RESEARCH: CANCER
FOLDING PROJECT #17779 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois Urbana-Champaign
WORK UNIT INFO
Atoms: 108,517Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. They're found in all living things and help with transporting things like drugs. The project looks at how these transporters work differently, but share the same basic principle of using ions for power.
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 fundamental principles governing the structure and function of molecules.
Molecular basis refers to the underlying principles that explain how molecules are structured and how they interact with each other. In this context, it likely pertains to understanding the structural components and interactions of secondary active transporters at a molecular level.
Secondary active transporters
Membrane proteins that use an ion gradient to transport molecules across cell membranes.
Secondary active transporters are specialized proteins embedded within cell membranes. They utilize the energy stored in an electrochemical gradient of ions, typically sodium or protons, to drive the movement of other molecules against their concentration gradient. This process is essential for various cellular functions, including nutrient uptake, waste removal, and signal transduction.
Ion gradient
A difference in ion concentration across a membrane.
An ion gradient refers to an uneven distribution of ions, such as sodium or potassium, between two regions separated by a membrane. This concentration difference creates a potential energy that can be harnessed for cellular processes like transporting molecules or generating electrical signals.
Drug targets
Molecules or cellular processes that are targeted by drugs to achieve therapeutic effects.
Drug targets represent specific molecules or biological pathways involved in disease pathogenesis. By inhibiting or modulating these targets, drugs aim to alleviate symptoms, halt disease progression, or cure the condition.
Simulations
Computer-based models that mimic real-world processes.
Simulations involve using computer algorithms to recreate and analyze complex systems or processes. In this context, simulations are likely employed to model the behavior of secondary active transporters under various conditions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:30|
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 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,207,112 | 183,514 | 39.27 | 0 hrs 37 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,812,063 | 177,087 | 38.47 | 0 hrs 37 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 6,615,924 | 177,915 | 37.19 | 0 hrs 39 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,139,718 | 174,015 | 35.28 | 0 hrs 41 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,620,920 | 157,721 | 29.30 | 0 hrs 49 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,543,865 | 157,521 | 28.85 | 0 hrs 50 mins |
| 7 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,073,749 | 150,880 | 27.00 | 0 hrs 53 mins |
| 8 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 3,782,337 | 148,842 | 25.41 | 0 hrs 57 mins |
| 9 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,521,548 | 129,126 | 19.53 | 1 hrs 14 mins |
| 10 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,429,654 | 128,402 | 18.92 | 1 hrs 16 mins |
| 11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,179,000 | 116,011 | 18.78 | 1 hrs 17 mins |
| 12 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,099,465 | 121,496 | 17.28 | 1 hrs 23 mins |
| 13 | Radeon RX 6700/6700 XT / 6800M Navi 22 [Radeon RX 6700/6700 XT / 6800M] |
AMD | Navi 22 | 1,736,273 | 114,546 | 15.16 | 1 hrs 35 mins |
| 14 | Radeon RX 6900 XT Navi 21 [Radeon RX 6900 XT] |
AMD | Navi 21 | 1,537,950 | 109,761 | 14.01 | 1 hrs 43 mins |
| 15 | Radeon RX 6800/6800 XT / 6900 XT Navi 21 [Radeon RX 6800/6800 XT / 6900 XT] |
AMD | Navi 21 | 1,407,579 | 106,430 | 13.23 | 1 hrs 49 mins |
| 16 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,178,978 | 100,288 | 11.76 | 2 hrs 2 mins |
| 17 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,152,684 | 99,375 | 11.60 | 2 hrs 4 mins |
| 18 | Tesla M40 GM200GL [Tesla M40] 6844 |
Nvidia | GM200GL | 1,120,112 | 98,685 | 11.35 | 2 hrs 7 mins |
| 19 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 779,566 | 88,054 | 8.85 | 2 hrs 43 mins |
|
|
|||||||
| 20 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 533,968 | 77,203 | 6.92 | 3 hrs 28 mins |
| 21 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 408,330 | 70,653 | 5.78 | 4 hrs 9 mins |
| 22 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 379,718 | 66,537 | 5.71 | 4 hrs 12 mins |
| 23 | GeForce GTX 980M GM204 [GeForce GTX 980M] 3189 |
Nvidia | GM204 | 335,305 | 66,318 | 5.06 | 4 hrs 45 mins |
| 24 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 306,760 | 64,404 | 4.76 | 5 hrs 2 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:30|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|