RESEARCH: CANCER
FOLDING PROJECT #17761 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 116,677
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project explores how proteins use ion power to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and are important for things like fighting diseases. By studying them, we can learn about a universal rule of how these proteins work.

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 utilize the energy stored in an ion gradient to move various molecules across cell membranes. These transporters play a crucial role in many biological processes, including nutrient uptake, waste removal, and signal transduction. Their importance makes them attractive targets for drug development to treat diseases like cancer, diabetes, and neurological disorders.


Ion gradient

A difference in ion concentration across a cell membrane.

Scientific: Pharmaceutical
Biotechnology / Membrane transport

An ion gradient is a key concept in understanding how cells maintain their internal environment and carry out essential functions. It refers to the unequal distribution of charged particles (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that can be harnessed by proteins like secondary active transporters to move molecules against their concentration gradient.


Cell membrane

A thin layer that surrounds every cell.

Scientific: Pharmaceutical
Biotechnology / Cellular biology

The cell membrane is a vital structure that separates the inside of a cell from its external environment. It acts as a selective barrier, controlling the passage of molecules in and out of the cell. This regulation is essential for maintaining cellular homeostasis and allowing cells to interact with their surroundings.


Proteins

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

Scientific: Pharmaceutical
Biotechnology / Molecular biology

Proteins are the workhorses of cells, carrying out a vast array of tasks essential for life. They act as enzymes, catalyzing biochemical reactions; provide structural support; transport molecules; and regulate cellular processes. Their diverse functions make them crucial targets for drug development.


Drug targets

Molecules or biological pathways that are the focus of drug development.

Technical: Biotechnology
Pharmaceutical / Drug discovery

Drug targets are specific molecules or pathways within cells that are implicated in disease processes. Identifying and targeting these pathways with drugs can help to treat or manage a variety of conditions. Drug target discovery is a crucial step in the pharmaceutical research process.


Simulations

Computer models used to mimic biological processes.

Technical: Pharmaceutical
Biotechnology / Computational biology

Simulations are powerful tools in biotechnology, allowing researchers to study complex biological systems in a virtual environment. They can be used to predict protein interactions, drug binding affinities, and the behavior of cells under different conditions. Simulations provide valuable insights that complement experimental research.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:57
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 7,875,611 209,833 37.53 0 hrs 38 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,876,720 198,189 34.70 0 hrs 42 mins
3 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 6,705,417 199,179 33.67 0 hrs 43 mins
4 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 5,410,593 186,241 29.05 0 hrs 50 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,754,901 178,811 26.59 0 hrs 54 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,358,695 173,480 25.13 0 hrs 57 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,576,054 162,654 21.99 1 hrs 5 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,034,098 154,514 19.64 1 hrs 13 mins
9 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,622,615 147,112 17.83 1 hrs 21 mins
10 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,833,186 129,795 14.12 1 hrs 42 mins
11 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,534,634 122,862 12.49 1 hrs 55 mins
12 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,438,478 101,135 14.22 1 hrs 41 mins
13 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,296,955 116,094 11.17 2 hrs 9 mins
14 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,230,492 114,188 10.78 2 hrs 14 mins
15 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,205,541 113,480 10.62 2 hrs 16 mins
16 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,146,120 111,137 10.31 2 hrs 20 mins
17 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,120,477 108,935 10.29 2 hrs 20 mins
18 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,110,394 110,476 10.05 2 hrs 23 mins
19 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 745,268 96,427 7.73 3 hrs 6 mins
20 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 655,988 92,742 7.07 3 hrs 24 mins
21 Radeon VII
Vega 20 [Radeon VII] 13,284
AMD Vega 20 564,101 88,291 6.39 3 hrs 45 mins
22 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 462,434 82,431 5.61 4 hrs 17 mins
23 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 410,378 79,292 5.18 4 hrs 38 mins
24 Radeon RX 6600/6600 XT/6600M
Navi 23 [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 304,727 71,788 4.24 5 hrs 39 mins
25 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 302,488 71,365 4.24 5 hrs 40 mins
26 Radeon RX Vega M XT/ M GH
[Radeon RX Vega M XT/ M GH]
AMD 235,735 63,845 3.69 6 hrs 30 mins
27 Radeon R9 200/300 Series
Tonga [Radeon R9 200/300 Series]
AMD Tonga 37,939 28,699 1.32 18 hrs 9 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:57
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make