Nanoscale binding site localization by molecular distance estimation on native cell surfaces using topological image averaging.
| Autor: | Kumra Ahnlide V; Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden., Kumra Ahnlide J; Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden., Wrighton S; Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden., Beech JP; Division of Solid State Physics, Department of Physics, Lund University, Lund, Sweden., Nordenfelt P; Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden. |
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| Jazyk: | angličtina |
| Zdroj: | ELife [Elife] 2022 Feb 24; Vol. 11. Date of Electronic Publication: 2022 Feb 24. |
| DOI: | 10.7554/eLife.64709 |
| Abstrakt: | Antibody binding to cell surface proteins plays a crucial role in immunity, and the location of an epitope can altogether determine the immunological outcome of a host-target interaction. Techniques available today for epitope identification are costly, time-consuming, and unsuited for high-throughput analysis. Fast and efficient screening of epitope location can be useful for the development of therapeutic monoclonal antibodies and vaccines. Cellular morphology typically varies, and antibodies often bind heterogeneously across a cell surface, making traditional particle-averaging strategies challenging for accurate native antibody localization. In the present work, we have developed a method, SiteLoc, for imaging-based molecular localization on cellular surface proteins. Nanometer-scale resolution is achieved through localization in one dimension, namely, the distance from a bound ligand to a reference surface. This is done by using topological image averaging. Our results show that this method is well suited for antibody binding site measurements on native cell surface morphology and that it can be applied to other molecular distance estimations as well. Competing Interests: VK, JK, SW, JB, PN No competing interests declared (© 2022, Kumra Ahnlide et al.) |
| Databáze: | MEDLINE |
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