RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17934 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 92,122Core: 0x23
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
The project relates to proteins that use ions (like sodium) to move other molecules across cell walls. These transporters are super important for all living things and help fight diseases like cancer and diabetes. Simulations will show how they work, no matter their shape!
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 membranes.
Secondary active transporters are crucial proteins found in all living organisms. They act like cellular ferries, using the energy from an existing ion gradient to move other molecules across cell membranes. This process is essential for various functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are also drug targets because they play roles in diseases like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in concentration of charged ions across a membrane.
An ion gradient refers to the unequal distribution of electrically charged atoms (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that cells can harness for various processes. For example, sodium-potassium pumps use energy to create an ion gradient that drives nerve impulse transmission.
Proteins
Large, complex molecules essential for all biological functions.
Proteins are the workhorses of cells, carrying out a vast array of functions. They are made up of chains of amino acids linked together in specific sequences. Proteins can act as enzymes (catalysts), structural components, transporters, hormones, and much more.
Membranes
Thin barriers that enclose cells and organelles.
Membranes are thin layers composed primarily of lipids and proteins. They act as barriers separating different compartments within cells and between cells and their environment. Membranes regulate the passage of molecules in and out of cells, maintaining cellular integrity and function.
Simulations
Computer models used to represent and study complex systems.
Simulations are powerful tools for understanding complex processes in biology and other fields. By creating computer models of biological systems, researchers can test hypotheses, explore different scenarios, and gain insights that would be difficult or impossible to obtain through experiments alone.
Families of proteins
Groups of proteins with similar structures and functions.
Proteins are classified into families based on similarities in their amino acid sequences, structures, and functions. This classification system helps researchers understand the evolutionary relationships between proteins and their roles in biological systems.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:55|
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 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 18,632,549 | 243,689 | 76.46 | 0 hrs 19 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 11,427,427 | 88,769 | 128.73 | 0 hrs 11 mins |
| 3 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,274,307 | 64,489 | 66.28 | 0 hrs 22 mins |
| 4 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,092,721 | 65,523 | 62.46 | 0 hrs 23 mins |
| 5 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,366,778 | 52,035 | 45.48 | 0 hrs 32 mins |
| 6 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,312,639 | 43,961 | 52.61 | 0 hrs 27 mins |
| 7 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,089,893 | 50,350 | 41.51 | 0 hrs 35 mins |
| 8 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,603,535 | 46,007 | 34.85 | 0 hrs 41 mins |
| 9 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 83,497 | 6,575 | 12.70 | 1 hrs 53 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:55|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
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