RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17938 PROFILE
PROJECT TEAM
Manager(s): Arnav PaulInstitution: University of Illinois
WORK UNIT INFO
Atoms: 100,957Core: 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 transporters are found everywhere in life and play a key role in health and disease. The project uses simulations to understand how these transporters work across different types of proteins, which could lead to new drug targets for conditions 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
Molecular basis
The underlying structure and function of molecules.
Molecular basis refers to the fundamental arrangement of atoms and bonds within a molecule that determines its properties and behavior. Understanding the molecular basis of a phenomenon is crucial in fields like biology and medicine.
Transporters
Proteins that facilitate the movement of molecules across cell membranes.
Transporters are specialized proteins embedded in cell membranes. They play a vital role in regulating what enters and exits cells, ensuring proper nutrient uptake, waste removal, and signaling between cells.
Secondary active transporters
Transporters that utilize an ion gradient to power the transport of another molecule.
Secondary active transporters are a type of protein found in cell membranes. They use the energy stored in an electrochemical gradient of ions (like sodium or potassium) to move other molecules against their concentration gradient. This is essential for nutrient uptake, waste removal, and various signaling processes.
Drug targets
Molecules or pathways that are targeted by drugs to achieve a therapeutic effect.
Drug targets are specific molecules or biological processes involved in disease development. By inhibiting or activating these targets, drugs can modulate disease progression and alleviate symptoms.
Cancer
A disease characterized by uncontrolled cell growth and spread.
Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. It arises from genetic mutations that disrupt normal cellular processes.
Diabetes
A metabolic disorder characterized by high blood sugar levels.
Diabetes is a chronic condition where the body either doesn't produce enough insulin or can't effectively use the insulin it produces. This leads to elevated blood sugar levels, affecting various organs and systems.
Neurological disorders
Conditions affecting the nervous system.
Neurological disorders encompass a wide range of conditions that impact the brain, spinal cord, and peripheral nerves. They can cause symptoms such as seizures, paralysis, cognitive impairment, and sensory disturbances.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:33:49|
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 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 19,207,806 | 22,675 | 847.09 | 0 hrs 2 mins |
| 2 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 15,724,718 | 30,715 | 511.96 | 0 hrs 3 mins |
| 3 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 13,296,958 | 92,444 | 143.84 | 0 hrs 10 mins |
| 4 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 11,207,476 | 92,329 | 121.39 | 0 hrs 12 mins |
| 5 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 9,320,571 | 7,075 | 1317.40 | 0 hrs 1 mins |
| 6 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 9,072,235 | 83,385 | 108.80 | 0 hrs 13 mins |
| 7 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 7,870,329 | 71,043 | 110.78 | 0 hrs 13 mins |
| 8 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 6,088,135 | 75,971 | 80.14 | 0 hrs 18 mins |
| 9 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,668,314 | 69,683 | 66.99 | 0 hrs 21 mins |
| 10 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,262,137 | 67,236 | 63.39 | 0 hrs 23 mins |
| 11 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,860,116 | 58,502 | 48.89 | 0 hrs 29 mins |
| 12 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 2,722,428 | 57,619 | 47.25 | 0 hrs 30 mins |
| 13 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,362,003 | 54,752 | 43.14 | 0 hrs 33 mins |
| 14 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,360,701 | 54,815 | 43.07 | 0 hrs 33 mins |
| 15 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,201,239 | 52,334 | 42.06 | 0 hrs 34 mins |
| 16 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,016,877 | 68,333 | 29.52 | 0 hrs 49 mins |
| 17 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 1,963,839 | 7,075 | 277.57 | 0 hrs 5 mins |
| 18 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,906,233 | 51,404 | 37.08 | 0 hrs 39 mins |
| 19 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,511,969 | 47,585 | 31.77 | 0 hrs 45 mins |
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| 20 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,449,218 | 47,260 | 30.66 | 0 hrs 47 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:33:49|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|