Carbon nanotube porin diffusion in mixed composition supported lipid bilayers
Abstract
Carbon nanotube porins (CNTPs), short pieces of carbon nanotubes capable of self-inserting into a lipid bilayer, represent a simplified model of biological membrane channels. We have used high-speed atomic force microscopy (HS-AFM) and all-atom molecular dynamics (MD) simulations to study the behavior of CNTPs in a mixed lipid membrane consisting of DOPC lipid with a variable percentage of DMPC lipid added to it. HS-AFM data reveal that the CNTPs undergo diffusive motion in the bilayer plane. Motion trajectories extracted from the HS-AFM movies indicate that CNTPs exhibit diffusion coefficient values broadly similar to values reported for membrane proteins in supported lipid bilayers. The data also indicate that increasing the percentage of DMPC leads to a marked slowing of CNTP diffusion. MD simulations reveal a CNTP-lipid assembly that diffuses in the membrane and show trends that are consistent with the experimental observations.
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation (NSF)
- OSTI Identifier:
- 1638906
- Alternate Identifier(s):
- OSTI ID: 1642362
- Report Number(s):
- LLNL-JRNL-800281
Journal ID: ISSN 2045-2322; 11908; PII: 68059
- Grant/Contract Number:
- AC52-07NA27344; SCW1607; AC02-05CH11231; 1626262
- Resource Type:
- Published Article
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Name: Scientific Reports Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 77 NANOSCIENCE AND NANOTECHNOLOGY; Biophysics; Nanoscale biophysics
Citation Formats
Sullivan, Kylee, Zhang, Yuliang, Lopez, Joseph, Lowe, Mary, and Noy, Aleksandr. Carbon nanotube porin diffusion in mixed composition supported lipid bilayers. United Kingdom: N. p., 2020.
Web. doi:10.1038/s41598-020-68059-2.
Sullivan, Kylee, Zhang, Yuliang, Lopez, Joseph, Lowe, Mary, & Noy, Aleksandr. Carbon nanotube porin diffusion in mixed composition supported lipid bilayers. United Kingdom. https://doi.org/10.1038/s41598-020-68059-2
Sullivan, Kylee, Zhang, Yuliang, Lopez, Joseph, Lowe, Mary, and Noy, Aleksandr. Fri .
"Carbon nanotube porin diffusion in mixed composition supported lipid bilayers". United Kingdom. https://doi.org/10.1038/s41598-020-68059-2.
@article{osti_1638906,
title = {Carbon nanotube porin diffusion in mixed composition supported lipid bilayers},
author = {Sullivan, Kylee and Zhang, Yuliang and Lopez, Joseph and Lowe, Mary and Noy, Aleksandr},
abstractNote = {Carbon nanotube porins (CNTPs), short pieces of carbon nanotubes capable of self-inserting into a lipid bilayer, represent a simplified model of biological membrane channels. We have used high-speed atomic force microscopy (HS-AFM) and all-atom molecular dynamics (MD) simulations to study the behavior of CNTPs in a mixed lipid membrane consisting of DOPC lipid with a variable percentage of DMPC lipid added to it. HS-AFM data reveal that the CNTPs undergo diffusive motion in the bilayer plane. Motion trajectories extracted from the HS-AFM movies indicate that CNTPs exhibit diffusion coefficient values broadly similar to values reported for membrane proteins in supported lipid bilayers. The data also indicate that increasing the percentage of DMPC leads to a marked slowing of CNTP diffusion. MD simulations reveal a CNTP-lipid assembly that diffuses in the membrane and show trends that are consistent with the experimental observations.},
doi = {10.1038/s41598-020-68059-2},
journal = {Scientific Reports},
number = 1,
volume = 10,
place = {United Kingdom},
year = {Fri Jul 17 00:00:00 EDT 2020},
month = {Fri Jul 17 00:00:00 EDT 2020}
}
https://doi.org/10.1038/s41598-020-68059-2
Web of Science
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