PPDDB data as of Sunday, 29 November 2020 11:22:33


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.


(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.


Manager(s): Si Zhang

Institution: Temple University

Project URL:


Atoms: 6,800

Core: OPENMM_21

Status: Public


Model Make GPU PPD
Points WU
WUs Day
WU Time
GeForce RTX 2080 Ti Nvidia TU102 1,261,066 124,035 10.17 2 hrs 22 mins
GeForce RTX 3080 10GB / 20GB Nvidia GA102 1,152,110 119,074 9.68 2 hrs 29 mins
GeForce RTX 2080 Super Nvidia TU104 1,064,650 116,330 9.15 3 hrs 37 mins
GeForce RTX 2060 Super Nvidia TU106 919,002 111,316 8.26 3 hrs 54 mins
GeForce RTX 2080 Ti Rev. A Nvidia TU102 834,886 107,642 7.76 3 hrs 6 mins
GeForce RTX 2080 SUPER Mobile / Max-Q Nvidia TU104M 789,216 105,188 7.50 3 hrs 12 mins
Tesla T4 Nvidia TU104GL 756,928 104,285 7.26 3 hrs 18 mins
GeForce RTX 2070 SUPER Nvidia TU104 740,258 98,152 7.54 3 hrs 11 mins
GeForce GTX 1070 Ti Nvidia GP104 688,732 101,148 6.81 4 hrs 31 mins
GeForce GTX 1080 Nvidia GP104 676,070 99,889 6.77 4 hrs 33 mins
GeForce RTX 2060 Nvidia TU104 666,861 99,837 6.68 4 hrs 36 mins
GeForce RTX 2060 Nvidia TU106 632,080 98,239 6.43 4 hrs 44 mins
Radeon RX Vega 56/64 AMD Vega 10 XL/XT 552,308 92,906 5.94 4 hrs 2 mins
P106-100 Nvidia GP106 435,360 86,669 5.02 5 hrs 47 mins
GeForce GTX 980 Nvidia GM204 366,547 81,879 4.48 5 hrs 22 mins
GeForce GTX 1060 6GB Nvidia GP106 363,335 81,470 4.46 5 hrs 23 mins
Radeon RX 470/480/570/580/590 AMD Ellesmere XT 339,499 79,980 4.24 6 hrs 39 mins
GeForce GTX 970 Nvidia GM204 292,773 76,214 3.84 6 hrs 15 mins
P106-090 Nvidia GP106 215,624 68,680 3.14 8 hrs 39 mins
Radeon R9 200/300 Series AMD Hawaii 213,246 67,138 3.18 8 hrs 33 mins
Radeon R9 M295X AMD Amethyst XT 156,948 58,987 2.66 9 hrs 1 mins
Polaris11 AMD Baffin 113,136 55,341 2.04 12 hrs 44 mins
GeForce GTX 750 Ti Nvidia GM107 84,917 50,485 1.68 14 hrs 16 mins
Radeon HD 7800 AMD Pitcairn 83,144 49,981 1.66 14 hrs 26 mins


CPU Model Make Logical
Processors (LP)
AVG PPD per 1 LP

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