RESEARCH: CANCER
FOLDING PROJECT #15313 PROFILE
PROJECT TEAM
Manager(s): Miko MiwaInstitution: UIUC
WORK UNIT INFO
Atoms: 84,280Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at how graspetides, special proteins, fold into complex shapes. Graspetides have different types of rings that give them unique abilities. Scientists will use computer simulations to figure out how the order of ring formation depends on the specific type of graspetide and its building blocks.
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 unique type of peptide created by living organisms. They have a special structure with rings that give them strength and allow them to do many important jobs like fighting off infections or stopping enzymes from working. Scientists are studying how these rings form to better understand how graspetides work.
Ribosomally synthesized peptides (RiPPs)
RiPPs
Ribosomally synthesized peptides (RiPPs) are a diverse group of small proteins produced by ribosomes in living organisms. They often undergo further modifications after being made, giving them unique properties and functions.
ATP-grasp ligase
An enzyme that uses ATP to form bonds between molecules.
ATP-grasp ligases are enzymes that play a crucial role in building complex molecules. They use the energy from ATP to join smaller pieces together, creating larger structures essential for many biological processes.
Macrolactam
A cyclic amide formed from a reaction between an amine and a carboxylic acid.
Macrolactam is a type of ring structure found in some peptides. It's formed when two parts of a molecule, an amine and a carboxylic acid, link together to create a closed loop. This ring structure can make the peptide more stable and give it special properties.
Macrolactone
A cyclic ester formed from a reaction between an alcohol and a carboxylic acid.
Macrolactone is another type of ring structure found in some peptides. It's formed when two parts of a molecule, an alcohol and a carboxylic acid, link together to create a closed loop. This ring structure can make the peptide more stable and give it special properties.
Antimicrobial
Relating to the inhibition of microbial growth.
Antimicrobial refers to substances or agents that can kill or inhibit the growth of microorganisms like bacteria, fungi, and viruses. They are essential for treating infections and preventing their spread.
Antiviral
Relating to the inhibition of viral replication.
Antiviral refers to substances or agents that can prevent or reduce the replication of viruses within a host. They are crucial for treating viral infections and managing their spread.
Enzyme inhibitory
Relating to the inhibition of enzyme activity.
Enzyme inhibitory refers to substances or agents that can block or reduce the activity of enzymes. Enzymes are proteins that catalyze (speed up) chemical reactions in living organisms, so inhibiting their activity can have various effects.
Molecular dynamics (MD)
MD
Molecular dynamics (MD) is a powerful computer simulation technique used to study the movement and interactions of atoms and molecules over time. It allows scientists to understand how biological systems work at a detailed level.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:31:47|
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 | 25,146,154 | 11,172 | 2250.82 | 0 hrs 1 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 20,266,919 | 84,610 | 239.53 | 0 hrs 6 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 16,943,212 | 30,177 | 561.46 | 0 hrs 3 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,174,411 | 17,082 | 946.87 | 0 hrs 2 mins |
| 5 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 12,914,312 | 11,172 | 1155.95 | 0 hrs 1 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 12,442,544 | 57,944 | 214.73 | 0 hrs 7 mins |
| 7 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 11,328,175 | 11,172 | 1013.98 | 0 hrs 1 mins |
| 8 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 10,838,311 | 125,814 | 86.15 | 0 hrs 17 mins |
| 9 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,509,647 | 49,697 | 211.47 | 0 hrs 7 mins |
| 10 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 9,144,921 | 11,172 | 818.56 | 0 hrs 2 mins |
| 11 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 9,092,119 | 11,172 | 813.83 | 0 hrs 2 mins |
| 12 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,320,429 | 110,232 | 75.48 | 0 hrs 19 mins |
| 13 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 7,011,604 | 147,196 | 47.63 | 0 hrs 30 mins |
| 14 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 6,717,436 | 11,172 | 601.27 | 0 hrs 2 mins |
| 15 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 6,145,688 | 52,191 | 117.75 | 0 hrs 12 mins |
| 16 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 5,946,119 | 139,175 | 42.72 | 0 hrs 34 mins |
| 17 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,599,311 | 11,172 | 501.19 | 0 hrs 3 mins |
| 18 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,588,979 | 109,947 | 50.83 | 0 hrs 28 mins |
| 19 | GeForce RTX 5070 Ti Mobile GB205M [GeForce RTX 5070 Ti Mobile] |
Nvidia | GB205M | 5,416,769 | 11,172 | 484.85 | 0 hrs 3 mins |
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| 20 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,042,769 | 11,172 | 451.38 | 0 hrs 3 mins |
| 21 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,614,987 | 56,082 | 82.29 | 0 hrs 17 mins |
| 22 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 4,403,444 | 114,045 | 38.61 | 0 hrs 37 mins |
| 23 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 4,208,538 | 241,137 | 17.45 | 1 hrs 23 mins |
| 24 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,902,411 | 11,172 | 349.30 | 0 hrs 4 mins |
| 25 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,438,197 | 11,172 | 307.75 | 0 hrs 5 mins |
| 26 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,392,620 | 11,172 | 303.67 | 0 hrs 5 mins |
| 27 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,357,094 | 11,826 | 283.87 | 0 hrs 5 mins |
| 28 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,492,564 | 11,172 | 223.11 | 0 hrs 6 mins |
| 29 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,427,912 | 11,172 | 217.32 | 0 hrs 7 mins |
| 30 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,423,911 | 11,172 | 216.96 | 0 hrs 7 mins |
| 31 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,410,093 | 11,172 | 215.73 | 0 hrs 7 mins |
| 32 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,316,297 | 103,023 | 22.48 | 1 hrs 4 mins |
| 33 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,164,037 | 99,997 | 21.64 | 1 hrs 7 mins |
| 34 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 1,629,084 | 11,172 | 145.82 | 0 hrs 10 mins |
| 35 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,614,201 | 91,928 | 17.56 | 1 hrs 22 mins |
| 36 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,438,399 | 11,172 | 128.75 | 0 hrs 11 mins |
| 37 | RTX A1000 GA107GL [RTX A1000] |
Nvidia | GA107GL | 1,208,266 | 11,172 | 108.15 | 0 hrs 13 mins |
| 38 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 1,169,944 | 11,172 | 104.72 | 0 hrs 14 mins |
| 39 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,087,124 | 11,172 | 97.31 | 0 hrs 15 mins |
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|||||||
| 40 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 803,453 | 71,473 | 11.24 | 2 hrs 8 mins |
| 41 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 663,649 | 11,172 | 59.40 | 0 hrs 24 mins |
| 42 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 309,041 | 11,172 | 27.66 | 0 hrs 52 mins |
| 43 | Quadro T400 Mobile TU117GLM [Quadro T400 Mobile] |
Nvidia | TU117GLM | 232,340 | 11,172 | 20.80 | 1 hrs 9 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:31:47|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|