PROJECT #16907 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE
PROJECT SUMMARY
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.
PROJECT INFO
Manager(s): Si Zhang
Institution: Temple University
Project URL: http://voelzlab.org
PROJECT WORK UNIT SUMMARY
Atoms: 6,800
Core: OPENMM_21
Status: Public
PROJECT FOLDING PPD AVERAGES BY GPU
PPDDB data as of Monday, 19 April 2021 05:10:44
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 | 3 hrs 37 mins |
4 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 919,002 | 111,316 | 8.26 | 3 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 | 4 hrs 31 mins |
10 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 676,070 | 99,889 | 6.77 | 4 hrs 33 mins |
11 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 666,861 | 99,837 | 6.68 | 4 hrs 36 mins |
12 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 632,080 | 98,239 | 6.43 | 4 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 | 5 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 | 6 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 | 8 hrs 39 mins |
20 | Radeon R9 200/300 Series Hawaii [Radeon R9 200/300 Series] |
AMD | Hawaii | 213,246 | 67,138 | 3.18 | 8 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 | 12 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
PPDDB data as of Monday, 19 April 2021 05:10:44
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
---|
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