RESEARCH: CANCER
FOLDING PROJECT #17787 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 94,899Core: 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. These transporters are found in all living things and are important for many bodily functions. The project relates to understanding how these transporters work, which could lead to new drugs for diseases like cancer and diabetes.
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
Secondary active transporters
Proteins that use ion gradients to transport molecules across cell membranes.
Secondary active transporters are crucial proteins found in all living organisms. They work by using the energy from an existing ion gradient to move other molecules, like nutrients or drugs, across cell membranes. This process is vital for many biological functions and is often targeted by medications for diseases like cancer and diabetes.
Ion gradient
A difference in concentration of ions across a membrane.
An ion gradient refers to the unequal distribution of charged particles (ions) on either side of a cell membrane. This difference in concentration creates electrical potential energy that can be used by cells to power various processes, such as transporting molecules across the membrane.
Simulations
Computer-based models used to mimic biological processes.
Simulations are powerful tools used in bioinformatics and other scientific fields to study complex systems. By creating computer models of biological processes, researchers can explore different scenarios, test hypotheses, and gain insights into how things work at a molecular level.
Proteins
Large biomolecules essential for cell structure and function.
Proteins are the workhorses of cells, carrying out a vast array of functions. From building tissues to catalyzing biochemical reactions, proteins are essential for life as we know it.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:17|
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 | 7,579,008 | 160,395 | 47.25 | 0 hrs 30 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,455,315 | 163,948 | 45.47 | 0 hrs 32 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,356,180 | 157,382 | 46.74 | 0 hrs 31 mins |
| 4 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,597,080 | 137,972 | 33.32 | 0 hrs 43 mins |
| 5 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,328,146 | 135,255 | 32.00 | 0 hrs 45 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,880,391 | 130,244 | 29.79 | 0 hrs 48 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,736,511 | 129,740 | 28.80 | 0 hrs 50 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,094,945 | 121,792 | 25.41 | 0 hrs 57 mins |
| 9 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,438,419 | 112,321 | 21.71 | 1 hrs 6 mins |
| 10 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,140,790 | 107,152 | 19.98 | 1 hrs 12 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,913,050 | 103,815 | 18.43 | 1 hrs 18 mins |
| 12 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,213,016 | 88,435 | 13.72 | 1 hrs 45 mins |
| 13 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,072,387 | 85,642 | 12.52 | 1 hrs 55 mins |
| 14 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,035,860 | 84,517 | 12.26 | 1 hrs 57 mins |
| 15 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 736,202 | 75,074 | 9.81 | 2 hrs 27 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:17|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|