RESEARCH: CANCER
FOLDING PROJECT #17791 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 117,169
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Secondary active transporters are proteins that use ions to move molecules across cell membranes. They work in all living things and are important targets for treating diseases like cancer and diabetes. This project uses simulations to 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.

Molecular basis

The fundamental structure and function of molecules.

Scientific: Biotechnology
Biochemistry / Transport Proteins

This refers to the underlying principles governing how molecules work at a chemical level. In this context, it likely focuses on how the structure of secondary active transporter proteins enables them to move molecules across cell membranes.


Secondary active transporters

Proteins that use an ion gradient to move molecules across cell membranes.

Technical: Pharmacology
Biochemistry / Membrane Transport

These specialized proteins act like cellular pumps, utilizing the energy stored in an electrochemical gradient of ions (like sodium or potassium) to drive the movement of other molecules (like nutrients or drugs) across cell membranes. This process is crucial for many biological functions and is often targeted by medications.


Ion gradient

A difference in concentration of ions across a membrane.

Scientific: Biotechnology
Biochemistry / Membrane Transport

This refers to the uneven distribution of electrically charged atoms (ions) across a cell membrane. The movement of these ions creates an electrochemical gradient that can be harnessed by proteins like secondary active transporters to power the transport of other molecules.


Drug targets

Molecules or biological processes that are potential therapeutic targets.

Technical: Medicine
Pharmacology / Disease Treatment

Drug targets are specific molecules or pathways within the body that can be manipulated by medications to treat diseases. Secondary active transporters often serve as drug targets because their function is essential for many physiological processes and their disruption can have therapeutic effects.


Simulations

Computer-based models used to study biological systems.

Technical: Biotechnology
Computational Biology / Drug Discovery

Simulations are powerful tools that allow researchers to virtually experiment and explore the behavior of complex biological systems like secondary active transporters. They can help predict how changes in molecular structure or environment might affect protein function.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:11
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 7,392,497 200,977 36.78 0 hrs 39 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,986,817 197,052 35.46 0 hrs 41 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,353,841 188,145 33.77 0 hrs 43 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,590,040 171,730 26.73 0 hrs 54 mins
5 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,396,229 167,912 26.18 0 hrs 55 mins
6 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,070,741 149,963 20.48 1 hrs 10 mins
7 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,503,775 139,951 17.89 1 hrs 20 mins
8 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,089,975 132,200 15.81 1 hrs 31 mins
9 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,868,509 129,167 14.47 1 hrs 40 mins
10 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,764,530 125,249 14.09 1 hrs 42 mins
11 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,473,344 117,663 12.52 1 hrs 55 mins
12 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,221,840 109,775 11.13 2 hrs 9 mins
13 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,103,580 106,267 10.38 2 hrs 19 mins
14 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 809,808 96,337 8.41 2 hrs 51 mins
15 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 679,022 91,126 7.45 3 hrs 13 mins
16 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 428,448 78,101 5.49 4 hrs 22 mins
17 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 427,903 77,915 5.49 4 hrs 22 mins
18 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 372,083 66,773 5.57 4 hrs 18 mins
19 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 313,848 70,215 4.47 5 hrs 22 mins
20 Radeon RX 460
Baffin XT [Radeon RX 460]
AMD Baffin XT 130,639 52,467 2.49 9 hrs 38 mins
21 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 114,894 50,073 2.29 10 hrs 28 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:11
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make