RESEARCH: CANCER
FOLDING PROJECT #17765 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 90,844
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project studies how proteins use ion power to move molecules across cell membranes. These proteins are super important because they're involved in many diseases and some even make good drug targets. By studying them, we can learn how different types of these proteins work together.

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 ion gradients to move molecules across cell membranes.

Scientific: Pharmaceutical
Biotechnology / Membrane transport

Secondary active transporters are crucial proteins found in all living organisms. They work by using the energy from an existing ion gradient to move other molecules across cell membranes. This process is essential for various biological functions, including nutrient uptake, waste removal, and signal transduction. Many secondary active transporters are targets for drugs used to treat diseases such as cancer, diabetes, and neurological disorders.


Ion gradient

A difference in ion concentration across a membrane.

Scientific: Pharmaceutical
Biotechnology / Membrane transport

An ion gradient is the unequal distribution of ions on either side of a cell membrane. This difference in concentration creates an electrical potential, which can be used by cells to power various processes, such as nerve impulse transmission and muscle contraction. Secondary active transporters utilize these ion gradients to drive the transport of other molecules across the membrane.


Drug targets

Molecules or proteins that are targeted by drugs to treat diseases.

Technical: Biotechnology
Pharmaceutical / Drug development

Drug targets are specific molecules or proteins involved in disease pathways. By targeting these molecules, drugs can interfere with the disease process and alleviate symptoms. Secondary active transporters are often drug targets because they play crucial roles in various physiological processes, including nutrient uptake and signal transduction.


Simulations

Computer-based models that mimic real-world processes.

Technical: Pharmaceutical
Biotechnology / Computational biology

Simulations are powerful tools used in various scientific fields to study complex systems. In biotechnology, simulations can be used to model biological processes, such as protein folding and drug interactions. These simulations can provide valuable insights into the mechanisms underlying disease and guide the development of new treatments.


Proteins

Large biomolecules essential for various cellular functions.

Scientific: Pharmaceutical
Biotechnology / Molecular biology

Proteins are complex molecules composed of amino acids. They play a wide range of roles in living organisms, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Understanding the structure and function of proteins is crucial for advancements in biotechnology and medicine.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:51
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 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,202,229 145,981 49.34 0 hrs 29 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,133,682 142,298 50.13 0 hrs 29 mins
3 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 6,109,510 138,057 44.25 0 hrs 33 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,615,517 128,208 36.00 0 hrs 40 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,507,711 126,689 35.58 0 hrs 40 mins
6 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,244,103 124,383 34.12 0 hrs 42 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,182,406 123,345 33.91 0 hrs 42 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,040,166 122,638 32.94 0 hrs 44 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,523,146 118,254 29.79 0 hrs 48 mins
10 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,495,539 104,917 23.79 1 hrs 1 mins
11 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,481,524 103,397 24.00 1 hrs 0 mins
12 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,368,894 119,246 19.87 1 hrs 12 mins
13 GeForce RTX 2060 12GB
TU106 [GeForce RTX 2060 12GB]
Nvidia TU106 1,922,538 95,682 20.09 1 hrs 12 mins
14 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,791,152 93,289 19.20 1 hrs 15 mins
15 GeForce RTX 2070
TU106 [GeForce RTX 2070]
Nvidia TU106 1,782,892 93,877 18.99 1 hrs 16 mins
16 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 1,628,155 89,973 18.10 1 hrs 20 mins
17 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,537,602 88,965 17.28 1 hrs 23 mins
18 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,494,198 88,199 16.94 1 hrs 25 mins
19 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,201,102 82,006 14.65 1 hrs 38 mins
20 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,193,572 81,405 14.66 1 hrs 38 mins
21 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,067,178 78,695 13.56 1 hrs 46 mins
22 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,020,564 77,301 13.20 1 hrs 49 mins
23 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 964,176 75,570 12.76 1 hrs 53 mins
24 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 809,686 71,908 11.26 2 hrs 8 mins
25 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 579,070 63,290 9.15 2 hrs 37 mins
26 Radeon VII
Vega 20 [Radeon VII] 13,284
AMD Vega 20 566,226 64,202 8.82 2 hrs 43 mins
27 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 539,534 62,694 8.61 2 hrs 47 mins
28 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 378,746 55,704 6.80 3 hrs 32 mins
29 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 312,855 52,344 5.98 4 hrs 1 mins
30 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 304,886 52,137 5.85 4 hrs 6 mins
31 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 219,659 45,620 4.81 4 hrs 59 mins
32 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 200,626 45,280 4.43 5 hrs 25 mins
33 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 59,528 29,970 1.99 12 hrs 5 mins
34 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 53,660 29,056 1.85 12 hrs 60 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:51
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make