RESEARCH: CANCER
FOLDING PROJECT #17725 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 85,162
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project looks at how proteins use ion gradients to move molecules across cell membranes. These proteins are found in all living things and are important for treating diseases like cancer, diabetes, and neurological disorders. By studying these proteins, we can learn more about how they work and develop new treatments.

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

OFFICAL PROJECT DESCRIPTION

Projects 17711-17724 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 disease like cancer, diabetes, and neurological disorders.

These simulations 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.

transporters

Proteins that move molecules across cell membranes.

Scientific: Pharmaceutical Research
Biotechnology / Membrane Biology

Transporters are essential proteins found in all living organisms. They facilitate the movement of various substances, such as nutrients, ions, and drugs, across cell membranes. This process is crucial for numerous biological functions, including nutrient uptake, waste removal, and signal transduction. Many diseases are caused by defects in transporter function.


secondary active transporters

Transporters that use an ion gradient to move molecules across cell membranes.

Scientific: Pharmaceutical Research
Biotechnology / Membrane Biology

Secondary active transporters are a class of membrane proteins that utilize the energy stored in an electrochemical gradient of ions to drive the transport of other molecules. They play a vital role in maintaining cellular homeostasis, nutrient uptake, and drug efflux. These transporters are often targeted by pharmaceuticals to treat various diseases.


membrane

A thin layer that surrounds cells and organelles.

Scientific: Pharmaceutical Research
Biotechnology / Cell Biology

The membrane is a fundamental component of all living cells. It serves as a barrier that separates the internal environment of the cell from its surroundings. The membrane is composed of lipids and proteins, which allow for selective transport of molecules in and out of the cell. It also plays a crucial role in cell signaling and communication.


proteins

Large biomolecules that perform various functions in cells.

Scientific: Pharmaceutical Research
Biotechnology / Molecular Biology

Proteins are essential macromolecules found in all living organisms. They play a diverse range of roles, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Proteins are made up of amino acids, which are linked together in specific sequences to form polypeptide chains.


ions

Atoms or molecules that have an electrical charge.

Scientific: Pharmaceutical Research
Biotechnology / Cellular Physiology

Ions are atoms or molecules that have gained or lost electrons, resulting in a net electric charge. They play a crucial role in various cellular processes, such as nerve impulse transmission, muscle contraction, and enzyme activity. The movement of ions across cell membranes is essential for maintaining electrochemical gradients and regulating cellular function.


simulations

Computer models used to mimic real-world processes.

Technical: Drug Discovery
Biotechnology / Computational Biology

Simulations are computer programs that create virtual representations of real-world systems. They are widely used in various fields, including biotechnology, to study complex biological processes, design new drugs, and predict the behavior of molecules.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:36:36
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 7,285,846 127,765 57.03 0 hrs 25 mins
2 GeForce RTX 3080 10GB / 20GB
GA102 [GeForce RTX 3080 10GB / 20GB]
Nvidia GA102 5,701,205 118,732 48.02 0 hrs 30 mins
3 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 5,675,205 118,233 48.00 0 hrs 30 mins
4 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 5,002,392 113,653 44.01 0 hrs 33 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,145,184 106,772 38.82 0 hrs 37 mins
6 Quadro RTX 6000/8000
TU102GL [Quadro RTX 6000/8000]
Nvidia TU102GL 4,047,015 105,880 38.22 0 hrs 38 mins
7 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,208,637 98,508 32.57 0 hrs 44 mins
8 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,197,627 98,095 32.60 0 hrs 44 mins
9 GeForce RTX 2080
TU104 [GeForce RTX 2080]
Nvidia TU104 3,148,574 97,702 32.23 0 hrs 45 mins
10 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 3,022,347 96,254 31.40 0 hrs 46 mins
11 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 2,926,358 95,239 30.73 0 hrs 47 mins
12 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 2,568,275 90,354 28.42 0 hrs 51 mins
13 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A] M 7465
Nvidia TU106 2,396,317 89,540 26.76 0 hrs 54 mins
14 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,282,870 88,192 25.89 0 hrs 56 mins
15 GeForce RTX 2080 SUPER Mobile / Max-Q
TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q]
Nvidia TU104M 2,240,550 88,170 25.41 0 hrs 57 mins
16 GeForce RTX 2070
TU106 [GeForce RTX 2070] M 6497
Nvidia TU106 2,209,945 86,965 25.41 0 hrs 57 mins
17 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,126,875 85,912 24.76 0 hrs 58 mins
18 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,998,052 83,832 23.83 1 hrs 0 mins
19 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 1,725,928 78,683 21.94 1 hrs 6 mins
20 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,670,178 79,392 21.04 1 hrs 8 mins
21 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,583,011 69,069 22.92 1 hrs 3 mins
22 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,356,242 74,257 18.26 1 hrs 19 mins
23 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,294,322 72,896 17.76 1 hrs 21 mins
24 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,196,145 71,450 16.74 1 hrs 26 mins
25 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,140,503 70,465 16.19 1 hrs 29 mins
26 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,140,363 70,116 16.26 1 hrs 29 mins
27 GeForce GTX 1660 Ti
TU116 [GeForce GTX 1660 Ti]
Nvidia TU116 910,011 64,996 14.00 1 hrs 43 mins
28 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 807,865 62,325 12.96 1 hrs 51 mins
29 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 567,916 55,376 10.26 2 hrs 20 mins
30 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 557,021 55,156 10.10 2 hrs 23 mins
31 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 499,272 53,388 9.35 2 hrs 34 mins
32 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 493,914 49,833 9.91 2 hrs 25 mins
33 P106-100
GP106 [P106-100]
Nvidia GP106 473,815 52,318 9.06 2 hrs 39 mins
34 GeForce GTX 1650
TU116 [GeForce GTX 1650] 2984
Nvidia TU116 422,241 50,220 8.41 2 hrs 51 mins
35 Radeon R9 200/300X Series
Hawaii [Radeon R9 200/300X Series]
AMD Hawaii 351,797 47,409 7.42 3 hrs 14 mins
36 GeForce GTX 960
GM206 [GeForce GTX 960] 2308
Nvidia GM206 292,061 44,690 6.54 3 hrs 40 mins
37 P106-090
GP106 [P106-090]
Nvidia GP106 248,562 42,321 5.87 4 hrs 5 mins
38 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 239,431 38,793 6.17 3 hrs 53 mins
39 GeForce GTX 1050 Ti Mobile
GP107M [GeForce GTX 1050 Ti Mobile]
Nvidia GP107M 216,000 40,417 5.34 4 hrs 29 mins
40 Radeon R9 280/HD 7900/8950
Tahiti PRO [Radeon R9 280/HD 7900/8950]
AMD Tahiti PRO 213,413 40,226 5.31 4 hrs 31 mins
41 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 180,720 37,984 4.76 5 hrs 3 mins
42 Radeon R9 200/300 Series
Hawaii [Radeon R9 200/300 Series]
AMD Hawaii 161,706 25,030 6.46 3 hrs 43 mins
43 Radeon RX Vega M XL
[Radeon RX Vega M XL]
AMD Vega 123,230 33,456 3.68 6 hrs 31 mins
44 Radeon HD 7800
Pitcairn [Radeon HD 7800]
AMD Pitcairn 119,889 33,107 3.62 6 hrs 38 mins
45 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 115,459 32,508 3.55 6 hrs 45 mins
46 Quadro P620
GP107GL [Quadro P620]
Nvidia GP107GL 110,937 32,342 3.43 6 hrs 60 mins
47 Quadro K2200
GM107GL [Quadro K2200]
Nvidia GM107GL 109,084 32,071 3.40 7 hrs 3 mins
48 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 106,239 31,046 3.42 7 hrs 1 mins
49 Radeon RX 460
Baffin XT [Radeon RX 460]
AMD Baffin XT 100,750 31,135 3.24 7 hrs 25 mins
50 GeForce GT 1030
GP108 [GeForce GT 1030] 1127
Nvidia GP108 85,393 29,581 2.89 8 hrs 19 mins
51 Quadro K1200
GM107GL [Quadro K1200]
Nvidia GM107GL 75,357 28,432 2.65 9 hrs 3 mins
52 GeForce GTX 960M
GM107 [GeForce GTX 960M] 1439
Nvidia GM107 65,309 26,974 2.42 9 hrs 55 mins
53 Radeon R7 250/HD 7700
R575A [Radeon R7 250/HD 7700]
AMD R575A 21,623 16,170 1.34 17 hrs 57 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:36:36
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make