RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17931 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 116,677Core: 0x23
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. These proteins are found everywhere and are important for many bodily functions. This project uses simulations to understand how these transporters work, which could lead to 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 ions to transport molecules across cell membranes.
Secondary active transporters are essential proteins found in all living organisms. They utilize the energy stored in an ion gradient to move other molecules across cell membranes. This process is vital for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Many of these transporters are drug targets for treating diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient is a fundamental concept in cellular biology. It refers to the unequal distribution of ions, such as sodium (Na+) and potassium (K+), across a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including nerve impulse transmission, muscle contraction, and nutrient uptake.
Simulations
Computer models used to study biological systems.
Simulations are powerful tools used in computational biology to understand complex biological processes. Researchers create computer models that mimic the behavior of cells, molecules, or entire organisms. These simulations allow scientists to explore different scenarios, test hypotheses, and gain insights into biological phenomena.
Drug targets
Molecules or pathways that are involved in disease and can be targeted by drugs.
Drug targets are essential components of the drug discovery process. They are specific molecules or cellular pathways that play a role in the development or progression of a disease. By targeting these drug targets, researchers aim to develop therapies that can effectively treat or prevent diseases.
Cancer
A group of diseases characterized by uncontrolled cell growth.
Cancer is a complex group of diseases that involves abnormal and uncontrolled cell growth. These cells can invade surrounding tissues and spread to other parts of the body. There are many different types of cancer, each with its own unique characteristics and treatments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:34:00|
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 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 18,008,736 | 28,763 | 626.11 | 0 hrs 2 mins |
| 2 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 16,756,937 | 138,868 | 120.67 | 0 hrs 12 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 15,790,305 | 124,768 | 126.56 | 0 hrs 11 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 11,275,193 | 122,362 | 92.15 | 0 hrs 16 mins |
| 5 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 10,115,526 | 113,568 | 89.07 | 0 hrs 16 mins |
| 6 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 7,340,616 | 102,320 | 71.74 | 0 hrs 20 mins |
| 7 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 6,719,733 | 178,361 | 37.67 | 0 hrs 38 mins |
| 8 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 6,285,024 | 79,300 | 79.26 | 0 hrs 18 mins |
| 9 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,449,112 | 79,511 | 43.38 | 0 hrs 33 mins |
| 10 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,021,338 | 10,140 | 297.96 | 0 hrs 5 mins |
| 11 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,680,451 | 72,660 | 36.89 | 0 hrs 39 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,652,822 | 73,409 | 36.14 | 0 hrs 40 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,081,495 | 66,780 | 31.17 | 0 hrs 46 mins |
| 14 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,348,853 | 58,027 | 23.25 | 1 hrs 2 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 771,302 | 43,234 | 17.84 | 1 hrs 21 mins |
| 16 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 707,300 | 10,140 | 69.75 | 0 hrs 21 mins |
| 17 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 583,566 | 44,578 | 13.09 | 1 hrs 50 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:34:00|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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