RESEARCH: CANCER
FOLDING PROJECT #17766 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 110,202
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

The project relates to understanding how proteins use ion power to move molecules across cell membranes. These proteins are found everywhere in life and are even drug targets for diseases like cancer and diabetes. By studying them, we can learn how different types of 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 ions to transport molecules across cell membranes.

Scientific: Pharmaceutical Research
Biotechnology / Membrane Transport

Secondary active transporters are crucial proteins found in all living organisms. They utilize the energy stored in an ion gradient to move various molecules across cell membranes. These transporters play vital roles in numerous biological processes and are often targeted by drugs for treating diseases like cancer, diabetes, and neurological disorders.


Membrane transporters

Proteins embedded in cell membranes that facilitate the movement of molecules across the membrane.

Scientific: Pharmaceutical Research
Biotechnology / Cell Biology

Membrane transporters are proteins embedded within cell membranes. They act as gatekeepers, selectively allowing specific molecules to enter or exit the cell. These transporters play essential roles in various cellular processes, including nutrient uptake, waste removal, and signal transduction.


Ion gradient

A difference in concentration of ions across a membrane.

Scientific: Pharmaceutical Research
Biochemistry / Cellular Transport

An ion gradient refers to an unequal distribution of charged atoms (ions) across a cell membrane. This difference in concentration creates an electrochemical potential that drives various cellular processes, including the function of secondary active transporters.


Proteins

Large, complex molecules essential for the structure and function of all living organisms.

Scientific: Biotechnology
Biochemistry / Molecular Biology

Proteins are the workhorses of the cell. They perform a vast array of functions, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes.


Simulations

Computer models used to mimic and study real-world processes.

Technical: Pharmaceutical Research
Biotechnology / Computational Biology

Simulations are powerful tools used in biotechnology to model complex biological systems. By running computer simulations, researchers can investigate various scenarios, test hypotheses, and gain insights into biological mechanisms.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:50
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 7,692,421 193,149 39.83 0 hrs 36 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 7,539,925 191,989 39.27 0 hrs 37 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,053,068 186,443 37.83 0 hrs 38 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,476,232 161,410 27.73 0 hrs 52 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,317,470 159,296 27.10 0 hrs 53 mins
6 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,077,228 155,727 26.18 0 hrs 55 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,757,421 152,210 24.69 0 hrs 58 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,187,506 143,880 22.15 1 hrs 5 mins
9 Quadro RTX 6000/8000
TU102GL [Quadro RTX 6000/8000]
Nvidia TU102GL 2,879,073 139,955 20.57 1 hrs 10 mins
10 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 2,785,832 136,220 20.45 1 hrs 10 mins
11 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,571,000 134,633 19.10 1 hrs 15 mins
12 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,167,787 118,910 18.23 1 hrs 19 mins
13 GeForce RTX 3060
GA106 [GeForce RTX 3060]
Nvidia GA106 1,738,801 118,033 14.73 1 hrs 38 mins
14 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,711,318 112,539 15.21 1 hrs 35 mins
15 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,547,181 112,948 13.70 1 hrs 45 mins
16 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 1,524,767 113,205 13.47 1 hrs 47 mins
17 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,503,450 112,240 13.39 1 hrs 48 mins
18 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,458,641 111,840 13.04 1 hrs 50 mins
19 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,372,516 108,809 12.61 1 hrs 54 mins
20 GeForce RTX 2060 Mobile
TU106M [GeForce RTX 2060 Mobile]
Nvidia TU106M 1,284,970 107,081 12.00 2 hrs 0 mins
21 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,189,412 102,355 11.62 2 hrs 4 mins
22 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,179,661 104,104 11.33 2 hrs 7 mins
23 Radeon RX 5600 OEM/5600 XT/5700/5700 XT
Navi 10 [Radeon RX 5600 OEM/5600 XT/5700/5700 XT]
AMD Navi 10 1,177,553 103,235 11.41 2 hrs 6 mins
24 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,111,873 101,015 11.01 2 hrs 11 mins
25 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 919,975 95,830 9.60 2 hrs 30 mins
26 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 606,795 82,857 7.32 3 hrs 17 mins
27 Radeon VII
Vega 20 [Radeon VII] 13,284
AMD Vega 20 542,836 80,313 6.76 3 hrs 33 mins
28 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 463,725 75,575 6.14 3 hrs 55 mins
29 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 429,286 74,191 5.79 4 hrs 9 mins
30 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 420,511 73,458 5.72 4 hrs 12 mins
31 Radeon RX 6600/6600 XT/6600M
Navi 23 [Radeon RX 6600/6600 XT/6600M]
AMD Navi 23 341,910 68,988 4.96 4 hrs 51 mins
32 P106-090
GP106 [P106-090]
Nvidia GP106 328,874 67,772 4.85 4 hrs 57 mins
33 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 308,219 67,074 4.60 5 hrs 13 mins
34 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 226,310 59,763 3.79 6 hrs 20 mins
35 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 107,034 46,788 2.29 10 hrs 29 mins
36 Radeon Vega Series / Radeon Vega Mobile Series
Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series]
AMD Raven Ridge 58,494 38,308 1.53 15 hrs 43 mins
37 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 53,004 37,044 1.43 16 hrs 46 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:50
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make