RESEARCH: MEMBRANE TRANSPORT
FOLDING PROJECT #17929 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 79,000
Core: 0x23
Status: Public

Related Projects

TLDR; PROJECT SUMMARY AI BETA

This project explores how membrane transporters, which move molecules in and out of cells, recognize different types of substances. Scientists chose sugar transporters to study this because they transport various molecules. Understanding how these transporters work can help design drugs that target specific transporters.

Note: This TLDR is a simplication and may not be 100% accurate.

OFFICAL PROJECT DESCRIPTION

Membrane transporters are important for enabling molecules to go in and out of cells.

What is interesting is that while transporters typically have set functions, they can also transport molecules that do not necessarily relate to their function or cellular purpose.

For example, drugs often hijack transporters to enter cells without necessarily resembling the molecules or metabolites which that target transporter normally transports.

The goal of this project is to see how exactly a typical membrane transporter recognizes and transports molecules that look different from one another.

We choose a class of sugar transporters that transports a variety of different types of substrates to satisfy this goal.

Findings from this study can be generalized for the design of molecules (e.g., drugs) specific to a given transporter and its general mechanism.

RELATED TERMS GLOSSARY AI BETA

Note: Glossary items are a high level summary and may not be 100% accurate.

Membrane transporters

Proteins that move molecules across cell membranes.

Scientific: Biotechnology
Cellular biology / Cell transport

Membrane transporters are crucial for cells to take in nutrients and expel waste. They act like gatekeepers, selectively allowing specific molecules to pass through the cell membrane.


Drugs

Chemical substances used to treat or prevent diseases.

Scientific: Medicine
Pharmacology / Drug action

Drugs are designed to interact with specific targets in the body, such as proteins or enzymes. They can have various effects, such as relieving pain, fighting infections, or controlling symptoms.


Transporters

Proteins that move molecules across cell membranes.

Scientific: Biotechnology
Cellular biology / Cell transport

Transporters are essential for cells to maintain their internal environment and function properly. They can transport a variety of molecules, including nutrients, ions, and waste products.


Sugar transporters

Proteins that specifically transport sugar molecules across cell membranes.

Scientific: Biotechnology
Cellular biology / Cell transport

Sugar transporters are responsible for taking up glucose and other sugars from the environment into cells. They play a crucial role in energy metabolism.


Substrates

Molecules that are acted upon by an enzyme.

Scientific: Pharmacology
Biochemistry / Enzyme action

Substrates are the inputs for a chemical reaction catalyzed by an enzyme. They bind to the enzyme's active site and undergo a transformation.


Molecules

Atoms or groups of atoms held together by chemical bonds.

Scientific: Biotechnology
Chemistry / Organic chemistry

Molecules are the building blocks of all matter. They can be simple, like water (H2O), or complex, like DNA.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:34:03
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 18,364,531 379,615 48.38 0 hrs 30 mins
2 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 13,889,332 348,983 39.80 0 hrs 36 mins
3 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 12,353,787 335,017 36.88 0 hrs 39 mins
4 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 11,226,620 254,024 44.20 0 hrs 33 mins
5 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 10,601,474 317,995 33.34 0 hrs 43 mins
6 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 10,194,513 315,718 32.29 0 hrs 45 mins
7 GeForce RTX 4070 SUPER
AD104 [GeForce RTX 4070 SUPER]
Nvidia AD104 8,121,859 239,394 33.93 0 hrs 42 mins
8 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 6,644,630 45,711 145.36 0 hrs 10 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 5,865,618 260,857 22.49 1 hrs 4 mins
10 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 5,703,688 259,350 21.99 1 hrs 5 mins
11 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,660,963 257,113 22.02 1 hrs 5 mins
12 GeForce RTX 4070 Ti SUPER
AD103 [GeForce RTX 4070 Ti SUPER]
Nvidia AD103 5,212,197 45,711 114.03 0 hrs 13 mins
13 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 5,046,999 248,607 20.30 1 hrs 11 mins
14 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,162,468 232,260 17.92 1 hrs 20 mins
15 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 4,107,398 233,599 17.58 1 hrs 22 mins
16 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,983,051 45,711 65.26 0 hrs 22 mins
17 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 2,956,433 208,304 14.19 1 hrs 41 mins
18 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,603,250 184,730 14.09 1 hrs 42 mins
19 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 2,578,554 141,667 18.20 1 hrs 19 mins
20 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 2,390,631 194,779 12.27 1 hrs 57 mins
21 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,892,670 181,108 10.45 2 hrs 18 mins
22 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 818,386 136,977 5.97 4 hrs 1 mins
23 RX 470/480/570/580/590
Ellesmere XT [RX 470/480/570/580/590]
AMD Ellesmere XT 369,309 81,944 4.51 5 hrs 20 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:34:03
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make