RESEARCH: CANCER
FOLDING PROJECT #17794 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 65,610Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how proteins use ion gradients to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and help with many important processes, including drug delivery. By studying them, we can learn more about how cells work and develop new treatments 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 help move various molecules across cell membranes by utilizing the energy stored in an existing ion gradient. This process is essential for numerous cellular functions, including nutrient uptake, waste removal, and signal transduction. Dysfunctional secondary active transporters are implicated in various diseases, making them attractive drug targets.
Ion gradient
A difference in ion concentration across a membrane.
An ion gradient is a difference in the concentration of electrically charged atoms (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that can be harnessed to power cellular processes. For example, ion gradients are essential for maintaining cell volume, transmitting nerve impulses, and transporting molecules across membranes.
Drug targets
Molecules or pathways that are targeted by drugs to treat diseases.
Drug targets are specific molecules or biological processes that are involved in the development or progression of a disease. Pharmaceuticals aim to develop drugs that interact with these targets to either block their activity, enhance their function, or modulate their signaling pathways. Targeting these specific molecules can help alleviate symptoms, slow disease progression, or even cure certain diseases.
Cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a broad term encompassing a variety of diseases characterized by the abnormal and uncontrolled growth of cells. These cancerous cells can invade surrounding tissues, spread to other parts of the body (metastasis), and disrupt normal bodily functions. Cancer arises from mutations in DNA that regulate cell growth and division, leading to uncontrolled proliferation and tumor formation.
Diabetes
A metabolic disorder characterized by high blood sugar levels.
Diabetes is a chronic condition affecting how the body regulates blood sugar (glucose). In type 1 diabetes, the immune system attacks insulin-producing cells in the pancreas. In type 2 diabetes, the body becomes resistant to insulin or doesn't produce enough. This leads to elevated blood sugar levels, which can damage various organs and tissues over time.
Neurological disorders
Conditions affecting the nervous system.
Neurological disorders encompass a wide range of conditions that affect the brain, spinal cord, and peripheral nerves. These disorders can manifest in various ways, including cognitive impairment, movement problems, sensory disturbances, and emotional changes. Examples include Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:06|
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 | 6,613,301 | 164,107 | 40.30 | 0 hrs 36 mins |
| 2 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 5,369,002 | 155,353 | 34.56 | 0 hrs 42 mins |
| 3 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,716,658 | 147,396 | 32.00 | 0 hrs 45 mins |
| 4 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 4,370,482 | 142,159 | 30.74 | 0 hrs 47 mins |
| 5 | RTX A5000 GA102GL [RTX A5000] |
Nvidia | GA102GL | 3,955,507 | 139,132 | 28.43 | 0 hrs 51 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,373,393 | 132,749 | 25.41 | 0 hrs 57 mins |
| 7 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,606,424 | 145,167 | 17.95 | 1 hrs 20 mins |
| 8 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,104,258 | 113,234 | 18.58 | 1 hrs 17 mins |
| 9 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,250,461 | 94,792 | 13.19 | 1 hrs 49 mins |
| 10 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 873,106 | 79,341 | 11.00 | 2 hrs 11 mins |
| 11 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 510,784 | 70,854 | 7.21 | 3 hrs 20 mins |
| 12 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 314,651 | 59,967 | 5.25 | 4 hrs 34 mins |
| 13 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 133,839 | 45,068 | 2.97 | 8 hrs 5 mins |
| 14 | GeForce GTX 770 GK104 [GeForce GTX 770] 3213 |
Nvidia | GK104 | 112,307 | 42,800 | 2.62 | 9 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:06|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|