RESEARCH: CANCER
FOLDING PROJECT #17790 PROFILE

PROJECT TEAM

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

WORK UNIT INFO

Atoms: 58,675
Core: OPENMM_22
Status: Public

TLDR; PROJECT SUMMARY AI BETA

This project looks at how proteins use ion power to move molecules across cell membranes. These proteins are important for many processes in the body and are even targets for some medicines! By studying them, we can learn how they work across different types of organisms.

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 mechanisms underlying molecular structure and function.

Scientific: Medicine
Biochemistry / Transport Proteins

Molecular basis refers to the intricate ways molecules interact and behave. In biochemistry, it's crucial for understanding how proteins like transporters work.


Secondary active transporters

Proteins that use an ion gradient to drive the transport of other molecules across cell membranes.

Technical: Pharmacology
Biochemistry / Membrane Transport

Secondary active transporters are crucial for moving molecules across cell walls. They utilize the energy from an existing ion gradient to transport various substances, like nutrients or drugs.


Ion gradient

The difference in concentration of ions across a cell membrane.

Scientific: Biotechnology
Biochemistry / Membrane Transport

An ion gradient is like an electrical potential created by having different amounts of charged particles (ions) on either side of a membrane. This difference drives many biological processes, including transport.


Drug targets

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

Technical: Medicine
Pharmacology / Disease Treatment

Drug targets are specific molecules or cellular processes that contribute to a disease. By targeting these, drugs can effectively treat or manage the condition.


Simulations

Computer models used to mimic biological processes or systems.

Technical: Biotechnology
Bioinformatics / Drug Discovery

Simulations are powerful tools that allow scientists to study complex biological systems without needing to conduct experiments in the real world. They can help predict how molecules interact and how drugs might work.

PROJECT FOLDING PPD AVERAGES BY GPU

Data as of Sunday, 26 April 2026 00:35:13
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 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,629,275 112,930 49.85 0 hrs 29 mins
2 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 5,296,236 113,530 46.65 0 hrs 31 mins
3 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 4,059,369 102,823 39.48 0 hrs 36 mins
4 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 4,045,941 103,021 39.27 0 hrs 37 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,018,472 102,702 39.13 0 hrs 37 mins
6 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,585,351 98,519 36.39 0 hrs 40 mins
7 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,563,310 98,981 36.00 0 hrs 40 mins
8 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 3,548,936 98,582 36.00 0 hrs 40 mins
9 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 3,529,102 98,031 36.00 0 hrs 40 mins
10 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 2,530,884 87,878 28.80 0 hrs 50 mins
11 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,160,218 83,735 25.80 0 hrs 56 mins
12 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,941,723 80,905 24.00 0 hrs 60 mins
13 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,932,577 80,524 24.00 0 hrs 60 mins
14 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,469,630 73,141 20.09 1 hrs 12 mins
15 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,408,478 72,530 19.42 1 hrs 14 mins
16 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,208,563 66,667 18.13 1 hrs 19 mins
17 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,180,085 68,739 17.17 1 hrs 24 mins
18 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 631,946 55,414 11.40 2 hrs 6 mins
19 P106-090
GP106 [P106-090]
Nvidia GP106 317,520 44,152 7.19 3 hrs 20 mins
20 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 214,469 37,885 5.66 4 hrs 14 mins
21 GeForce GTX 760
GK104 [GeForce GTX 760] 2258
Nvidia GK104 58,289 25,207 2.31 10 hrs 23 mins
22 GeForce GTX 660
GK106 [GeForce GTX 660]
Nvidia GK106 52,511 24,391 2.15 11 hrs 9 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

Data as of Sunday, 26 April 2026 00:35:13
Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make