Research section Research projects and references

Research: CANCER Folding Project #13005

Project #13005 overview

Project Summary AI Beta

This project is making super-bright fluorescent proteins that can grab rare earth elements. Rare earths are important for our bodies, and finding them early could help diagnose diseases like cancer. These new proteins could also be used to deliver medicine directly to sick cells.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Lin Zhu
Institution
the University of Illinois Urbana-Champaign

Work unit

Atoms
60,000
Core
0x23
Status
Public
Source material

Official Project Description

Super Charged Fluorescent protein design Rare earth elements play vital roles in various biological processes, and their detection using fluorescent proteins could advance biomedical research and diagnostics.

By developing more sensitive and specific sensors for rare earth elements, this project could contribute to early detection of diseases, such as cancer and neurodegenerative disorders, where abnormal levels of certain elements are often observed.

Moreover, the enhanced understanding of binding mechanisms between fluorescent proteins and rare earth elements could lead to the development of novel therapeutic agents or drug delivery systems tailored to target specific cellular pathways or tissues, thereby potentially improving treatment outcomes and patient well-being.This project aims to investigate the binding mechanism of supercharged fluorescent proteins for capturing rare earth elements and then design a new variant of fluorescent protein that exhibits stronger binding affinity for specific rare earth elements than existing fluorescent proteins.
In this project, there are 100 single-point mutant systems of yellow fluorescent protein with a negative charge of 31.

Performance data

Hardware Performance for Project 13005

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

Data as of Sunday, 02 August 2026 22:07:59

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 Radeon RX 7900XT/XTX/GRE
Navi 31 [Radeon RX 7900XT/XTX/GRE]
AMD Navi 31 3,999,004 408,454 9.79 2 hrs 27 mins
2 GeForce RTX 2060
TU106 [Geforce RTX 2060]
Nvidia TU106 2,224,297 230,778 9.64 2 hrs 29 mins
3 Radeon RX 6800/6800XT/6900XT
Navi 21 [Radeon RX 6800/6800XT/6900XT]
AMD Navi 21 1,947,692 331,180 5.88 4 hrs 5 mins
4 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,442,364 91,735 15.72 1 hrs 32 mins
5 GeForce GTX 980 Ti
GM200 [GeForce GTX 980 Ti] 5632
Nvidia GM200 1,171,000 280,438 4.18 5 hrs 45 mins
6 Radeon RX 6700/6700XT/6800M
Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M]
AMD Navi 22 XT-XL 818,312 169,030 4.84 4 hrs 57 mins
7 RX 5600 OEM/5600XT/5700/5700XT
Navi 10 [RX 5600 OEM/5600XT/5700/5700XT]
AMD Navi 10 423,527 131,272 3.23 7 hrs 26 mins