of an ion down its concentration gradient with the movement of a different molecule against its concentration gradient.
Ion Gradient
A difference in ion concentration across a membrane.
An ion gradient refers to the unequal distribution of ions (charged atoms) on either side of a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including nerve impulse transmission and muscle contraction.
Drug Targets
Molecules that are involved in disease pathways and can be inhibited by drugs.
Drug targets are molecules or biological processes that are implicated in the development or progression of diseases. By targeting these specific molecules with medications, researchers aim to disrupt the disease pathway and alleviate symptoms.
Simulations
Computer models that mimic biological processes.
Simulations are computer-based representations of complex biological systems. These models allow researchers to study and predict the behavior of cells, tissues, and organisms under various conditions, providing insights into disease mechanisms and potential therapeutic strategies.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:05|
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 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,604,557 | 161,188 | 40.97 | 0 hrs 35 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,134,551 | 156,537 | 39.19 | 0 hrs 37 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 5,599,064 | 154,073 | 36.34 | 0 hrs 40 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,031,734 | 151,418 | 33.23 | 0 hrs 43 mins |
| 5 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,474,117 | 144,995 | 30.86 | 0 hrs 47 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,035,159 | 139,802 | 28.86 | 0 hrs 50 mins |
| 7 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,954,491 | 139,089 | 28.43 | 0 hrs 51 mins |
| 8 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,445,952 | 132,909 | 25.93 | 0 hrs 56 mins |
| 9 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,245,907 | 130,210 | 24.93 | 0 hrs 58 mins |
| 10 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,654,931 | 151,852 | 17.48 | 1 hrs 22 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,609,059 | 120,790 | 21.60 | 1 hrs 7 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,518,094 | 119,493 | 21.07 | 1 hrs 8 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,117,408 | 112,732 | 18.78 | 1 hrs 17 mins |
| 14 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,253,811 | 94,326 | 13.29 | 1 hrs 48 mins |
| 15 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,119,186 | 91,642 | 12.21 | 1 hrs 58 mins |
| 16 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 552,662 | 72,246 | 7.65 | 3 hrs 8 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:05|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|