RESEARCH: CANCER
FOLDING PROJECT #17792 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 109,778
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

The project relates to special proteins that move molecules across cell walls using energy from ion gradients. These transporters are important for many functions in living things and can be used as targets for treating diseases like cancer and diabetes. Simulations will help us understand how these proteins work.

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

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: Biotechnology
Membrane Transport / Cellular Processes

Secondary active transporters are a crucial type of protein 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 essential for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Many of these transporters are also targets for drugs used to treat diseases like cancer, diabetes, and neurological disorders.


Ion Gradient

A difference in ion concentration across a membrane.

Scientific: Pharmaceutical
Biochemistry / Membrane Transport

An ion gradient is a difference in the concentration of charged particles (ions) on either side of a cell membrane. This difference in concentration creates an electrochemical potential that can be used to drive cellular processes, such as the transport of molecules across the membrane. Ion gradients are essential for maintaining cell function and are often exploited by drugs to target specific biological pathways.


Proteins

Large biomolecules composed of amino acids.

Scientific: Biotechnology
Biochemistry / Cellular Structure

Proteins are essential macromolecules that perform a wide variety of functions in living organisms. They are made up of chains of amino acids folded into complex three-dimensional structures. Proteins play crucial roles in cellular processes such as catalysis, transport, signaling, and structural support.


Cell Membranes

Thin lipid bilayers that enclose cells and regulate the passage of substances.

Scientific: Biomedical
Biology / Cellular Structure

Cell membranes are essential barriers that separate the internal environment of a cell from its surroundings. They are composed primarily of lipids and proteins arranged in a bilayer structure. Cell membranes play crucial roles in maintaining cellular integrity, regulating transport of molecules, and mediating communication between cells.


Drug Targets

Molecules or cellular processes that are targeted by drugs to treat diseases.

Technical: Pharmaceutical
Pharmacology / Disease Treatment

Drug targets are specific molecules or pathways within cells that are involved in the development or progression of diseases. Drugs are designed to interact with these targets and modulate their activity to achieve a therapeutic effect. Identifying effective drug targets is a crucial step in the drug discovery process.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:10
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 7,081,214 184,271 38.43 0 hrs 37 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,845,244 181,156 37.79 0 hrs 38 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,042,119 174,346 34.66 0 hrs 42 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,892,114 164,203 29.79 0 hrs 48 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,166,910 155,599 26.78 0 hrs 54 mins
6 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,076,630 155,704 26.18 0 hrs 55 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,063,175 154,226 26.35 0 hrs 55 mins
8 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,260,113 143,384 22.74 1 hrs 3 mins
9 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,056,043 141,483 21.60 1 hrs 7 mins
10 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,931,901 138,946 21.10 1 hrs 8 mins
11 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,073,692 123,831 16.75 1 hrs 26 mins
12 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,779,733 117,413 15.16 1 hrs 35 mins
13 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,466,851 109,005 13.46 1 hrs 47 mins
14 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,134,451 100,760 11.26 2 hrs 8 mins
15 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,095,141 99,486 11.01 2 hrs 11 mins
16 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,067,813 98,871 10.80 2 hrs 13 mins
17 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 508,400 77,077 6.60 3 hrs 38 mins
18 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 501,485 77,118 6.50 3 hrs 41 mins
19 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 449,128 74,630 6.02 3 hrs 59 mins
20 P106-090
GP106 [P106-090]
Nvidia GP106 317,635 66,320 4.79 5 hrs 1 mins
21 Radeon RX 6600/6600 XT/6600M
Navi 23 [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 288,351 64,010 4.50 5 hrs 20 mins
22 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 156,018 48,108 3.24 7 hrs 24 mins
23 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 94,117 43,933 2.14 11 hrs 12 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:10
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make