RESEARCH: CANCER
FOLDING PROJECT #17737 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 51,029Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project explores how proteins use ion power to move molecules across cell membranes. These 'secondary active transporters' are important for many bodily functions and are targets for treating diseases like cancer and diabetes. By studying them, we can learn about how different types of proteins work together.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Projects 17711-17724 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 disease like cancer, diabetes, and neurological disorders.
These simulations 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 crucial proteins found in all living organisms. They work by using an existing ion gradient to power the movement of other molecules across cell membranes. These transporters are involved in various essential cellular processes and are also important 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 refers to the unequal distribution of ions, such as sodium or potassium, on either side of a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including nerve impulse transmission and nutrient uptake.
Drug Targets
Molecules or pathways that are potential therapeutic targets for drug development.
Drug targets are specific molecules or cellular pathways involved in disease processes. By inhibiting or modulating these targets, drugs can exert their therapeutic effects. Identifying and targeting appropriate drug targets is a crucial step in the drug discovery process.
Simulations
Computer-based models that mimic biological processes.
Simulations are powerful tools used in bioinformatics to study complex biological systems. By creating computer models that mimic real-world processes, researchers can explore various scenarios and gain insights into how biological systems function.
Proteins
Large biomolecules essential for various cellular functions.
Proteins are the workhorses of cells, performing a wide range of functions such as catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Their diverse structures and functions make them vital components of all living organisms.
Cancer
A group of diseases characterized by abnormal cell growth.
Cancer is a complex group of diseases where cells grow uncontrollably and spread to other parts of the body. This uncontrolled growth can result from genetic mutations or environmental factors, leading to various forms of cancer affecting different organs and tissues.
Diabetes
A group of metabolic disorders characterized by high blood sugar levels.
Diabetes is a chronic condition where the body struggles to regulate blood sugar levels. There are different types of diabetes, including type 1 and type 2. In type 1 diabetes, the immune system attacks insulin-producing cells, while in type 2 diabetes, the body becomes resistant to insulin or doesn't produce enough. High blood sugar can lead to various complications affecting the heart, kidneys, eyes, and nerves.
Neurological Disorders
Conditions that affect the nervous system.
Neurological disorders encompass a wide range of conditions affecting the brain, spinal cord, and nerves. These disorders can cause various symptoms, including seizures, paralysis, memory problems, and cognitive decline. Examples include Alzheimer's disease, Parkinson's disease, stroke, and multiple sclerosis.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:36:17|
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 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 6,133,718 | 85,191 | 72.00 | 0 hrs 20 mins |
| 2 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 4,987,276 | 80,610 | 61.87 | 0 hrs 23 mins |
| 3 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,738,350 | 79,223 | 59.81 | 0 hrs 24 mins |
| 4 | Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000] |
Nvidia | TU102GL | 4,030,821 | 74,644 | 54.00 | 0 hrs 27 mins |
| 5 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 3,755,017 | 73,883 | 50.82 | 0 hrs 28 mins |
| 6 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,060,763 | 68,699 | 44.55 | 0 hrs 32 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 3,010,220 | 68,270 | 44.09 | 0 hrs 33 mins |
| 8 | GeForce RTX 2080 TU104 [GeForce RTX 2080] |
Nvidia | TU104 | 2,836,885 | 66,815 | 42.46 | 0 hrs 34 mins |
| 9 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 2,797,173 | 67,101 | 41.69 | 0 hrs 35 mins |
| 10 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 2,699,771 | 65,899 | 40.97 | 0 hrs 35 mins |
| 11 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,378,138 | 62,993 | 37.75 | 0 hrs 38 mins |
| 12 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] M 7465 |
Nvidia | TU106 | 2,357,623 | 63,442 | 37.16 | 0 hrs 39 mins |
| 13 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,314,290 | 62,254 | 37.17 | 0 hrs 39 mins |
| 14 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 2,235,569 | 62,099 | 36.00 | 0 hrs 40 mins |
| 15 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 2,070,378 | 60,578 | 34.18 | 0 hrs 42 mins |
| 16 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,009,020 | 59,728 | 33.64 | 0 hrs 43 mins |
| 17 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 1,759,058 | 57,227 | 30.74 | 0 hrs 47 mins |
| 18 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,699,896 | 56,585 | 30.04 | 0 hrs 48 mins |
| 19 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,461,737 | 54,157 | 26.99 | 0 hrs 53 mins |
|
|
|||||||
| 20 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,321,992 | 52,022 | 25.41 | 0 hrs 57 mins |
| 21 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,173,748 | 50,136 | 23.41 | 1 hrs 2 mins |
| 22 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,166,796 | 50,538 | 23.09 | 1 hrs 2 mins |
| 23 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,106,311 | 49,189 | 22.49 | 1 hrs 4 mins |
| 24 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,087,326 | 41,087 | 26.46 | 0 hrs 54 mins |
| 25 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] 4608 |
Nvidia | TU106M | 1,076,482 | 48,553 | 22.17 | 1 hrs 5 mins |
| 26 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 964,331 | 46,931 | 20.55 | 1 hrs 10 mins |
| 27 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 697,091 | 42,219 | 16.51 | 1 hrs 27 mins |
| 28 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 661,280 | 41,528 | 15.92 | 1 hrs 30 mins |
| 29 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 513,164 | 38,087 | 13.47 | 1 hrs 47 mins |
| 30 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 451,975 | 36,352 | 12.43 | 1 hrs 56 mins |
| 31 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 2984 |
Nvidia | TU116 | 411,861 | 35,268 | 11.68 | 2 hrs 3 mins |
| 32 | GeForce GTX 960 GM206 [GeForce GTX 960] 2308 |
Nvidia | GM206 | 308,073 | 32,145 | 9.58 | 2 hrs 30 mins |
| 33 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 212,321 | 28,512 | 7.45 | 3 hrs 13 mins |
| 34 | GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile] |
Nvidia | GP107M | 210,391 | 28,429 | 7.40 | 3 hrs 15 mins |
| 35 | GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634 |
Nvidia | GK104 | 135,349 | 21,775 | 6.22 | 3 hrs 52 mins |
| 36 | Quadro K2200 GM107GL [Quadro K2200] |
Nvidia | GM107GL | 109,321 | 22,859 | 4.78 | 5 hrs 1 mins |
| 37 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 95,790 | 19,843 | 4.83 | 4 hrs 58 mins |
| 38 | GeForce GT 1030 GP108 [GeForce GT 1030] 1127 |
Nvidia | GP108 | 88,678 | 21,310 | 4.16 | 5 hrs 46 mins |
| 39 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 74,041 | 20,071 | 3.69 | 6 hrs 30 mins |
|
|
|||||||
| 40 | GeForce GT 710 GK208B [GeForce GT 710] 366 |
Nvidia | GK208B | 8,666 | 9,743 | 0.89 | 26 hrs 59 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:36:17|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|