RESEARCH: CANCER
FOLDING PROJECT #17785 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 90,906Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how proteins use ion power to move molecules across cell membranes. These proteins are super important because they're found everywhere and help with things like fighting diseases. The simulations will help us understand how these proteins work no matter their shape or size.
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
Transporters
Proteins that move molecules across cell membranes.
Transporters are essential proteins that facilitate the movement of various molecules across cell membranes. They play a crucial role in maintaining cellular homeostasis and transporting nutrients, ions, and waste products. Different types of transporters exist, including primary and secondary active transporters, as well as passive transporters.
Secondary Active Transporters
Membrane proteins that use an ion gradient to transport molecules across the membrane.
Secondary active transporters are a type of protein embedded in cell membranes. They leverage the energy stored in an ion gradient (a difference in concentration of charged particles) to move other molecules against their concentration gradient – essentially uphill. This process is crucial for nutrient uptake, waste removal, and maintaining cellular balance.
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 imbalance creates an electrochemical potential that can be used to drive various cellular processes, such as the movement of molecules through secondary active transporters.
Membrane Proteins
Proteins embedded in cell membranes.
Membrane proteins are specialized proteins that reside within the lipid bilayer of cell membranes. They perform a wide range of functions, including transporting molecules, receiving signals from the environment, and anchoring the membrane to the cytoskeleton.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:20|
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 | 7,340,282 | 152,944 | 47.99 | 0 hrs 30 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,988,898 | 149,883 | 46.63 | 0 hrs 31 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,328,391 | 146,491 | 43.20 | 0 hrs 33 mins |
| 4 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,611,667 | 133,439 | 34.56 | 0 hrs 42 mins |
| 5 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,330,692 | 130,322 | 33.23 | 0 hrs 43 mins |
| 6 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,328,143 | 130,256 | 33.23 | 0 hrs 43 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,280,781 | 129,840 | 32.97 | 0 hrs 44 mins |
| 8 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,163,385 | 127,626 | 32.62 | 0 hrs 44 mins |
| 9 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,713,648 | 124,648 | 29.79 | 0 hrs 48 mins |
| 10 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,567,024 | 109,930 | 23.35 | 1 hrs 2 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,003,544 | 101,439 | 19.75 | 1 hrs 13 mins |
| 12 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,201,173 | 84,805 | 14.16 | 1 hrs 42 mins |
| 13 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,128,146 | 80,937 | 13.94 | 1 hrs 43 mins |
| 14 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,004,227 | 80,485 | 12.48 | 1 hrs 55 mins |
| 15 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 770,439 | 73,301 | 10.51 | 2 hrs 17 mins |
| 16 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 555,923 | 65,730 | 8.46 | 2 hrs 50 mins |
| 17 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 310,915 | 54,562 | 5.70 | 4 hrs 13 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:20|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|