Research section Research projects and references

Research: CANCER Folding Project #15306

Project #15306 overview

Project Summary AI Beta

This project relates to graspetides, which are small proteins with special rings that give them different abilities like fighting infections. Scientists will use computer simulations to figure out how these rings form and if different types of graspetides have unique ways of building their rings.
Automated summary; simplified and may not be fully accurate.

Project team

Manager(s)
Miko Miwa
Institution
UIUC

Work unit

Atoms
89,592
Core
0x27
Status
Beta
Source material

Official Project Description

Graspetides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by the ATP-grasp ligase–catalyzed macrolactam or macrolactone linkages in their structure.

These macrocycles impart structural stability and diverse bioactivities, including antimicrobial, antiviral, and enzyme inhibitory effects.

Graspetides are classified into distinct groups based on sequence motifs, cyclization patterns, and biosynthetic machinery.

While each group exhibits characteristic ring topologies, the molecular basis by which core peptide sequence and folding pathways dictate the order of ring formation remains poorly understood. In this study, we will investigate model species from multiple graspetide groups using atomic-level molecular dynamics (MD) simulations.

By comparing folding trajectories across these representative systems, we aim to identify conserved and group-specific determinants of ring pattern formation, and to assess whether distinct biosynthetic groups exhibit preferences for particular ring closure orders.

Performance data

Hardware Performance for Project 15306

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

Data as of Friday, 14 August 2026 15:38:37

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 5090
GB202 [GeForce RTX 5090]
Nvidia GB202 31,643,777 12,192 2595.45 0 hrs 1 mins
2 GeForce RTX 4090
AD102 [GeForce RTX 4090]
Nvidia AD102 22,877,163 226,593 100.96 0 hrs 14 mins
3 GeForce RTX 4080 SUPER
AD103 [GeForce RTX 4080 SUPER]
Nvidia AD103 16,710,754 12,192 1370.63 0 hrs 1 mins
4 GeForce RTX 4080
AD103 [GeForce RTX 4080]
Nvidia AD103 16,612,140 13,861 1198.48 0 hrs 1 mins
5 GeForce RTX 5070 Ti
GB203 [GeForce RTX 5070 Ti]
Nvidia GB203 13,149,366 70,521 186.46 0 hrs 8 mins
6 GeForce RTX 4070 SUPER
AD104 [GeForce RTX 4070 SUPER]
Nvidia AD104 10,363,303 12,192 850.01 0 hrs 2 mins
7 GeForce RTX 4070 Ti
AD104 [GeForce RTX 4070 Ti]
Nvidia AD104 10,016,041 172,905 57.93 0 hrs 25 mins
8 GeForce RTX 5070
GB205 [GeForce RTX 5070]
Nvidia GB205 9,958,431 12,192 816.80 0 hrs 2 mins
9 GeForce RTX 5080
GB203 [GeForce RTX 5080]
Nvidia GB203 7,869,757 12,192 645.49 0 hrs 2 mins
10 GeForce RTX 4060 Ti
AD106 [GeForce RTX 4060 Ti]
Nvidia AD106 5,972,679 148,788 40.14 0 hrs 36 mins
11 TITAN V
GV100 [TITAN V] M 12288
Nvidia GV100 5,810,716 12,192 476.60 0 hrs 3 mins
12 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 4,252,538 131,574 32.32 0 hrs 45 mins
13 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 3,441,884 12,192 282.31 0 hrs 5 mins
14 GeForce RTX 4060
AD107 [GeForce RTX 4060]
Nvidia AD107 3,021,307 12,192 247.81 0 hrs 6 mins
15 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 2,144,766 107,959 19.87 1 hrs 12 mins
16 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 1,933,340 12,192 158.57 0 hrs 9 mins
17 GeForce GTX 1070 Mobile
GP104BM [GeForce GTX 1070 Mobile] 6463
Nvidia GP104BM 1,242,126 12,192 101.88 0 hrs 14 mins
18 RTX A1000
GA107GL [RTX A1000]
Nvidia GA107GL 1,211,837 12,192 99.40 0 hrs 14 mins