RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17941 PROFILE

PROJECT TEAM

Manager(s): Arnav Paul
Institution: University of Illinois

WORK UNIT INFO

Atoms: 94,854
Core: 0x23
Status: Public

TLDR; PROJECT SUMMARY AI BETA

The project relates to proteins called secondary active transporters that use ion power to move molecules across cell membranes. These proteins are found everywhere in nature and help with many important processes, including treating diseases like cancer and diabetes. Simulations will help us 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.

Molecular basis

Fundamental mechanisms at a molecular level.

Scientific: Biopharmaceuticals
Biotechnology / Cellular Transport

This refers to the underlying processes and interactions at the molecular level that determine how something functions. In this case, it's looking at the specific molecules and their relationships involved in how secondary active transporters work.


Secondary active transporters

Membrane proteins that use an ion gradient to transport molecules across cell membranes.

Scientific: Biopharmaceuticals
Biotechnology / Cellular Transport

These are specialized proteins found in cell membranes. They help move various substances (like nutrients or drugs) across the membrane by using the energy stored in an ion concentration difference. This is like a 'coupled transport' system, where one molecule's movement helps power another.


Ion gradient

Difference in concentration of charged particles (ions) across a membrane.

Scientific: Biopharmaceuticals
Biotechnology / Cellular Transport

Think of it like this: imagine two sides of a barrier (the cell membrane). On one side, there are more positively charged particles than the other. This difference in concentration is called an ion gradient and it's a form of stored energy that cells can use.


Drug targets

Molecules or processes that are involved in a disease and can be targeted by drugs.

Technical: Biopharmaceuticals
Pharmacology / Disease Treatment

When scientists are developing new medications, they look for specific molecules or pathways (like secondary active transporters) that are essential to the development or progression of a disease. These 'drug targets' become the focus of research because if we can block or manipulate them, we can potentially treat the illness.


Simulations

Computer models used to study complex biological processes.

Scientific: Biopharmaceuticals
Biotechnology / Computational Biology

Simulations are like virtual experiments. Scientists use computer programs to create models of biological systems (like a cell or a protein) and then run them on computers. This allows them to explore how things work in a controlled environment and test different hypotheses without needing to conduct real-life experiments.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:33:45
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 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 18,280,102 100,138 182.55 0 hrs 8 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 14,632,467 65,026 225.02 0 hrs 6 mins
3 GeForce RTX 4070 SUPER
AD104 [GeForce RTX 4070 SUPER]
Nvidia AD104 10,670,215 6,417 1662.80 0 hrs 1 mins
4 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 9,960,466 82,242 121.11 0 hrs 12 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 9,846,462 83,089 118.51 0 hrs 12 mins
6 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 8,872,645 78,245 113.40 0 hrs 13 mins
7 RTX 5000 Ada Generation Laptop GPU
AD103GLM [RTX 5000 Ada Generation Laptop GPU]
Nvidia AD103GLM 6,548,496 71,876 91.11 0 hrs 16 mins
8 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,119,034 66,356 77.15 0 hrs 19 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,568,828 58,916 60.57 0 hrs 24 mins
10 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,811,101 6,417 438.07 0 hrs 3 mins
11 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,771,865 56,428 49.12 0 hrs 29 mins
12 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,704,668 53,101 50.93 0 hrs 28 mins
13 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,357,634 50,339 46.84 0 hrs 31 mins
14 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,232,410 50,898 43.86 0 hrs 33 mins
15 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,963,658 6,417 306.01 0 hrs 5 mins
16 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,466,095 43,315 33.85 0 hrs 43 mins
17 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,264,976 6,417 197.13 0 hrs 7 mins
18 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,075,651 40,417 26.61 0 hrs 54 mins
19 RX 5600 OEM/5600XT/5700/5700XT
Navi 10 [RX 5600 OEM/5600XT/5700/5700XT]
AMD Navi 10 869,626 8,859 98.16 0 hrs 15 mins
20 GeForce GTX 1050 LP
GP107 [GeForce GTX 1050 LP] 1862
Nvidia GP107 246,842 23,952 10.31 2 hrs 20 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:33:45
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make