RESEARCH: CANCER
FOLDING PROJECT #15312 PROFILE
PROJECT TEAM
Manager(s): Miko MiwaInstitution: UIUC
WORK UNIT INFO
Atoms: 89,572Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project relates to graspetides - tiny, powerful proteins with unique ring structures. Scientists will use computer simulations to figure out how these rings form and why different types of graspetides have different ring patterns. This could lead to new medicines based on these amazing molecules.
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) characterized by ATP-grasp ligase-catalyzed macrolactam or macrolactone linkages in their structure.
Graspetides are a type of antimicrobial peptide produced by bacteria. They have a unique structure with rings formed through chemical reactions. These rings give them stability and allow them to fight against viruses, bacteria, and even enzymes. Researchers are studying how the specific sequence of amino acids in graspetides determines the formation of these rings.
RiPPs
Ribosomally synthesized and post-translationally modified peptides
RiPPs are a diverse group of small proteins produced by bacteria. They are made on ribosomes like regular proteins but then undergo further chemical modifications after synthesis. These modifications give them unique properties and functions, such as fighting infections or regulating cellular processes.
ATP-grasp ligase
An enzyme family that catalyzes the formation of macrolactam or macrolactone linkages in peptides.
ATP-grasp ligases are specialized enzymes that play a crucial role in building the unique rings found in graspetides. They use energy from ATP (a cellular energy source) to join specific parts of a peptide chain together, forming stable rings. This process is essential for giving graspetides their antimicrobial properties.
Macrolactam
A cyclic structure formed by a peptide bond between the carboxyl group of one amino acid and the amine group of another amino acid.
Macrolactam is a type of ring structure commonly found in peptides. It's formed when the end of a peptide chain connects back to itself through a specific chemical bond. This creates a stable loop, contributing to the overall stability and function of the peptide.
Macrolactone
A cyclic structure formed by an ester bond between the carboxyl group of one amino acid and the hydroxyl group of another.
Macrolactone is another type of ring structure often found in peptides. It's similar to macrolactam but uses a different type of chemical bond – an ester bond – to connect parts of the peptide chain. This creates a stable loop, contributing to the peptide's overall stability and function.
Molecular dynamics (MD)
A computer simulation method used to study the movement and interactions of atoms and molecules over time.
Molecular dynamics (MD) is a powerful tool for understanding how molecules behave. It involves simulating the movements of atoms and molecules in a system over time. This allows researchers to study how proteins fold, how drugs interact with their targets, and other complex biological processes.
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 | 24,314,152 | 12,072 | 2014.09 | 0 hrs 1 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 19,276,945 | 61,438 | 313.76 | 0 hrs 5 mins |
| 3 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 16,129,898 | 35,306 | 456.86 | 0 hrs 3 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,028,235 | 22,947 | 698.49 | 0 hrs 2 mins |
| 5 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 12,838,641 | 49,600 | 258.84 | 0 hrs 6 mins |
| 6 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 12,612,545 | 12,072 | 1044.78 | 0 hrs 1 mins |
| 7 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 11,070,461 | 135,902 | 81.46 | 0 hrs 18 mins |
| 8 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 10,363,869 | 51,775 | 200.17 | 0 hrs 7 mins |
| 9 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 10,204,324 | 12,072 | 845.29 | 0 hrs 2 mins |
| 10 | GeForce RTX 5070 GB205 [GeForce RTX 5070] |
Nvidia | GB205 | 9,568,798 | 12,072 | 792.64 | 0 hrs 2 mins |
| 11 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 8,282,716 | 163,228 | 50.74 | 0 hrs 28 mins |
| 12 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,489,090 | 153,762 | 48.71 | 0 hrs 30 mins |
| 13 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,916,922 | 122,398 | 56.51 | 0 hrs 25 mins |
| 14 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,885,915 | 12,072 | 570.40 | 0 hrs 3 mins |
| 15 | GeForce RTX 5060 Ti GB206 [GeForce RTX 5060 Ti] |
Nvidia | GB206 | 6,247,966 | 12,072 | 517.56 | 0 hrs 3 mins |
| 16 | GeForce RTX 4090 Laptop GPU AD103M / GN21-X11 [GeForce RTX 4090 Laptop GPU] |
Nvidia | AD103M / GN21-X11 | 6,128,576 | 12,072 | 507.67 | 0 hrs 3 mins |
| 17 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 5,897,555 | 147,245 | 40.05 | 0 hrs 36 mins |
| 18 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,637,153 | 111,192 | 50.70 | 0 hrs 28 mins |
| 19 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,561,276 | 12,072 | 460.68 | 0 hrs 3 mins |
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| 20 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 5,356,068 | 38,951 | 137.51 | 0 hrs 10 mins |
| 21 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 4,869,827 | 12,072 | 403.40 | 0 hrs 4 mins |
| 22 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,333,729 | 130,620 | 33.18 | 0 hrs 43 mins |
| 23 | GeForce RTX 5070 Ti Mobile GB205M [GeForce RTX 5070 Ti Mobile] |
Nvidia | GB205M | 4,296,254 | 12,072 | 355.89 | 0 hrs 4 mins |
| 24 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 4,005,670 | 237,632 | 16.86 | 1 hrs 25 mins |
| 25 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 3,929,376 | 125,692 | 31.26 | 0 hrs 46 mins |
| 26 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,788,152 | 12,072 | 313.80 | 0 hrs 5 mins |
| 27 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,452,990 | 12,072 | 286.03 | 0 hrs 5 mins |
| 28 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,381,875 | 93,346 | 36.23 | 0 hrs 40 mins |
| 29 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,330,388 | 12,072 | 275.88 | 0 hrs 5 mins |
| 30 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,225,888 | 12,072 | 267.22 | 0 hrs 5 mins |
| 31 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,959,381 | 115,139 | 25.70 | 0 hrs 56 mins |
| 32 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,774,547 | 113,987 | 24.34 | 0 hrs 59 mins |
| 33 | Quadro RTX 4000 TU104GL [Quadro RTX 4000] |
Nvidia | TU104GL | 2,464,516 | 12,072 | 204.15 | 0 hrs 7 mins |
| 34 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,307,425 | 12,072 | 191.14 | 0 hrs 8 mins |
| 35 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,215,855 | 75,426 | 29.38 | 0 hrs 49 mins |
| 36 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,728,611 | 12,072 | 143.19 | 0 hrs 10 mins |
| 37 | GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q] |
Nvidia | TU106M | 1,650,871 | 12,072 | 136.75 | 0 hrs 11 mins |
| 38 | RTX A2000 GA106 [RTX A2000] |
Nvidia | GA106 | 1,648,084 | 12,072 | 136.52 | 0 hrs 11 mins |
| 39 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,647,824 | 96,657 | 17.05 | 1 hrs 24 mins |
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| 40 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 1,478,123 | 19,656 | 75.20 | 0 hrs 19 mins |
| 41 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,445,343 | 12,072 | 119.73 | 0 hrs 12 mins |
| 42 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,260,092 | 50,963 | 24.73 | 0 hrs 58 mins |
| 43 | RTX A1000 GA107GL [RTX A1000] |
Nvidia | GA107GL | 1,198,798 | 12,072 | 99.30 | 0 hrs 15 mins |
| 44 | Tesla P4 GP104GL [Tesla P4] 5704 |
Nvidia | GP104GL | 757,804 | 73,899 | 10.25 | 2 hrs 20 mins |
| 45 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 450,897 | 62,576 | 7.21 | 3 hrs 20 mins |
| 46 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 391,873 | 59,340 | 6.60 | 3 hrs 38 mins |
| 47 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 275,034 | 12,072 | 22.78 | 1 hrs 3 mins |
| 48 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 238,527 | 50,195 | 4.75 | 5 hrs 3 mins |
| 49 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 105,449 | 12,072 | 8.74 | 2 hrs 45 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 |
|---|