RESEARCH: CANCER
FOLDING PROJECT #17787 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 94,899
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. These transporters are found in all living things and are important for many bodily functions. The project relates to understanding how these transporters work, which could lead to new drugs for diseases like cancer and diabetes.

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.

Technical: Pharmaceutical
Biotechnology / Membrane Transport

Secondary active transporters are crucial proteins found in all living organisms. They work by using the energy from an existing ion gradient to move other molecules, like nutrients or drugs, across cell membranes. This process is vital for many biological functions and is often targeted by medications for diseases like cancer and diabetes.


Ion gradient

A difference in concentration of ions across a membrane.

Scientific: Pharmaceutical
Biochemistry / Membrane Transport

An ion gradient refers to the unequal distribution of charged particles (ions) on either side of a cell membrane. This difference in concentration creates electrical potential energy that can be used by cells to power various processes, such as transporting molecules across the membrane.


Simulations

Computer-based models used to mimic biological processes.

Technical: Pharmaceutical
Bioinformatics / Molecular Modeling

Simulations are powerful tools used in bioinformatics and other scientific fields to study complex systems. By creating computer models of biological processes, researchers can explore different scenarios, test hypotheses, and gain insights into how things work at a molecular level.


Proteins

Large biomolecules essential for cell structure and function.

Scientific: Pharmaceutical
Biochemistry / Molecular Biology

Proteins are the workhorses of cells, carrying out a vast array of functions. From building tissues to catalyzing biochemical reactions, proteins are essential for life as we know it.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:17
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 7,579,008 160,395 47.25 0 hrs 30 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 7,455,315 163,948 45.47 0 hrs 32 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,356,180 157,382 46.74 0 hrs 31 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,597,080 137,972 33.32 0 hrs 43 mins
5 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,328,146 135,255 32.00 0 hrs 45 mins
6 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,880,391 130,244 29.79 0 hrs 48 mins
7 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,736,511 129,740 28.80 0 hrs 50 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,094,945 121,792 25.41 0 hrs 57 mins
9 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,438,419 112,321 21.71 1 hrs 6 mins
10 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,140,790 107,152 19.98 1 hrs 12 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,913,050 103,815 18.43 1 hrs 18 mins
12 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,213,016 88,435 13.72 1 hrs 45 mins
13 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,072,387 85,642 12.52 1 hrs 55 mins
14 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,035,860 84,517 12.26 1 hrs 57 mins
15 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 736,202 75,074 9.81 2 hrs 27 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:17
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make