RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17933 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 108,493
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. This project uses simulations to understand how these important proteins work, as they're used in treating 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 move molecules across cell membranes.

Technical: Biotechnology
Membrane transport / Cellular physiology

Secondary active transporters are a type of protein found in all living things. They help move different molecules across the barriers of cells by using energy from an existing concentration gradient of ions (like sodium or potassium). This process is essential for many cellular functions, and malfunctions in these transporters can lead to various diseases.


Ion gradient

A difference in concentration of ions across a membrane.

Scientific: Pharmaceuticals
Biochemistry / Membrane transport

An ion gradient is a difference in the number of charged particles (ions) on either side of a cell membrane. These gradients are created by specialized proteins that pump ions against their concentration gradient, using energy from processes like cellular respiration. Ion gradients play a crucial role in many cellular functions, including nerve impulse transmission, muscle contraction, and nutrient uptake.


Simulations

Computer models used to mimic biological processes.

Technical: Biotechnology
Computational biology / Drug discovery

Simulations are computer programs that create virtual environments to represent real-world systems, like cells or molecules. These simulations can be used to study complex biological processes, predict the behavior of drugs, and design new therapies.


Drug targets

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

Technical: Pharmaceuticals
Pharmacology / Disease treatment

Drug targets are specific molecules or cellular processes that contribute to the development or progression of a disease. By targeting these molecules with drugs, scientists aim to interrupt disease pathways and achieve therapeutic effects.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:33:57
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
AD103 [GeForce RTX 4080]
Nvidia AD103 18,784,495 8,750 2146.80 0 hrs 1 mins
2 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 18,549,832 8,750 2119.98 0 hrs 1 mins
3 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 15,977,458 106,247 150.38 0 hrs 10 mins
4 GeForce RTX 4060 Ti
AD106 [GeForce RTX 4060 Ti]
Nvidia AD106 11,500,401 230,497 49.89 0 hrs 29 mins
5 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 10,575,099 105,264 100.46 0 hrs 14 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 9,187,173 102,253 89.85 0 hrs 16 mins
7 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 7,029,406 90,211 77.92 0 hrs 18 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,717,093 73,137 50.82 0 hrs 28 mins
9 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 3,018,842 69,057 43.72 0 hrs 33 mins
10 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,972,566 68,663 43.29 0 hrs 33 mins
11 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 2,833,499 8,750 323.83 0 hrs 4 mins
12 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,483,812 64,072 38.77 0 hrs 37 mins
13 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,025,243 59,931 33.79 0 hrs 43 mins
14 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,686,903 57,207 29.49 0 hrs 49 mins
15 GeForce RTX 3050 8GB
GA107 [GeForce RTX 3050 8GB]
Nvidia GA107 1,663,720 56,833 29.27 0 hrs 49 mins
16 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 1,660,201 8,750 189.74 0 hrs 8 mins
17 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,632,821 55,747 29.29 0 hrs 49 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:33:57
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make