RESEARCH: CANCER
FOLDING PROJECT #15306 PROFILE
PROJECT TEAM
Manager(s): Miko MiwaInstitution: UIUC
WORK UNIT INFO
Atoms: 89,592Core: 0x27
Status: Beta
Related Projects
TLDR; 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.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL 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.
RELATED TERMS GLOSSARY AI BETA
Graspetides
A class of ribosomally synthesized and post-translationally modified peptides (RiPPs)
Graspetides are a type of peptide produced by living organisms. They have unique structural features called macrolactam or macrolactone linkages that make them stable and effective against things like bacteria, viruses, and enzymes. Researchers are studying how graspetides are made and how their structure influences their activity.
RiPPs
Ribosomally synthesized and post-translationally modified peptides
RiPPs stand for ribosomally synthesized and post-translationally modified peptides. They are a diverse group of bioactive compounds produced by bacteria, fungi, and plants. RiPPs have various applications in medicine, agriculture, and biotechnology.
ATP-grasp ligase
An enzyme that catalyzes the formation of macrolactam or macrolactone linkages
ATP-grasp ligases are a type of enzyme involved in making special chemical bonds in peptides. They use energy from ATP to create macrocyclic structures, which are rings found in many bioactive molecules.
Macrolactam
A cyclic amide formed by the reaction of an amine with a carboxylic acid
Macrolactam is a type of ring structure found in some peptides. It's formed when an amine group reacts with a carboxylic acid group, creating a stable loop-like shape.
Macrolactone
A cyclic ester formed by the reaction of an alcohol with a carboxylic acid
Macrolactone is another type of ring structure found in some peptides. It's formed when an alcohol group reacts with a carboxylic acid group, creating a stable loop-like shape.
Molecular Dynamics (MD)
A computer simulation technique used to study the motion of atoms and molecules over time
Molecular Dynamics (MD) is a powerful tool used by scientists to simulate how molecules move and interact. By running these simulations on computers, researchers can gain insights into complex biological processes.
Folding Trajectories
The path followed by a molecule as it folds into its final three-dimensional structure
Folding trajectories describe the step-by-step process by which a molecule, like a protein, changes shape to become functional. Scientists use these trajectories to understand how proteins fold and what influences their shape.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:31:51|
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 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 3,951,593 | 235,976 | 16.75 | 1 hrs 26 mins |
| 14 | 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 |
| 15 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,021,307 | 12,192 | 247.81 | 0 hrs 6 mins |
| 16 | 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 |
| 17 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,933,340 | 12,192 | 158.57 | 0 hrs 9 mins |
| 18 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 1,242,126 | 12,192 | 101.88 | 0 hrs 14 mins |
| 19 | RTX A1000 GA107GL [RTX A1000] |
Nvidia | GA107GL | 1,211,837 | 12,192 | 99.40 | 0 hrs 14 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:31:51|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|