Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.

Atomic force microscopy (AFM) can visualize functional biomolecules near the physiological condition, but the observed data are limited to the surface height of specimens. Since the AFM images highly depend on the probe tip shape, for successful inference of molecular structures from the measurement...

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Autores principales:	Toru Niina, Yasuhiro Matsunaga, Shoji Takada
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c1bf236f10b544b0afa04f3ccd3d9337
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spelling	oai:doaj.org-article:c1bf236f10b544b0afa04f3ccd3d93372021-12-02T19:57:23ZRigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.1553-734X1553-735810.1371/journal.pcbi.1009215https://doaj.org/article/c1bf236f10b544b0afa04f3ccd3d93372021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009215https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Atomic force microscopy (AFM) can visualize functional biomolecules near the physiological condition, but the observed data are limited to the surface height of specimens. Since the AFM images highly depend on the probe tip shape, for successful inference of molecular structures from the measurement, the knowledge of the probe shape is required, but is often missing. Here, we developed a method of the rigid-body fitting to AFM images, which simultaneously finds the shape of the probe tip and the placement of the molecular structure via an exhaustive search. First, we examined four similarity scores via twin-experiments for four test proteins, finding that the cosine similarity score generally worked best, whereas the pixel-RMSD and the correlation coefficient were also useful. We then applied the method to two experimental high-speed-AFM images inferring the probe shape and the molecular placement. The results suggest that the appropriate similarity score can differ between target systems. For an actin filament image, the cosine similarity apparently worked best. For an image of the flagellar protein FlhAC, we found the correlation coefficient gave better results. This difference may partly be attributed to the flexibility in the target molecule, ignored in the rigid-body fitting. The inferred tip shape and placement results can be further refined by other methods, such as the flexible fitting molecular dynamics simulations. The developed software is publicly available.Toru NiinaYasuhiro MatsunagaShoji TakadaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009215 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Toru Niina Yasuhiro Matsunaga Shoji Takada Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
description	Atomic force microscopy (AFM) can visualize functional biomolecules near the physiological condition, but the observed data are limited to the surface height of specimens. Since the AFM images highly depend on the probe tip shape, for successful inference of molecular structures from the measurement, the knowledge of the probe shape is required, but is often missing. Here, we developed a method of the rigid-body fitting to AFM images, which simultaneously finds the shape of the probe tip and the placement of the molecular structure via an exhaustive search. First, we examined four similarity scores via twin-experiments for four test proteins, finding that the cosine similarity score generally worked best, whereas the pixel-RMSD and the correlation coefficient were also useful. We then applied the method to two experimental high-speed-AFM images inferring the probe shape and the molecular placement. The results suggest that the appropriate similarity score can differ between target systems. For an actin filament image, the cosine similarity apparently worked best. For an image of the flagellar protein FlhAC, we found the correlation coefficient gave better results. This difference may partly be attributed to the flexibility in the target molecule, ignored in the rigid-body fitting. The inferred tip shape and placement results can be further refined by other methods, such as the flexible fitting molecular dynamics simulations. The developed software is publicly available.
format	article
author	Toru Niina Yasuhiro Matsunaga Shoji Takada
author_facet	Toru Niina Yasuhiro Matsunaga Shoji Takada
author_sort	Toru Niina
title	Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
title_short	Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
title_full	Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
title_fullStr	Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
title_full_unstemmed	Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
title_sort	rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c1bf236f10b544b0afa04f3ccd3d9337
work_keys_str_mv	AT toruniina rigidbodyfittingtoatomicforcemicroscopyimagesforinferringprobeshapeandbiomolecularstructure AT yasuhiromatsunaga rigidbodyfittingtoatomicforcemicroscopyimagesforinferringprobeshapeandbiomolecularstructure AT shojitakada rigidbodyfittingtoatomicforcemicroscopyimagesforinferringprobeshapeandbiomolecularstructure
_version_	1718375865559023616

Rigid-body fitting to atomic force microscopy images for inferring probe shape and biomolecular structure.

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