RESEARCH: CANCER
FOLDING PROJECT #16907 PROFILE
WORK UNIT INFO
Atoms: 6,800Core: OPENMM_21
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project studies how cyclic peptides, which are small rings of amino acids, move across cell membranes. Some, like cyclosporin A, can pass through easily, while others struggle. By using computer simulations, researchers will figure out how the shape and structure of these peptides affect their ability to cross membranes.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Recently, cyclic peptides have gained increasing interests due to their potential applications in the “undruggable” target space of intracellular protein-protein interactions that are difficult to target using small molecules.
Although the size and complexity of most cyclic peptides often fail to meet Lipinski’s Rule of Five (1) for predicting drug-likeness, there are known examples of natural products such as cyclosporin A (CsA) that can cross cell membrane by passive diffusion.
However, its mutant, CsE has one order of magnitude lower permeability, even though it differs only in one backbone methylation.
Early studies (2, 3) give insights into studying the conformational behaviors of CsA and CsE from kinetic aspect to understand the siginificant difference.
Thus, in this study, we will perform all-atom MD simulations to study the conformational behaviors of CsA and its varivants in solvents and crossing membrane.
We hope with the kinetic information obtained from our MD simulation, we could investigate and rationalize the differences in permeability of CsA and its variants as well as other cyclic peptide families from kinetic aspects. References: (1) Lipinski, C.
A.
(2000).
Drug-like properties and the causes of poor solubility and poor permeability.
Journal of Pharmacological and Toxicological Methods. (2) Witek, J., Keller, B.
G., Blatter, M., Meissner, A., Wagner, T., & Riniker, S.
(2016).
Kinetic Models of Cyclosporin A in Polar and Apolar Environments Reveal Multiple Congruent Conformational States.
Journal of Chemical Information and Modeling. (3) Ahlbach, C.
L., Lexa, K.
W., Bockus, A.
T., Chen, V., Crews, P., Jacobson, M.
P., & Lokey, R.
S.
(2015).
Beyond cyclosporine A: Conformation-dependent passive membrane permeabilities of cyclic peptide natural products.
Future Medicinal Chemistry.
RELATED TERMS GLOSSARY AI BETA
cyclic peptides
A class of peptide molecules with a cyclic structure.
Cyclic peptides are a type of molecule made up of amino acids joined together in a ring-like shape. They have gained attention in drug development because they can target specific proteins involved in diseases.
undruggable
Describes targets that are difficult to drug with conventional small molecules.
Undruggable targets are proteins or biological pathways that have been historically challenging to develop drugs against. This is often due to their complex structure or location within the cell.
protein-protein interactions
Interactions between two or more protein molecules.
Protein-protein interactions are essential for many cellular processes. They allow proteins to work together, form complexes, and regulate each other's activity.
small molecules
Low molecular weight organic compounds that can be used as drugs.
Small molecules are often the building blocks of pharmaceuticals. They can bind to specific targets in the body, like proteins or enzymes, and alter their function.
Lipinski's Rule of Five
Rule of five
A set of guidelines used to predict the likelihood that a molecule will be orally active. It states that a drug candidate should have no more than 5 hydrogen bond donors, 10 hydrogen bond acceptors, a molecular weight less than 500 Daltons, and a lipophilicity (logP) value less than 5.
cyclosporin A
A cyclic peptide immunosuppressive drug.
Cyclosporin A (CsA) is a powerful drug used to suppress the immune system. It was originally discovered as a natural product from soil fungi and has been widely used in transplantation medicine to prevent organ rejection.
CsE
Mutant of cyclosporin A
CsE is a modified version of Cyclosporin A with a single amino acid change. It exhibits reduced permeability compared to CsA, highlighting the importance of structural variations in drug effectiveness.
MD simulations
Computer simulations of molecular dynamics.
Molecular Dynamics (MD) simulations are powerful tools used to study the movement and interactions of atoms and molecules over time. They provide insights into protein folding, drug binding, and other biological processes.
permeability
The ability of a substance to pass through a membrane.
Permeability is a crucial property for drug molecules. It determines how easily they can cross cell membranes and reach their target sites in the body.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:43:23|
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 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 1,261,066 | 124,035 | 10.17 | 2 hrs 22 mins |
| 2 | GeForce RTX 3080 10GB / 20GB GA102 [GeForce RTX 3080 10GB / 20GB] |
Nvidia | GA102 | 1,152,110 | 119,074 | 9.68 | 2 hrs 29 mins |
| 3 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER] |
Nvidia | TU104 | 1,064,650 | 116,330 | 9.15 | 2 hrs 37 mins |
| 4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 919,002 | 111,316 | 8.26 | 2 hrs 54 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 834,886 | 107,642 | 7.76 | 3 hrs 6 mins |
| 6 | GeForce RTX 2080 SUPER Mobile / Max-Q TU104M [GeForce RTX 2080 SUPER Mobile / Max-Q] |
Nvidia | TU104M | 789,216 | 105,188 | 7.50 | 3 hrs 12 mins |
| 7 | Tesla T4 TU104GL [Tesla T4] 8141 |
Nvidia | TU104GL | 756,928 | 104,285 | 7.26 | 3 hrs 18 mins |
| 8 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 740,258 | 98,152 | 7.54 | 3 hrs 11 mins |
| 9 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 688,732 | 101,148 | 6.81 | 3 hrs 31 mins |
| 10 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 676,070 | 99,889 | 6.77 | 3 hrs 33 mins |
| 11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 666,861 | 99,837 | 6.68 | 3 hrs 36 mins |
| 12 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 632,080 | 98,239 | 6.43 | 3 hrs 44 mins |
| 13 | Radeon RX Vega 56/64 Vega 10 XL/XT [Radeon RX Vega 56/64] |
AMD | Vega 10 XL/XT | 552,308 | 92,906 | 5.94 | 4 hrs 2 mins |
| 14 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 435,360 | 86,669 | 5.02 | 4 hrs 47 mins |
| 15 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 366,547 | 81,879 | 4.48 | 5 hrs 22 mins |
| 16 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 363,335 | 81,470 | 4.46 | 5 hrs 23 mins |
| 17 | Radeon RX 470/480/570/580/590 Ellesmere XT [Radeon RX 470/480/570/580/590] |
AMD | Ellesmere XT | 339,499 | 79,980 | 4.24 | 5 hrs 39 mins |
| 18 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 292,773 | 76,214 | 3.84 | 6 hrs 15 mins |
| 19 | P106-090 GP106 [P106-090] |
Nvidia | GP106 | 215,624 | 68,680 | 3.14 | 7 hrs 39 mins |
|
|
|||||||
| 20 | Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series] |
AMD | Hawaii | 213,246 | 67,138 | 3.18 | 7 hrs 33 mins |
| 21 | Radeon R9 M295X Amethyst XT [Radeon R9 M295X] |
AMD | Amethyst XT | 156,948 | 58,987 | 2.66 | 9 hrs 1 mins |
| 22 | Polaris11 Baffin [Polaris11] |
AMD | Baffin | 113,136 | 55,341 | 2.04 | 11 hrs 44 mins |
| 23 | GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389 |
Nvidia | GM107 | 84,917 | 50,485 | 1.68 | 14 hrs 16 mins |
| 24 | Radeon HD 7800 Pitcairn [Radeon HD 7800] |
AMD | Pitcairn | 83,144 | 49,981 | 1.66 | 14 hrs 26 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:43:23|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|