RESEARCH: CANCER
FOLDING PROJECT #17769 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 90,887Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how special proteins move molecules across cell walls using the power of ion gradients. These proteins are found everywhere and play a big role in treating diseases like cancer and diabetes. Studying them helps us understand how they work across different types of organisms.
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
Secondary active transporters
Proteins that use ions to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They utilize an existing ion gradient to power the movement of various molecules across cell membranes. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. Due to their importance in various physiological processes, secondary active transporters are also attractive drug targets for treating a wide range of diseases.
Proteins
Large, complex molecules that perform a wide range of functions in living organisms.
Proteins are the workhorses of cells, responsible for carrying out countless tasks essential for life. They can act as enzymes to speed up chemical reactions, provide structural support, transport molecules, and participate in signaling pathways. The diversity of protein functions is immense, reflecting their crucial role in all aspects of biology.
Cell membranes
Thin barriers that surround cells, regulating the passage of molecules in and out.
Cell membranes are essential for compartmentalizing cellular processes and maintaining cell integrity. They act as selective barriers, allowing certain molecules to pass through while restricting others. This control over molecular movement is crucial for maintaining cellular homeostasis and enabling communication with the external environment.
Ion gradient
A difference in concentration of charged ions across a membrane.
An ion gradient is a fundamental concept in cellular energy and transport. It refers to an uneven distribution of charged particles (ions) across a membrane. This difference in concentration creates an electrochemical potential that can be harnessed by cells to power various processes, such as the movement of molecules across membranes.
Drug targets
Molecules or biological pathways that are involved in disease processes and can be targeted by drugs.
Drug targets are key components of pharmaceutical research and development. By identifying molecules or cellular pathways that contribute to disease progression, scientists can design drugs that specifically interfere with these targets, aiming to alleviate or cure the disease. Understanding drug targets is crucial for developing effective and safe medications.
Simulations
Computer models used to represent and study biological systems.
Simulations are powerful tools in bioinformatics, allowing researchers to explore complex biological processes without conducting physical experiments. By creating mathematical models that mimic real-world interactions, scientists can gain insights into how cells function, predict the effects of genetic mutations, and design new therapies.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:45|
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 | 8,054,859 | 159,086 | 50.63 | 0 hrs 28 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,117,804 | 152,835 | 46.57 | 0 hrs 31 mins |
| 3 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,020,882 | 154,394 | 45.47 | 0 hrs 32 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,916,695 | 143,809 | 41.14 | 0 hrs 35 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 5,418,946 | 139,472 | 38.85 | 0 hrs 37 mins |
| 6 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 4,557,709 | 131,878 | 34.56 | 0 hrs 42 mins |
| 7 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,528,854 | 131,993 | 34.31 | 0 hrs 42 mins |
| 8 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,116,663 | 128,645 | 32.00 | 0 hrs 45 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,399,037 | 119,950 | 28.34 | 0 hrs 51 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,320,622 | 119,142 | 27.87 | 0 hrs 52 mins |
| 11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,817,336 | 114,128 | 24.69 | 0 hrs 58 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,671,634 | 111,318 | 24.00 | 0 hrs 60 mins |
| 13 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,516,277 | 108,706 | 23.15 | 1 hrs 2 mins |
| 14 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,254,889 | 113,643 | 19.84 | 1 hrs 13 mins |
| 15 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,209,073 | 104,178 | 21.20 | 1 hrs 8 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,044,604 | 100,954 | 20.25 | 1 hrs 11 mins |
| 17 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 1,842,315 | 97,969 | 18.81 | 1 hrs 17 mins |
| 18 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,294,769 | 86,917 | 14.90 | 1 hrs 37 mins |
| 19 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,269,950 | 84,264 | 15.07 | 1 hrs 36 mins |
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| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,239,968 | 85,623 | 14.48 | 1 hrs 39 mins |
| 21 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,203,203 | 85,406 | 14.09 | 1 hrs 42 mins |
| 22 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,177,646 | 84,487 | 13.94 | 1 hrs 43 mins |
| 23 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 550,579 | 65,406 | 8.42 | 2 hrs 51 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:45|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|