RESEARCH: CANCER
FOLDING PROJECT #17788 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 131,328
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. They're found everywhere and help transport many things, including drugs. The project uses simulations to understand how these transporters work in different types of proteins.

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 move molecules across cell membranes.

Technical: Biotechnology
Membrane Transport / Cellular Processes

Secondary active transporters are essential proteins found in all living organisms. They help move various molecules across cell membranes by utilizing the energy stored in an existing ion gradient. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. Disruptions in these transporters can lead to various diseases, making them important drug targets.


membrane transporters

Proteins that facilitate the movement of substances across cell membranes.

Technical: Biotechnology
Membrane Transport / Cellular Processes

Membrane transporters are specialized proteins embedded within cell membranes. They play a vital role in regulating the flow of molecules into and out of cells, ensuring proper cellular function. Different types of transporters move specific molecules, such as nutrients, ions, or waste products, through various mechanisms.


ion gradient

A difference in ion concentration across a membrane.

Technical: Biotechnology
Electrochemistry / Cellular Processes

An ion gradient refers to an uneven distribution of charged ions (like sodium or potassium) across a cell membrane. This concentration difference creates a potential energy that can be harnessed for various cellular processes. For example, nerve cells utilize ion gradients to transmit electrical signals.


drug targets

Molecules or pathways that are targeted by drugs to treat diseases.

Technical: Medicine
Pharmacology / Drug Discovery

Drug targets are specific molecules or biological pathways involved in disease development. By targeting these molecules, drugs can either inhibit their function (for example, blocking a harmful enzyme) or enhance their activity (for example, stimulating a beneficial protein). Identifying effective drug targets is crucial for developing new therapies.


simulations

Computer models used to study biological systems.

Technical: Biotechnology
Computational Biology / Drug Discovery

Simulations are powerful tools used in computational biology to model and analyze complex biological processes. By creating virtual representations of cells, molecules, or entire organisms, researchers can investigate how these systems behave under different conditions. Simulations can help accelerate drug discovery by predicting the effects of potential drugs on target molecules.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:16
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 7,696,035 155,695 49.43 0 hrs 29 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,445,409 148,735 50.06 0 hrs 29 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,243,279 149,634 48.41 0 hrs 30 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,631,167 132,339 34.99 0 hrs 41 mins
5 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 4,042,531 126,329 32.00 0 hrs 45 mins
6 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,351,934 118,287 28.34 0 hrs 51 mins
7 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,552,949 109,088 23.40 1 hrs 2 mins
8 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 2,486,870 62,926 39.52 0 hrs 36 mins
9 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,169,700 101,694 21.34 1 hrs 7 mins
10 GeForce RTX 2080 Mobile
TU104M [GeForce RTX 2080 Mobile]
Nvidia TU104M 2,082,426 102,418 20.33 1 hrs 11 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,901,322 99,027 19.20 1 hrs 15 mins
12 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,539,086 92,326 16.67 1 hrs 26 mins
13 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,363,310 87,138 15.65 1 hrs 32 mins
14 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,292,373 86,756 14.90 1 hrs 37 mins
15 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,201,118 84,784 14.17 1 hrs 42 mins
16 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,187,750 84,537 14.05 1 hrs 42 mins
17 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,036,881 80,702 12.85 1 hrs 52 mins
18 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 843,880 72,258 11.68 2 hrs 3 mins
19 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 609,716 66,990 9.10 2 hrs 38 mins
20 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 469,957 61,823 7.60 3 hrs 9 mins
21 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 427,456 60,002 7.12 3 hrs 22 mins
22 Radeon RX 6600/6600 XT/6600M
Navi 23 [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 308,833 53,795 5.74 4 hrs 11 mins
23 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 284,266 47,604 5.97 4 hrs 1 mins
24 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 121,394 39,515 3.07 7 hrs 49 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:16
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make