RESEARCH: CANCER
FOLDING PROJECT #17770 PROFILE

PROJECT TEAM

Manager(s): Matthew Chan
Institution: University of Illinois at Urbana-Champaign

WORK UNIT INFO

Atoms: 143,182
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project studies how proteins use ion power to move molecules across cell membranes. These proteins are important for many processes in the body and are even drug targets for diseases like cancer and diabetes. By simulating these proteins, researchers can learn more about how they work and potentially develop new treatments.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Projects 17745-17750 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

Note: Glossary items are a high level summary and may not be 100% accurate.

Secondary active transporters

Proteins that use ion gradients to transport molecules across cell membranes.

Scientific: Pharmaceutical
Biotechnology / Membrane Transport

Secondary active transporters are essential proteins found in all living organisms. They work by utilizing the energy stored in an ion gradient to move other molecules across cell membranes. This process is crucial for various cellular functions, including nutrient uptake, waste removal, and signal transduction. These transporters are also important drug targets because they play a role in diseases like cancer, diabetes, and neurological disorders.


Ion gradient

A difference in ion concentration across a membrane.

Scientific: Pharmaceutical
Biotechnology / Cellular Transport

An ion gradient refers to the unequal distribution of ions (electrically charged atoms) across a cell membrane. This difference in concentration creates an electrochemical potential that can be used by cells to perform various functions, such as transporting molecules and generating electrical signals. The movement of ions across membranes is crucial for maintaining cellular homeostasis.


Proteins

Large, complex molecules that perform a wide variety of functions in living organisms.

Scientific: Pharmaceutical
Biotechnology / Molecular Biology

Proteins are essential macromolecules found in all living organisms. They play diverse roles, including catalyzing biochemical reactions (enzymes), transporting molecules, providing structural support, and transmitting signals. Proteins are made up of chains of amino acids, folded into complex three-dimensional structures that determine their function.


Cell membranes

Thin, flexible barriers that surround cells and regulate the passage of molecules in and out.

Scientific: Pharmaceutical
Biotechnology / Cellular Biology

Cell membranes are crucial components of all living cells. They act as selective barriers, controlling the movement of substances into and out of the cell. This regulation is essential for maintaining cellular homeostasis and carrying out various functions. Cell membranes are composed primarily of lipids and proteins.


Drug targets

Molecules or pathways that are involved in disease processes and can be targeted by drugs.

Technical: Pharmaceutical
Pharmaceutical / Drug Discovery

Drug targets are specific molecules or biological pathways that play a role in the development or progression of diseases. By targeting these molecules with drugs, researchers aim to disrupt the disease process and alleviate symptoms. Drug targets can include proteins, enzymes, receptors, or genes.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:43
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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,114,864 172,430 47.06 0 hrs 31 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 7,434,530 172,095 43.20 0 hrs 33 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,428,338 167,367 44.38 0 hrs 32 mins
4 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,917,046 157,514 37.57 0 hrs 38 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,933,516 148,462 33.23 0 hrs 43 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,587,135 144,903 31.66 0 hrs 45 mins
7 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,499,463 144,463 31.15 0 hrs 46 mins
8 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,391,262 142,504 30.82 0 hrs 47 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,740,787 135,385 27.63 0 hrs 52 mins
10 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,127,587 127,569 24.52 0 hrs 59 mins
11 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 3,059,955 127,498 24.00 0 hrs 60 mins
12 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,841,116 123,087 23.08 1 hrs 2 mins
13 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,320,128 111,391 20.83 1 hrs 9 mins
14 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,219,006 113,632 19.53 1 hrs 14 mins
15 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,848,581 110,175 16.78 1 hrs 26 mins
16 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,633,362 103,209 15.83 1 hrs 31 mins
17 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,378,666 97,336 14.16 1 hrs 42 mins
18 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,281,529 94,928 13.50 1 hrs 47 mins
19 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,213,234 93,564 12.97 1 hrs 51 mins
20 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,174,779 91,870 12.79 1 hrs 53 mins
21 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,152,546 91,593 12.58 1 hrs 54 mins
22 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,133,853 90,534 12.52 1 hrs 55 mins
23 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 863,439 80,785 10.69 2 hrs 15 mins
24 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 666,762 76,310 8.74 2 hrs 45 mins
25 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 660,255 76,006 8.69 2 hrs 46 mins
26 Radeon VII
Vega 20 [Radeon VII] 13,284
AMD Vega 20 580,490 73,338 7.92 3 hrs 2 mins
27 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 425,867 65,784 6.47 3 hrs 42 mins
28 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 420,359 65,759 6.39 3 hrs 45 mins
29 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 326,000 60,068 5.43 4 hrs 25 mins
30 Radeon RX Vega M XT/ M GH
[Radeon RX Vega M XT/ M GH]
AMD 259,394 54,931 4.72 5 hrs 5 mins
31 GeForce GTX 750
GM107 [GeForce GTX 750] 1111
Nvidia GM107 109,362 41,836 2.61 9 hrs 11 mins
32 FirePro W4100
Cape Verde GL [FirePro W4100]
AMD Cape Verde GL 17,737 21,426 0.83 28 hrs 59 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:43
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make