The aggregation of striped nanoparticles in mixed phospholipid bilayers
Source abstract
Abstract
The unique and adjustable properties of nanoparticles present enormous opportunities for their use as targeted drug delivery vectors. For example, nanoparticles functionalized with key surface ligands have been shown to pass through phospholipid bilayers without causing localised disruption. However, the further effects nanoparticles have on multi-component phospholipid bilayers remain unclear. We use coarse-grained computational models to investigate the structural properties of mixed phospholipid bilayers in the presence of ligand-functionalized nanoparticles. Model bilayers are composed of DPPC, DUPC, DFPC and cholesterol, and the nanoparticles are striped with a hydrophobic-ligand band and charged-ligand spherical caps. Our results show that nanoparticles aggregate near unsaturated phospholipid regions, phospholipid bilayer phase-separation is promoted in the presence of nanoparticles, and the heterogeneous components of a phospholipid bilayer play a significant role in the lateral organization of nanoparticles. This study highlights the need for considering the complexity of realistic phospholipid bilayers when optimising ligand functionalized nanoparticles for efficient drug delivery vectors.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Lipid Membrane Structure and Behavior · Nanoparticle-Based Drug Delivery · RNA Interference and Gene Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemistry
University of Warwick
Coventry
UK
Nuffield Department of Clinical Neurosciences
University of Oxford
Oxford
References (76)
Show source-provided references
1. Allen · Science · 2004 · 303 · 181810.1126/science.1095833 ↗
2. Blanco · Nat. Biotechnol. · 2015 · 33 · 94110.1038/nbt.3330 ↗
3. Jang · Molecules · 2017 · 22 · E219710.3390/molecules22122197 ↗
4. Kang · J. Phys. Chem. B · 2014 · 118 · 1196510.1021/jp5047613 ↗
5. Dadwal · Artif. Cells, Nanomed., Biotechnol. · 2018 · 46 · 110.1080/21691401.2018.1457039 ↗
6. Werner · Biointerphases · 2018 · 13 · 02850110.1116/1.5022145 ↗
7. Walrant · Acc. Chem. Res. · 2017 · 50 · 296810.1021/acs.accounts.7b00455 ↗
8. Bennion · J. Phys. Chem. B · 2017 · 121 · 522810.1021/acs.jpcb.7b02914 ↗
9. Aller · Science · 2009 · 323 · 171810.1126/science.1168750 ↗
10. Rossi · Adv. Phys.: X · 2016 · 1 · 276
11. Cho · Clin. Cancer Res. · 2008 · 14 · 131010.1158/1078-0432.CCR-07-1441 ↗
12. Meka · Nanoscale · 2019 · 11 · 793110.1039/C8NR10293G ↗
13. Baoukina · J. Am. Chem. Soc. · 2012 · 134 · 1754310.1021/ja304792p ↗
14. London · Biophys. J. · 2016 · 111 · 46510.1016/j.bpj.2016.06.037 ↗
15. Schmid · Biochim. Biophys. Acta · 2017 · 1859 · 50910.1016/j.bbamem.2016.10.021 ↗
16. Crane · Biophys. J. · 2004 · 86 · 296510.1016/S0006-3495(04)74347-7 ↗
17. Yesylevskyy · Sci. World J. · 2013 · 110.1155/2013/965230 ↗
18. Ong · Acc. Chem. Res. · 2017 · 50 · 191110.1021/acs.accounts.7b00165 ↗
19. Gibson · J. Am. Chem. Soc. · 2007 · 129 · 1165310.1021/ja075181k ↗
20. Pengo · Eur. Biophys. J. · 2017 · 46 · 74910.1007/s00249-017-1250-6 ↗
21. Colangelo · Bioconjugate Chem. · 2017 · 28 · 1110.1021/acs.bioconjchem.6b00587 ↗
22. Angeioletti-Uberti · npj Comput. Mater. · 2017 · 3 · 1910.1038/s41524-017-0022-2 ↗
23. Zhang · ACS Nano · 2015 · 9 · 865510.1021/acsnano.5b03184 ↗
24. Yuan · Phys. Rev. Lett. · 2010 · 105 · 13810110.1103/PhysRevLett.105.138101 ↗
25. Van Lehn · J. Phys. Chem. · 2014 · 118 · 584810.1021/jp411662c ↗
26. Van Lehn · Nat. Commun. · 2014 · 5 · 448210.1038/ncomms5482 ↗
27. Van Lehn · Soft Matter · 2014 · 10 · 64810.1039/C3SM52329B ↗
28. Jackson · Nat. Mater. · 2004 · 3 · 33010.1038/nmat1116 ↗
29. Quan · Langmuir · 2017 · 33 · 36110.1021/acs.langmuir.6b02937 ↗
30. Barnoud · PLoS Comput. Biol. · 2014 · 10 · e100387310.1371/journal.pcbi.1003873 ↗
31. Jewell · Angew. Chem., Int. Ed. · 2011 · 50 · 1231210.1002/anie.201104514 ↗
32. Berendsen · Comput. Phys. Commun. · 1995 · 91 · 4310.1016/0010-4655(95)00042-E ↗
33. Abraham · SoftwareX · 2015 · 1–2 · 1910.1016/j.softx.2015.06.001 ↗
34. Parrinello · Phys. Rev. Lett. · 1980 · 45 · 119610.1103/PhysRevLett.45.1196 ↗
35. Parrinello · J. Appl. Phys. · 1981 · 52 · 718210.1063/1.328693 ↗
36. Parrinello · J. Chem. Phys. · 1982 · 76 · 266210.1063/1.443248 ↗
37. Risselada · Proc. Natl. Acad. Sci. U. S. A. · 2008 · 105 · 1736710.1073/pnas.0807527105 ↗
38. Davis · J. Phys. Chem. B · 2013 · 117 · 407210.1021/jp4000686 ↗
39. Chen · Nanoscale · 2018 · 10 · 248110.1039/C7NR06494B ↗
40. Marrink · J. Phys. Chem. B · 2007 · 111 · 781210.1021/jp071097f ↗
41. Wassenar · J. Chem. Theory Comput. · 2015 · 11 · 214410.1021/acs.jctc.5b00209 ↗
42. Martínez · J. Comput. Chem. · 2003 · 24 · 81910.1002/jcc.10216 ↗
43. Ge · Small · 2015 · 11 · 489410.1002/smll.201501049 ↗
44. Simonelli · J. Phys. Chem. Lett. · 2015 · 6 · 317510.1021/acs.jpclett.5b01469 ↗
45. Gkeka · PLoS Comput. Biol. · 2014 · 12 · e100391710.1371/journal.pcbi.1003917 ↗
46. Angeliopoulos · Nanoscale · 2017 · 9 · 104010.1039/C6NR05853A ↗
47. D. Frenkel and B.Smit , Understanding Molecular Simulation: From Algorithms to Applications , 2001
48. D. C. Rapaport , The Art of Molecular Simulation , 200410.1017/CBO9780511816581 ↗
49. Verlet · Phys. Rev. · 1962 · 159 · 9810.1103/PhysRev.159.98 ↗
50. Swope · J. Chem. Phys. · 1982 · 76 · 63710.1063/1.442716 ↗
51. Castillo · Chem. Phys. Lipids · 2013 · 169 · 9510.1016/j.chemphyslip.2013.02.001 ↗
52. Vanegas · J. Chem. Theory Comput. · 2014 · 10 · 69110.1021/ct4008926 ↗
53. Torres-Sànchez · Phys. Rev. Lett. · 2015 · 114 · 25810210.1103/PhysRevLett.114.258102 ↗
54. Katira · eLife · 2016 · 11 · 1315010.7554/eLife.13150 ↗
55. Halperin · Phys. Rev. Lett. · 1978 · 41 · 12110.1103/PhysRevLett.41.121 ↗
56. Nelson · Phys. Rev. B: Condens. Matter Mater. Phys. · 1979 · 19 · 245710.1103/PhysRevB.19.2457 ↗
57. Pantelopoulos · J. Chem. Phys. · 2017 · 147 · 09510110.1063/1.4999709 ↗
58. Humphrey · J. Mol. Graphics · 1996 · 14 · 3310.1016/0263-7855(96)00018-5 ↗
59. Lukat · J. Chem. Inf. Model. · 2013 · 53 · 290810.1021/ci400172g ↗
60. Michaud-Agrawal · J. Comput. Chem. · 2011 · 32 · 231910.1002/jcc.21787 ↗
61. Cheung · J. Chem. Phys. · 2014 · 141 · 19490810.1063/1.4901740 ↗
62. Hu · Phys. Rev. E · 2019 · 99 · 01240310.1103/PhysRevE.99.012403 ↗
63. Schäfer · Proc. Natl. Acad. Sci. U. S. A. · 2011 · 108 · 134310.1073/pnas.1009362108 ↗
64. Parton · Biophys. J. · 2011 · 101 · 69110.1016/j.bpj.2011.06.048 ↗
65. Madsen · Proc. Natl. Acad. Sci. U. S. A. · 2018 · 115 · E859510.1073/pnas.1805443115 ↗
66. Parton · PLoS Comput. Biol. · 2013 · 9 · e100303410.1371/journal.pcbi.1003034 ↗
67. Ollila · Phys. Rev. Lett. · 2009 · 102 · 07810110.1103/PhysRevLett.102.078101 ↗
68. Yefimov · Biophys. J. · 2008 · 94 · 299410.1529/biophysj.107.119966 ↗
69. Yoo · Biophys. J. · 2013 · 104 · 12810.1016/j.bpj.2012.11.3813 ↗
70. Heinrich · J. Phys. Chem. B · 2008 · 112 · 806310.1021/jp7116246 ↗
71. Baumgart · Nature · 2003 · 23 · 82110.1038/nature02013 ↗
72. Tian · Phys. Rev. Lett. · 2007 · 98 · 20810210.1103/PhysRevLett.98.208102 ↗
73. Lipowsky · J. Phys. II · 1992 · 2 · 1825
74. Rosetti · J. Phys. Chem. B · 2017 · 121 · 158710.1021/acs.jpcb.6b10836 ↗
75. Rasch · Nano Lett. · 2010 · 10 · 373310.1021/nl102387n ↗
76. Guo · Sci. Adv. · 2016 · 2 · e160026110.1126/sciadv.1600261 ↗