RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17942 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 131,306Core: 0x23
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ions to move molecules across cell membranes. They're found in all living things and work the same way, even if they look different. The project relates to understanding how these transporters work across different types of proteins because they're important drug targets 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 ion gradients to move various molecules across cell membranes. These transporters play a crucial role in many biological processes, such as nutrient uptake, waste removal, and signaling. Due to their involvement in various diseases, they are also important drug targets for treating conditions like cancer, diabetes, and neurological disorders.
Ion gradient
A difference in ion concentration across a cell membrane.
An ion gradient refers to the unequal distribution of charged ions (like sodium or potassium) between the inside and outside of a cell. This difference in concentration is essential for various cellular functions, including nerve impulse transmission, muscle contraction, and nutrient uptake. Cells maintain these gradients through specialized proteins called pumps and channels.
Simulations
Computer models used to mimic biological processes.
Simulations are powerful tools used in computational biology to study complex biological systems. By creating virtual models of cells or molecules, researchers can investigate how they interact and function under different conditions. Simulations help accelerate drug discovery by allowing scientists to test potential therapies before conducting expensive and time-consuming experiments.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:43|
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 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 20,093,071 | 153,532 | 130.87 | 0 hrs 11 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 17,396,933 | 10,897 | 1596.49 | 0 hrs 1 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 14,012,994 | 133,648 | 104.85 | 0 hrs 14 mins |
| 4 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 10,611,785 | 120,365 | 88.16 | 0 hrs 16 mins |
| 5 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,408,996 | 107,367 | 78.32 | 0 hrs 18 mins |
| 6 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 7,023,949 | 108,970 | 64.46 | 0 hrs 22 mins |
| 7 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 3,342,355 | 81,358 | 41.08 | 0 hrs 35 mins |
| 8 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,144,149 | 10,897 | 288.53 | 0 hrs 5 mins |
| 9 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,805,616 | 76,680 | 36.59 | 0 hrs 39 mins |
| 10 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,429,886 | 73,710 | 32.97 | 0 hrs 44 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,328,808 | 28,031 | 83.08 | 0 hrs 17 mins |
| 12 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,296,309 | 61,468 | 37.36 | 0 hrs 39 mins |
| 13 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,001,457 | 44,159 | 45.32 | 0 hrs 32 mins |
| 14 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,551,236 | 64,914 | 23.90 | 1 hrs 0 mins |
| 15 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,523,872 | 63,946 | 23.83 | 1 hrs 0 mins |
| 16 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,506,530 | 62,709 | 24.02 | 0 hrs 60 mins |
| 17 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,342,722 | 62,238 | 21.57 | 1 hrs 7 mins |
| 18 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 1,174,165 | 10,897 | 107.75 | 0 hrs 13 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:43|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|