RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17931 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 116,677
Core: 0x23
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. These proteins are found everywhere and are important for many bodily functions. This project uses simulations to understand how these transporters work, which could lead to new treatments 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 ions to transport molecules across cell membranes.

Scientific: Biotechnology
Cellular Biology / Membrane Transport

Secondary active transporters are essential proteins found in all living organisms. They utilize the energy stored in an ion gradient to move other molecules across cell membranes. This process is vital for various cellular functions, including nutrient uptake, waste removal, and signal transduction. Many of these transporters are drug targets for treating diseases like cancer, diabetes, and neurological disorders.


Ion gradient

A difference in ion concentration across a cell membrane.

Scientific: Biotechnology
Cellular Biology / Membrane Transport

An ion gradient is a fundamental concept in cellular biology. It refers to the unequal distribution of ions, such as sodium (Na+) and potassium (K+), across a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including nerve impulse transmission, muscle contraction, and nutrient uptake.


Simulations

Computer models used to study biological systems.

Scientific: Pharmaceutical
Biotechnology / Computational Biology

Simulations are powerful tools used in computational biology to understand complex biological processes. Researchers create computer models that mimic the behavior of cells, molecules, or entire organisms. These simulations allow scientists to explore different scenarios, test hypotheses, and gain insights into biological phenomena.


Drug targets

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

Scientific: Pharmaceutical
Pharmacology / Drug Discovery

Drug targets are essential components of the drug discovery process. They are specific molecules or cellular pathways that play a role in the development or progression of a disease. By targeting these drug targets, researchers aim to develop therapies that can effectively treat or prevent diseases.


Cancer

A group of diseases characterized by uncontrolled cell growth.

Medical: Healthcare
Oncology / Tumor Biology

Cancer is a complex group of diseases that involves abnormal and uncontrolled cell growth. These cells can invade surrounding tissues and spread to other parts of the body. There are many different types of cancer, each with its own unique characteristics and treatments.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:34:00
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 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 18,008,736 28,763 626.11 0 hrs 2 mins
2 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 16,756,937 138,868 120.67 0 hrs 12 mins
3 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 15,790,305 124,768 126.56 0 hrs 11 mins
4 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 11,275,193 122,362 92.15 0 hrs 16 mins
5 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 10,115,526 113,568 89.07 0 hrs 16 mins
6 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 7,340,616 102,320 71.74 0 hrs 20 mins
7 GeForce RTX 4060 Ti
AD106 [GeForce RTX 4060 Ti]
Nvidia AD106 6,719,733 178,361 37.67 0 hrs 38 mins
8 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 6,285,024 79,300 79.26 0 hrs 18 mins
9 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 3,449,112 79,511 43.38 0 hrs 33 mins
10 Radeon RX 7700XT/7800XT
Navi 32 [Radeon RX 7700XT/7800XT]
AMD Navi 32 3,021,338 10,140 297.96 0 hrs 5 mins
11 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,680,451 72,660 36.89 0 hrs 39 mins
12 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,652,822 73,409 36.14 0 hrs 40 mins
13 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,081,495 66,780 31.17 0 hrs 46 mins
14 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,348,853 58,027 23.25 1 hrs 2 mins
15 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 771,302 43,234 17.84 1 hrs 21 mins
16 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 707,300 10,140 69.75 0 hrs 21 mins
17 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 583,566 44,578 13.09 1 hrs 50 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:34:00
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make