RESEARCH: CANCER
FOLDING PROJECT #17777 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 116,699
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 help treat diseases like cancer and diabetes. The project uses simulations to understand 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

Fundamental structure and function of molecules.

Scientific: Biotechnology
Biochemistry / Transport Proteins

This refers to the underlying structure and how it works at a molecular level. It's about understanding the building blocks of life and how they interact.


Secondary active transporters

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

Technical: Pharmacology
Biochemistry / Membrane Transport

These are special proteins embedded in cell walls. They help move different substances into and out of cells by using the energy from moving ions (charged particles). This is important for many biological processes.


Ion gradient

Difference in concentration of ions across a membrane.

Scientific: Medicine
Biochemistry / Cellular Transport

Think of it like a battery. Ions are charged particles, and their unequal distribution across a cell membrane creates an electrical difference that can be used to power the movement of other molecules.


Drug targets

Molecules or biological pathways that can be modified by drugs to treat diseases.

Technical: Biotechnology
Pharmacology / Disease Treatment

These are specific molecules or processes within our bodies that researchers try to influence with drugs. By targeting these 'drug targets', scientists aim to develop new therapies for various illnesses.


Cancer

Uncontrolled cell growth and division.

Medical: Healthcare
Pathology / Oncology

Cancer is a group of diseases where cells in the body grow uncontrollably. This can lead to tumors forming and invading healthy tissues.


Diabetes

Chronic metabolic disorder characterized by high blood sugar.

Medical: Healthcare
Endocrinology / Metabolic Disorders

Diabetes occurs when the body can't properly regulate blood sugar levels. This is often due to problems with insulin, a hormone that helps cells absorb glucose.


Neurological disorders

Diseases affecting the nervous system.

Medical: Healthcare
Neurology / Brain and Nervous System Diseases

These disorders impact the brain, spinal cord, and nerves. Examples include Alzheimer's disease, Parkinson's disease, and epilepsy.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:33
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 7,505,210 208,040 36.08 0 hrs 40 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 6,933,639 204,363 33.93 0 hrs 42 mins
3 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 6,073,688 192,601 31.54 0 hrs 46 mins
4 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,484,908 175,614 25.54 0 hrs 56 mins
5 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,476,277 174,856 25.60 0 hrs 56 mins
6 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,448,641 175,062 25.41 0 hrs 57 mins
7 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 4,376,018 174,136 25.13 0 hrs 57 mins
8 GeForce RTX 2070 SUPER
TU104 [GeForce RTX 2070 SUPER] 8218
Nvidia TU104 3,248,914 157,933 20.57 1 hrs 10 mins
9 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 2,492,125 144,220 17.28 1 hrs 23 mins
10 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,492,044 144,215 17.28 1 hrs 23 mins
11 Radeon RX 6800/6800 XT / 6900 XT
Navi 21 [Radeon RX 6800/6800 XT / 6900 XT]
AMD Navi 21 1,637,854 125,337 13.07 1 hrs 50 mins
12 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,211,971 113,900 10.64 2 hrs 15 mins
13 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 751,331 96,693 7.77 3 hrs 5 mins
14 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 659,674 92,604 7.12 3 hrs 22 mins
15 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 644,820 92,248 6.99 3 hrs 26 mins
16 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 458,499 82,428 5.56 4 hrs 19 mins
17 Radeon RX 6700/6700 XT / 6800M
Navi 22 [Radeon RX 6700/6700 XT / 6800M]
AMD Navi 22 450,908 82,254 5.48 4 hrs 23 mins
18 Radeon RX 470/480/570/580/590
Ellesmere XT [Radeon RX 470/480/570/580/590]
AMD Ellesmere XT 341,069 60,332 5.65 4 hrs 15 mins
19 GeForce GTX 980M
GM204 [GeForce GTX 980M] 3189
Nvidia GM204 332,760 74,170 4.49 5 hrs 21 mins
20 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 116,700 52,342 2.23 10 hrs 46 mins
21 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 60,396 41,798 1.44 16 hrs 37 mins
22 Radeon Vega Series / Radeon Vega Mobile Series
Raven Ridge [Radeon Vega Series / Radeon Vega Mobile Series]
AMD Raven Ridge 53,087 40,121 1.32 18 hrs 8 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:33
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make