RESEARCH: CANCER
FOLDING PROJECT #17779 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 108,517
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

Secondary active transporters are proteins that use ion gradients to move molecules across cell membranes. They're found in all living things and help with transporting things like drugs. The project looks at how these transporters work differently, but share the same basic principle of using ions for power.

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 principles governing the structure and function of molecules.

Scientific: Pharmaceutical Research
Biotechnology / Structural Biology

Molecular basis refers to the underlying principles that explain how molecules are structured and how they interact with each other. In this context, it likely pertains to understanding the structural components and interactions of secondary active transporters at a molecular level.


Secondary active transporters

Membrane proteins that use an ion gradient to transport molecules across cell membranes.

Technical: Pharmaceutical Research
Biotechnology / Membrane Transport

Secondary active transporters are specialized proteins embedded within cell membranes. They utilize the energy stored in an electrochemical gradient of ions, typically sodium or protons, to drive the movement of other molecules against their concentration gradient. This process is essential for various cellular functions, including nutrient uptake, waste removal, and signal transduction.


Ion gradient

A difference in ion concentration across a membrane.

Scientific: Pharmaceutical Research
Biotechnology / Cellular Transport

An ion gradient refers to an uneven distribution of ions, such as sodium or potassium, between two regions separated by a membrane. This concentration difference creates a potential energy that can be harnessed for cellular processes like transporting molecules or generating electrical signals.


Drug targets

Molecules or cellular processes that are targeted by drugs to achieve therapeutic effects.

Technical: Pharmaceutical Research
Pharmacology / Disease Treatment

Drug targets represent specific molecules or biological pathways involved in disease pathogenesis. By inhibiting or modulating these targets, drugs aim to alleviate symptoms, halt disease progression, or cure the condition.


Simulations

Computer-based models that mimic real-world processes.

Technical: Pharmaceutical Research
Biotechnology / Computational Biology

Simulations involve using computer algorithms to recreate and analyze complex systems or processes. In this context, simulations are likely employed to model the behavior of secondary active transporters under various conditions.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:30
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 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 7,207,112 183,514 39.27 0 hrs 37 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 6,812,063 177,087 38.47 0 hrs 37 mins
3 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,615,924 177,915 37.19 0 hrs 39 mins
4 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 6,139,718 174,015 35.28 0 hrs 41 mins
5 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,620,920 157,721 29.30 0 hrs 49 mins
6 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,543,865 157,521 28.85 0 hrs 50 mins
7 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,073,749 150,880 27.00 0 hrs 53 mins
8 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,782,337 148,842 25.41 0 hrs 57 mins
9 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,521,548 129,126 19.53 1 hrs 14 mins
10 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,429,654 128,402 18.92 1 hrs 16 mins
11 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,179,000 116,011 18.78 1 hrs 17 mins
12 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,099,465 121,496 17.28 1 hrs 23 mins
13 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 1,736,273 114,546 15.16 1 hrs 35 mins
14 Radeon RX 6900 XT
Navi 21 [Radeon RX 6900 XT]
AMD Navi 21 1,537,950 109,761 14.01 1 hrs 43 mins
15 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,407,579 106,430 13.23 1 hrs 49 mins
16 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,178,978 100,288 11.76 2 hrs 2 mins
17 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,152,684 99,375 11.60 2 hrs 4 mins
18 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 1,120,112 98,685 11.35 2 hrs 7 mins
19 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 779,566 88,054 8.85 2 hrs 43 mins
20 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 533,968 77,203 6.92 3 hrs 28 mins
21 Radeon RX Vega 56/64
Vega 10 XL/XT [Radeon RX Vega 56/64]
AMD Vega 10 XL/XT 408,330 70,653 5.78 4 hrs 9 mins
22 GeForce GTX 1650
TU117 [GeForce GTX 1650]
Nvidia TU117 379,718 66,537 5.71 4 hrs 12 mins
23 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 335,305 66,318 5.06 4 hrs 45 mins
24 GeForce GTX 1050 Ti
GP107 [GeForce GTX 1050 Ti] 2138
Nvidia GP107 306,760 64,404 4.76 5 hrs 2 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:30
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make