RESEARCH: CANCER
FOLDING PROJECT #15321 PROFILE
PROJECT TEAM
Manager(s): Miko MiwaInstitution: UIUC
WORK UNIT INFO
Atoms: 139,750Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
Graspetides are special peptides with strong shapes that make them good at fighting germs and viruses. Scientists are using computer simulations to understand how these peptides fold and create their unique shapes, hoping to learn more about how they work.
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 the interactions between graspetide precursor peptides and their respective ATP-grasp ligases using atomic-level molecular dynamics (MD) simulations.
By comparing the folding trajectories of these systems under differing conditions, we aim to identify and assess patterns in ring closure preference.
RELATED TERMS GLOSSARY AI BETA
Graspetides
A class of ribosomally synthesized and post-translationally modified peptides (RiPPs).
Graspetides are a special type of peptide produced by bacteria. They have a unique ring structure that makes them very stable. This stability allows them to be used for various purposes, such as fighting infections or blocking the activity of enzymes.
RiPPs
Ribosomally Synthesized and Post-translationally Modified Peptides
RiPPs are a category of peptides produced by ribosomes in bacteria. These peptides undergo modifications after they're made, giving them specific functions like fighting infections or regulating biological processes.
ATP-grasp ligase
An enzyme that catalyzes the formation of macrolactam or macrolactone linkages in peptides.
ATP-grasp ligases are enzymes that play a crucial role in creating a specific type of ring structure in some peptides. This ring structure is important for the peptide's function and stability.
Macrolactam
A type of cyclic peptide linkage formed by an amide bond between the carboxyl group of one amino acid and the amine group of another amino acid.
Macrolactam is a term used to describe a specific type of ring structure found in some peptides. It's formed when a part of a peptide chain connects back to itself through a special chemical bond.
Macrolactone
A type of cyclic peptide linkage formed by an ester bond between the carboxyl group of one amino acid and the hydroxyl group of another amino acid.
Macrolactone is similar to macrolactam but uses a different type of chemical bond. Both terms describe ring structures found in some peptides, which are important for their function and stability.
Molecular Dynamics (MD) Simulations
A computational method used to simulate the movement and interactions of atoms and molecules over time.
Molecular Dynamics Simulations are computer programs that allow scientists to study how molecules move and interact. This helps us understand how things work at a very small level.
Folding Trajectories
The path a molecule takes as it changes shape and structure.
Folding trajectories describe how a molecule, like a protein, changes shape over time. This process is important because the shape of a molecule determines its function.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:31:44|
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 | 9,678,027 | 23,243 | 416.38 | 0 hrs 3 mins |
| 2 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,914,488 | 151,476 | 58.85 | 0 hrs 24 mins |
| 3 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 7,565,512 | 247,140 | 30.61 | 0 hrs 47 mins |
| 4 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,639,872 | 23,243 | 285.67 | 0 hrs 5 mins |
| 5 | Instinct MI300X Aqua Vanjaram [Instinct MI300X] |
AMD | Aqua Vanjaram | 6,483,169 | 23,243 | 278.93 | 0 hrs 5 mins |
| 6 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,650,328 | 157,642 | 35.84 | 0 hrs 40 mins |
| 7 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,251,013 | 23,243 | 225.92 | 0 hrs 6 mins |
| 8 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,088,532 | 76,874 | 66.19 | 0 hrs 22 mins |
| 9 | Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)] |
AMD | Navi 48 | 5,062,354 | 30,425 | 166.39 | 0 hrs 9 mins |
| 10 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 4,594,740 | 23,243 | 197.68 | 0 hrs 7 mins |
| 11 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,296,921 | 203,237 | 21.14 | 1 hrs 8 mins |
| 12 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,826,035 | 23,243 | 164.61 | 0 hrs 9 mins |
| 13 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,790,229 | 158,510 | 23.91 | 1 hrs 0 mins |
| 14 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,510,879 | 23,243 | 151.05 | 0 hrs 10 mins |
| 15 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,506,601 | 23,243 | 107.84 | 0 hrs 13 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,491,425 | 23,243 | 107.19 | 0 hrs 13 mins |
| 17 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,237,674 | 23,243 | 96.27 | 0 hrs 15 mins |
| 18 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,155,869 | 162,724 | 13.25 | 1 hrs 49 mins |
| 19 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 1,977,137 | 23,243 | 85.06 | 0 hrs 17 mins |
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|||||||
| 20 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,515,890 | 86,494 | 17.53 | 1 hrs 22 mins |
| 21 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,396,471 | 23,243 | 60.08 | 0 hrs 24 mins |
| 22 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 1,335,841 | 23,243 | 57.47 | 0 hrs 25 mins |
| 23 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 752,750 | 114,301 | 6.59 | 3 hrs 39 mins |
| 24 | GeForce GTX 1650 TU106 [GeForce GTX 1650] |
Nvidia | TU106 | 688,609 | 23,243 | 29.63 | 0 hrs 49 mins |
| 25 | R9 Fury X/NANO Fiji XT [R9 Fury X/NANO] |
AMD | Fiji XT | 495,095 | 23,243 | 21.30 | 1 hrs 8 mins |
| 26 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 353,616 | 88,811 | 3.98 | 6 hrs 2 mins |
| 27 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 232,189 | 23,243 | 9.99 | 2 hrs 24 mins |
| 28 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 222,644 | 76,469 | 2.91 | 8 hrs 15 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:31:44|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|