Research section Research projects and references

Research: CANCER Folding Project #17794

Project #17794 overview

Project Summary AI Beta

This project explores how proteins use ion gradients to move molecules across cell membranes. These 'secondary active transporters' are found everywhere and help with many important processes, including drug delivery. By studying them, we can learn more about how cells work and develop new treatments for diseases like cancer and diabetes.
Automated summary; simplified and may not be fully accurate.

Project team

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

Work unit

Atoms
65,610
Core
OPENMM_22
Status
Public
Source material

Official 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.

Performance data

Hardware Performance for Project 17794

Compare community-sampled Folding@Home output for the GPUs and CPUs processing this project.

Data as of Sunday, 02 August 2026 21:55:44

GPU PPD Averages

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 6,613,301 164,107 40.30 0 hrs 36 mins
2 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 5,369,002 155,353 34.56 0 hrs 42 mins
3 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 4,716,658 147,396 32.00 0 hrs 45 mins
4 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,370,482 142,159 30.74 0 hrs 47 mins
5 RTX A5000
GA102GL [RTX A5000]
Nvidia GA102GL 3,955,507 139,132 28.43 0 hrs 51 mins
6 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,373,393 132,749 25.41 0 hrs 57 mins
7 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 2,606,424 145,167 17.95 1 hrs 20 mins
8 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 2,104,258 113,234 18.58 1 hrs 17 mins
9 GeForce RTX 2060 Mobile / Max-Q
TU106M [GeForce RTX 2060 Mobile / Max-Q]
Nvidia TU106M 1,250,461 94,792 13.19 1 hrs 49 mins
10 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 873,106 79,341 11.00 2 hrs 11 mins
11 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 510,784 70,854 7.21 3 hrs 20 mins
12 P106-090
GP106 [P106-090]
Nvidia GP106 314,651 59,967 5.25 4 hrs 34 mins
13 GeForce GTX 750 Ti
GM107 [GeForce GTX 750 Ti] 1389
Nvidia GM107 133,839 45,068 2.97 8 hrs 5 mins
14 GeForce GTX 770
GK104 [GeForce GTX 770] 3213
Nvidia GK104 112,307 42,800 2.62 9 hrs 9 mins