RESEARCH: CANCER
FOLDING PROJECT #15307 PROFILE
PROJECT TEAM
Manager(s): Miko MiwaInstitution: UIUC
WORK UNIT INFO
Atoms: 29,693Core: 0x27
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project looks at graspetides, which are special chemicals made by bacteria. These chemicals have a ring shape that makes them strong and useful for fighting infections or blocking enzymes. Scientists will use computer simulations to figure out how these rings form during the creation process 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 with macrolactam or macrolactone linkages.
Graspetides are a special type of peptide created by living organisms. They have a unique ring structure that makes them strong and useful for various purposes like fighting infections, stopping viruses, and inhibiting enzymes. Scientists are trying to understand how these rings are formed and what makes each group of graspetides different.
RiPPs
Ribosomally synthesized and post-translationally modified peptides
RiPPs are short chains of amino acids produced by ribosomes (protein factories) in cells. They undergo further modifications after creation to become functional molecules with diverse roles like antibiotics or signaling agents.
ATP-grasp ligase
An enzyme that uses ATP to catalyze the formation of peptide bonds.
ATP-grasp ligases are proteins that act like molecular glue, using energy from ATP (cellular energy currency) to join amino acids together and build larger peptides. They play a crucial role in creating specific types of peptides, including graspetides.
Macrolactam
A cyclic amide formed by a reaction between an amine and a carboxylic acid.
Macrolactam is a special type of ring structure found in some peptides. It's created when an amino group (NH2) reacts with a carboxylic acid group (COOH), forming a stable ring that increases the peptide's stability and helps it perform its functions.
Macrolactone
A cyclic ester formed by a reaction between an alcohol and a carboxylic acid.
Macrolactone is another type of ring structure found in some peptides. It's created when an alcohol group (OH) reacts with a carboxylic acid group (COOH), forming a stable ring that increases the peptide's stability and helps it perform its functions.
Molecular dynamics (MD)
A computer simulation method used to model the movement of atoms and molecules over time.
Molecular dynamics (MD) is like a virtual microscope that lets scientists see how molecules move and interact at the atomic level. It uses computer simulations to track the movements of atoms in a molecule, providing insights into how they fold, bind to other molecules, and carry out their functions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Tuesday, 14 April 2026 06:31:50|
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 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 69,862,005 | 612,965 | 113.97 | 0 hrs 13 mins |
| 2 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 28,158,290 | 453,523 | 62.09 | 0 hrs 23 mins |
| 3 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 10,373,663 | 26,386 | 393.15 | 0 hrs 4 mins |
| 4 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 10,258,204 | 8,424 | 1217.74 | 0 hrs 1 mins |
| 5 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 8,284,370 | 11,009 | 752.51 | 0 hrs 2 mins |
| 6 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 8,063,938 | 25,965 | 310.57 | 0 hrs 5 mins |
| 7 | GeForce GTX 1070 Mobile GP104BM [GeForce GTX 1070 Mobile] 6463 |
Nvidia | GP104BM | 7,574,661 | 16,129 | 469.63 | 0 hrs 3 mins |
| 8 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 7,548,864 | 10,281 | 734.25 | 0 hrs 2 mins |
| 9 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 7,102,168 | 19,989 | 355.30 | 0 hrs 4 mins |
| 10 | GeForce RTX 5060 GB206 [GeForce RTX 5060] |
Nvidia | GB206 | 5,525,546 | 2,259 | 2446.01 | 0 hrs 1 mins |
| 11 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 5,421,361 | 2,259 | 2399.89 | 0 hrs 1 mins |
| 12 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 5,377,439 | 2,259 | 2380.45 | 0 hrs 1 mins |
| 13 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,952,466 | 16,622 | 297.95 | 0 hrs 5 mins |
| 14 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,440,379 | 18,705 | 237.39 | 0 hrs 6 mins |
| 15 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 4,361,196 | 15,073 | 289.34 | 0 hrs 5 mins |
| 16 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 3,796,267 | 2,259 | 1680.51 | 0 hrs 1 mins |
| 17 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 3,484,327 | 2,259 | 1542.42 | 0 hrs 1 mins |
| 18 | GeForce RTX 5070 Ti Mobile GB205M [GeForce RTX 5070 Ti Mobile] |
Nvidia | GB205M | 3,406,295 | 2,259 | 1507.88 | 0 hrs 1 mins |
| 19 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,302,816 | 10,658 | 309.89 | 0 hrs 5 mins |
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| 20 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,563,900 | 2,259 | 1134.97 | 0 hrs 1 mins |
| 21 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 2,537,108 | 2,259 | 1123.11 | 0 hrs 1 mins |
| 22 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 2,272,195 | 32,748 | 69.38 | 0 hrs 21 mins |
| 23 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,253,001 | 2,259 | 997.34 | 0 hrs 1 mins |
| 24 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,079,083 | 2,259 | 920.36 | 0 hrs 2 mins |
| 25 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 1,988,438 | 2,259 | 880.23 | 0 hrs 2 mins |
| 26 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 1,733,657 | 32,058 | 54.08 | 0 hrs 27 mins |
| 27 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,241,422 | 2,259 | 549.54 | 0 hrs 3 mins |
| 28 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 1,209,259 | 2,259 | 535.31 | 0 hrs 3 mins |
| 29 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 836,371 | 2,259 | 370.24 | 0 hrs 4 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Tuesday, 14 April 2026 06:31:50|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|