| Autor: |
Heath GR; Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.; School of Physics and Astronomy, University of Leeds, Leeds, UK., Kots E; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA., Robertson JL; Department of Biochemistry and Molecular Biophysics, Washington University, St. Louis, MO, USA., Lansky S; Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA., Khelashvili G; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA., Weinstein H; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA., Scheuring S; Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA. sis2019@med.cornell.edu.; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA. sis2019@med.cornell.edu. |
| Abstrakt: |
Understanding structural dynamics of biomolecules at the single-molecule level is vital to advancing our knowledge of molecular mechanisms. Currently, there are few techniques that can capture dynamics at the sub-nanometre scale and in physiologically relevant conditions. Atomic force microscopy (AFM) 1 has the advantage of analysing unlabelled single molecules in physiological buffer and at ambient temperature and pressure, but its resolution limits the assessment of conformational details of biomolecules 2 . Here we present localization AFM (LAFM), a technique developed to overcome current resolution limitations. By applying localization image reconstruction algorithms 3 to peak positions in high-speed AFM and conventional AFM data, we increase the resolution beyond the limits set by the tip radius, and resolve single amino acid residues on soft protein surfaces in native and dynamic conditions. LAFM enables the calculation of high-resolution maps from either images of many molecules or many images of a single molecule acquired over time, facilitating single-molecule structural analysis. LAFM is a post-acquisition image reconstruction method that can be applied to any biomolecular AFM dataset. |
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