Dunlop, Alex, Bowman, Kate, Aarstad, Olav, Skjåk-Bræk, Gudmund, Stokke, Bjørn T. and Round, Andrew N. ORCID: https://orcid.org/0000-0001-9026-0620 (2017) Polymer sequencing by molecular machines: A framework for predicting the resolving power of a sliding contact force spectroscopy sequencing method. Nanoscale, 9 (39). pp. 15089-15097. ISSN 2040-3364
Preview |
PDF (c7nr03358c)
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
We evaluate an AFM-based single molecule force spectroscopy method for mapping sequences in otherwise difficult to sequence heteropolymers, including glycosylated proteins and glycans. The sliding contact force spectroscopy (SCFS) method exploits a sliding contact made between a nanopore threaded over a polymer axle and an AFM probe. We find that for sliding α- and β- cyclodextrin nanopores over a wide range of hydrophilic monomers, the free energy of sliding is proportional to the sum of two dimensionless, easily calculable parameters representing the relative partitioning of the monomer inside the nanopore or in the aqueous phase, and the friction arising from sliding the nanopore over the monomer. Using this relationship we calculate sliding energies for nucleic acids, amino acids, glycan and synthetic monomers and predict on the basis of these calculations that SCFS will detect N- and O-glycosylation of proteins and patterns of sidechains in glycans. For these applications, SCFS offers an alternative to sequence mapping by mass spectrometry or newly-emerging nanopore technologies that may be easily implemented using a standard AFM.
Item Type: | Article |
---|---|
Faculty \ School: | Faculty of Science > School of Pharmacy (former - to 2024) |
UEA Research Groups: | Faculty of Science > Research Groups > Drug Delivery and Pharmaceutical Materials (former - to 2017) |
Depositing User: | Pure Connector |
Date Deposited: | 03 Aug 2017 08:17 |
Last Modified: | 25 Dec 2024 00:50 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/64334 |
DOI: | 10.1039/c7nr03358c |
Downloads
Downloads per month over past year
Actions (login required)
View Item |