RESEARCH: CANCER
FOLDING PROJECT #17769 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 90,887
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project explores how special proteins move molecules across cell walls using the power of ion gradients. These proteins are found everywhere and play a big role in treating diseases like cancer and diabetes. Studying them helps us understand how they work across different types of organisms.

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

OFFICAL PROJECT DESCRIPTION

Projects 17745-17750 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.

Technical: Biotechnology
Molecular Biology / Membrane Transport

Secondary active transporters are essential proteins found in all living organisms. They utilize an existing ion gradient to power the movement of various molecules across cell membranes. This process is crucial for many cellular functions, including nutrient uptake, waste removal, and signal transduction. Due to their importance in various physiological processes, secondary active transporters are also attractive drug targets for treating a wide range of diseases.


Proteins

Large, complex molecules that perform a wide range of functions in living organisms.

Scientific: Biotechnology
Molecular Biology / Cellular Structure

Proteins are the workhorses of cells, responsible for carrying out countless tasks essential for life. They can act as enzymes to speed up chemical reactions, provide structural support, transport molecules, and participate in signaling pathways. The diversity of protein functions is immense, reflecting their crucial role in all aspects of biology.


Cell membranes

Thin barriers that surround cells, regulating the passage of molecules in and out.

Technical: Biotechnology
Molecular Biology / Cellular Structure

Cell membranes are essential for compartmentalizing cellular processes and maintaining cell integrity. They act as selective barriers, allowing certain molecules to pass through while restricting others. This control over molecular movement is crucial for maintaining cellular homeostasis and enabling communication with the external environment.


Ion gradient

A difference in concentration of charged ions across a membrane.

Technical: Biotechnology
Molecular Biology / Membrane Transport

An ion gradient is a fundamental concept in cellular energy and transport. It refers to an uneven distribution of charged particles (ions) across a membrane. This difference in concentration creates an electrochemical potential that can be harnessed by cells to power various processes, such as the movement of molecules across membranes.


Drug targets

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

Technical: Biotechnology
Pharmacology / Disease Treatment

Drug targets are key components of pharmaceutical research and development. By identifying molecules or cellular pathways that contribute to disease progression, scientists can design drugs that specifically interfere with these targets, aiming to alleviate or cure the disease. Understanding drug targets is crucial for developing effective and safe medications.


Simulations

Computer models used to represent and study biological systems.

Technical: Biotechnology
Bioinformatics / Computational Biology

Simulations are powerful tools in bioinformatics, allowing researchers to explore complex biological processes without conducting physical experiments. By creating mathematical models that mimic real-world interactions, scientists can gain insights into how cells function, predict the effects of genetic mutations, and design new therapies.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:45
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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 8,054,859 159,086 50.63 0 hrs 28 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,117,804 152,835 46.57 0 hrs 31 mins
3 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 7,020,882 154,394 45.47 0 hrs 32 mins
4 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,916,695 143,809 41.14 0 hrs 35 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 5,418,946 139,472 38.85 0 hrs 37 mins
6 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,557,709 131,878 34.56 0 hrs 42 mins
7 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,528,854 131,993 34.31 0 hrs 42 mins
8 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 4,116,663 128,645 32.00 0 hrs 45 mins
9 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,399,037 119,950 28.34 0 hrs 51 mins
10 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,320,622 119,142 27.87 0 hrs 52 mins
11 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,817,336 114,128 24.69 0 hrs 58 mins
12 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,671,634 111,318 24.00 0 hrs 60 mins
13 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,516,277 108,706 23.15 1 hrs 2 mins
14 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,254,889 113,643 19.84 1 hrs 13 mins
15 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,209,073 104,178 21.20 1 hrs 8 mins
16 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 2,044,604 100,954 20.25 1 hrs 11 mins
17 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,842,315 97,969 18.81 1 hrs 17 mins
18 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,294,769 86,917 14.90 1 hrs 37 mins
19 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,269,950 84,264 15.07 1 hrs 36 mins
20 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,239,968 85,623 14.48 1 hrs 39 mins
21 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,203,203 85,406 14.09 1 hrs 42 mins
22 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,177,646 84,487 13.94 1 hrs 43 mins
23 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 550,579 65,406 8.42 2 hrs 51 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:45
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make