RESEARCH: CANCER
FOLDING PROJECT #17774 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 58,595Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how proteins move molecules across cell membranes using ion power. These proteins are found everywhere and help treat diseases like cancer and diabetes. Simulations will reveal how these proteins work, no matter their shape.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17745-17750 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 help cells move various substances in and out. They use energy or gradients to carry molecules like nutrients, ions, and waste products. These proteins are crucial for many cellular processes, including nutrient uptake, signaling, and detoxification. Dysfunctional transporters can lead to various diseases.
Secondary Active Transporters
Membrane proteins that use an ion gradient to transport molecules.
Secondary active transporters are a specific type of membrane protein that rely on an existing ion gradient to move other molecules across the cell membrane. They couple the movement of one molecule (usually an ion) down its concentration gradient with the movement of another molecule (the solute) against its concentration gradient. This process is vital for various cellular functions, including nutrient uptake, waste removal, and signal transduction.
Ion Gradient
A difference in ion concentration across a membrane.
An ion gradient refers to the unequal distribution of charged ions (like sodium, potassium, or calcium) across a cell membrane. This difference in concentration creates an electrical potential, which is essential for many cellular processes, including nerve impulse transmission and muscle contraction. Secondary active transporters utilize this gradient to drive the transport of other molecules.
Drug Targets
Molecules or pathways that are potential sites for drug action.
Drug targets are specific molecules or biological pathways involved in disease processes. Scientists aim to develop drugs that can interact with these targets and modulate their activity. By interfering with a target's function, a drug can potentially treat or prevent a disease.
Simulations
Computer models of biological processes.
Simulations are computer-based representations of real-world phenomena. In biology, simulations can model complex processes like protein interactions, cell signaling pathways, or drug effects. These virtual experiments allow researchers to test hypotheses, explore different scenarios, and gain insights into biological systems without conducting physical experiments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:37|
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 | 7,104,000 | 123,333 | 57.60 | 0 hrs 25 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,670,224 | 121,104 | 55.08 | 0 hrs 26 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,648,277 | 114,742 | 49.23 | 0 hrs 29 mins |
| 4 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,136,564 | 103,671 | 39.90 | 0 hrs 36 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,074,997 | 102,600 | 39.72 | 0 hrs 36 mins |
| 6 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,838,975 | 101,132 | 37.96 | 0 hrs 38 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 3,814,851 | 100,698 | 37.88 | 0 hrs 38 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,167,779 | 95,326 | 33.23 | 0 hrs 43 mins |
| 9 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 3,096,177 | 129,886 | 23.84 | 1 hrs 0 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 2,734,510 | 88,265 | 30.98 | 0 hrs 46 mins |
| 11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,576,016 | 84,522 | 30.48 | 0 hrs 47 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,450,356 | 87,663 | 27.95 | 0 hrs 52 mins |
| 13 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,430,446 | 87,203 | 27.87 | 0 hrs 52 mins |
| 14 | GeForce RTX 2080 Mobile TU104M [GeForce RTX 2080 Mobile] |
Nvidia | TU104M | 1,994,623 | 81,934 | 24.34 | 0 hrs 59 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 1,957,593 | 80,982 | 24.17 | 0 hrs 60 mins |
| 16 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,339,571 | 71,266 | 18.80 | 1 hrs 17 mins |
| 17 | GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB] |
Nvidia | GA104M | 1,231,675 | 68,447 | 17.99 | 1 hrs 20 mins |
| 18 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,117,882 | 67,280 | 16.62 | 1 hrs 27 mins |
| 19 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,081,980 | 66,371 | 16.30 | 1 hrs 28 mins |
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| 20 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,042,203 | 64,184 | 16.24 | 1 hrs 29 mins |
| 21 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 570,493 | 53,478 | 10.67 | 2 hrs 15 mins |
| 22 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 560,789 | 53,437 | 10.49 | 2 hrs 17 mins |
| 23 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 455,198 | 50,250 | 9.06 | 2 hrs 39 mins |
| 24 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 346,699 | 45,410 | 7.63 | 3 hrs 9 mins |
| 25 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 341,852 | 45,170 | 7.57 | 3 hrs 10 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:37|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|