RESEARCH: CANCER
FOLDING PROJECT #17786 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 143,212
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Secondary active transporters are proteins that use ion power to move molecules across cell membranes. They're found everywhere and are important drug targets for diseases like cancer and diabetes. The project looks at how these transporters work across different types of proteins.

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

OFFICAL PROJECT DESCRIPTION

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.

Molecular basis

The fundamental structure and function of molecules.

Scientific: Academia
Biochemistry / Protein Structure

This refers to the underlying arrangement of atoms within molecules and how those arrangements contribute to their specific properties and roles in biological processes. For example, understanding the molecular basis of an enzyme helps us comprehend how it catalyzes reactions.


Secondary active transporters

Proteins that use ion gradients to transport molecules across cell membranes.

Technical: Academia, Biotechnology
Biochemistry / Membrane Transport

These specialized proteins are crucial for moving various substances into and out of cells. They leverage the energy stored in an existing concentration gradient of ions (like sodium or potassium) to drive the movement of other molecules, like nutrients or drugs, against their own concentration gradients.


Ion gradient

A difference in ion concentration across a membrane.

Scientific: Academia, Biotechnology
Biochemistry / Cellular Transport

Cells maintain unequal concentrations of charged particles (ions) inside and outside their membranes. This difference in concentration creates an electrochemical gradient, which serves as a source of energy for various cellular processes, including the function of secondary active transporters.


Drug targets

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

Technical: Academia, Biotechnology, Pharmaceutical
Pharmacology / Disease Treatment

Drug targets are the specific molecules or pathways within the body that contribute to the development or progression of diseases. By understanding these targets, scientists can design drugs that interact with them and modify their activity, ultimately leading to therapeutic effects.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:19
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 7,936,731 171,982 46.15 0 hrs 31 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 7,103,281 158,329 44.86 0 hrs 32 mins
3 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 5,510,801 153,078 36.00 0 hrs 40 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 5,229,953 151,330 34.56 0 hrs 42 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,888,055 147,868 33.06 0 hrs 44 mins
6 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,837,513 147,392 32.82 0 hrs 44 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,612,189 144,131 32.00 0 hrs 45 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,218,632 128,487 25.05 0 hrs 57 mins
9 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,753,343 122,585 22.46 1 hrs 4 mins
10 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,232,242 114,725 19.46 1 hrs 14 mins
11 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,925,841 109,220 17.63 1 hrs 22 mins
12 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,713,412 105,105 16.30 1 hrs 28 mins
13 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,272,109 95,205 13.36 1 hrs 48 mins
14 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,259,967 94,774 13.29 1 hrs 48 mins
15 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,254,219 94,714 13.24 1 hrs 49 mins
16 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 1,188,247 84,399 14.08 1 hrs 42 mins
17 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,053,592 89,019 11.84 2 hrs 2 mins
18 GeForce RTX 2080 Mobile
TU104M [GeForce RTX 2080 Mobile]
Nvidia TU104M 1,002,890 88,879 11.28 2 hrs 8 mins
19 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 845,050 83,450 10.13 2 hrs 22 mins
20 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 667,695 76,701 8.71 2 hrs 45 mins
21 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 655,280 76,295 8.59 2 hrs 48 mins
22 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 456,414 62,696 7.28 3 hrs 18 mins
23 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 358,334 62,104 5.77 4 hrs 10 mins
24 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 223,151 53,463 4.17 5 hrs 45 mins
25 Radeon RX 6400 / 6500 XT
Navi 24 [Radeon RX 6400 / 6500 XT]
AMD Navi 24 150,262 46,562 3.23 7 hrs 26 mins
26 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 116,285 42,509 2.74 8 hrs 46 mins
27 GeForce GTX 750
GM107 [GeForce GTX 750] 1111
Nvidia GM107 109,748 41,923 2.62 9 hrs 10 mins
28 GeForce GTX 680
GK104 [GeForce GTX 680] 3250
Nvidia GK104 101,001 40,715 2.48 9 hrs 40 mins
29 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 56,634 33,891 1.67 14 hrs 22 mins
30 GeForce GT 730
GK208B [GeForce GT 730] 692.7
Nvidia GK208B 13,576 21,426 0.63 37 hrs 53 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:19
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make