RESEARCH: CANCER
FOLDING PROJECT #17774 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 58,595
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project studies how proteins move molecules across cell membranes using ion power. These proteins are found everywhere and help treat diseases like cancer and diabetes. Simulations will reveal how these proteins work, no matter their shape.

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.

Transporters

Proteins that move molecules across cell membranes.

Scientific: Pharmaceutical
Biotechnology / Cellular Transport

Transporters are essential proteins that help cells move various substances in and out. They use energy or gradients to carry molecules like nutrients, ions, and waste products. These proteins are crucial for many cellular processes, including nutrient uptake, signaling, and detoxification. Dysfunctional transporters can lead to various diseases.


Secondary Active Transporters

Membrane proteins that use an ion gradient to transport molecules.

Scientific: Pharmaceutical
Biotechnology / Cellular Transport

Secondary active transporters are a specific type of membrane protein that rely on an existing ion gradient to move other molecules across the cell membrane. They couple the movement of one molecule (usually an ion) down its concentration gradient with the movement of another molecule (the solute) against its concentration gradient. This process is vital for various cellular functions, including nutrient uptake, waste removal, and signal transduction.


Ion Gradient

A difference in ion concentration across a membrane.

Scientific: Pharmaceutical
Biotechnology / Cellular Transport

An ion gradient refers to the unequal distribution of charged ions (like sodium, potassium, or calcium) across a cell membrane. This difference in concentration creates an electrical potential, which is essential for many cellular processes, including nerve impulse transmission and muscle contraction. Secondary active transporters utilize this gradient to drive the transport of other molecules.


Drug Targets

Molecules or pathways that are potential sites for drug action.

Scientific: Pharmaceutical
Biotechnology / Pharmaceutical Research

Drug targets are specific molecules or biological pathways involved in disease processes. Scientists aim to develop drugs that can interact with these targets and modulate their activity. By interfering with a target's function, a drug can potentially treat or prevent a disease.


Simulations

Computer models of biological processes.

Scientific: Pharmaceutical
Biotechnology / Computational Biology

Simulations are computer-based representations of real-world phenomena. In biology, simulations can model complex processes like protein interactions, cell signaling pathways, or drug effects. These virtual experiments allow researchers to test hypotheses, explore different scenarios, and gain insights into biological systems without conducting physical experiments.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:37
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 7,104,000 123,333 57.60 0 hrs 25 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,670,224 121,104 55.08 0 hrs 26 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,648,277 114,742 49.23 0 hrs 29 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,136,564 103,671 39.90 0 hrs 36 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,074,997 102,600 39.72 0 hrs 36 mins
6 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,838,975 101,132 37.96 0 hrs 38 mins
7 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,814,851 100,698 37.88 0 hrs 38 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,167,779 95,326 33.23 0 hrs 43 mins
9 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 3,096,177 129,886 23.84 1 hrs 0 mins
10 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,734,510 88,265 30.98 0 hrs 46 mins
11 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,576,016 84,522 30.48 0 hrs 47 mins
12 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,450,356 87,663 27.95 0 hrs 52 mins
13 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,430,446 87,203 27.87 0 hrs 52 mins
14 GeForce RTX 2080 Mobile
TU104M [GeForce RTX 2080 Mobile]
Nvidia TU104M 1,994,623 81,934 24.34 0 hrs 59 mins
15 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,957,593 80,982 24.17 0 hrs 60 mins
16 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,339,571 71,266 18.80 1 hrs 17 mins
17 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 1,231,675 68,447 17.99 1 hrs 20 mins
18 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,117,882 67,280 16.62 1 hrs 27 mins
19 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,081,980 66,371 16.30 1 hrs 28 mins
20 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,042,203 64,184 16.24 1 hrs 29 mins
21 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 570,493 53,478 10.67 2 hrs 15 mins
22 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 560,789 53,437 10.49 2 hrs 17 mins
23 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 455,198 50,250 9.06 2 hrs 39 mins
24 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 346,699 45,410 7.63 3 hrs 9 mins
25 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 341,852 45,170 7.57 3 hrs 10 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:37
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make