Literature DB >> 11310543

Semi-automatized processing of AFM force-spectroscopy data.

C Gergely1, B Senger, J C Voegel, J K Hörber, P Schaaf, J Hemmerlé.   

Abstract

Atomic force microscopy operated in the force-spectroscopy mode is now a widespread technique, often used to investigate ligand-receptor interactions with the goal of measuring forces at the individual molecule level. However, in an experiment, the simultaneous interaction of several ligand/receptor pairs cannot be excluded. This may produce complicated force curves, although unambiguous ruptures are sometimes observed. In the case of the non-specific adhesion of molecules, such as fibrinogen, to a surface, it is usually difficult to identify the real events on the force curves. This can render the application of fixed rules uneasy and in addition can introduce some degree of arbitrariness if the analysis has to be performed by hand. In the present paper a computer algorithm, aimed at speeding up the processing, and at applying selection rules in a reproducible manner, is proposed. It is applied to force recordings performed at various retraction velocities, thus various loading rates. The influence on the evaluation of the rupture forces of the different parameters that can be set by the operator is discussed.

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Year:  2001        PMID: 11310543     DOI: 10.1016/s0304-3991(00)00063-2

Source DB:  PubMed          Journal:  Ultramicroscopy        ISSN: 0304-3991            Impact factor:   2.689


  7 in total

1.  Multi-bead-and-spring model to interpret protein detachment studied by AFM force spectroscopy.

Authors:  Csilla Gergely; Joseph Hemmerlé; Pierre Schaaf; J K Heinrich Hörber; Jean-Claude Voegel; Bernard Senger
Journal:  Biophys J       Date:  2002-08       Impact factor: 4.033

2.  FEATHER: Automated Analysis of Force Spectroscopy Unbinding and Unfolding Data via a Bayesian Algorithm.

Authors:  Patrick R Heenan; Thomas T Perkins
Journal:  Biophys J       Date:  2018-08-07       Impact factor: 4.033

3.  Automated AFM force curve analysis for determining elastic modulus of biomaterials and biological samples.

Authors:  Yow-Ren Chang; Vijay Krishna Raghunathan; Shaun P Garland; Joshua T Morgan; Paul Russell; Christopher J Murphy
Journal:  J Mech Behav Biomed Mater       Date:  2014-06-05

4.  POTATO: Automated pipeline for batch analysis of optical tweezers data.

Authors:  Stefan Buck; Lukas Pekarek; Neva Caliskan
Journal:  Biophys J       Date:  2022-06-30       Impact factor: 3.699

5.  Micromanipulation and Automatic Data Analysis to Determine the Mechanical Strength of Microparticles.

Authors:  Zhihua Zhang; Yanping He; Zhibing Zhang
Journal:  Micromachines (Basel)       Date:  2022-05-10       Impact factor: 3.523

6.  Force measurement enabling precise analysis by dynamic force spectroscopy.

Authors:  Atsushi Taninaka; Yuuichi Hirano; Osamu Takeuchi; Hidemi Shigekawa
Journal:  Int J Mol Sci       Date:  2011-12-29       Impact factor: 5.923

7.  Fast automated processing of AFM PeakForce curves to evaluate spatially resolved Young modulus and stiffness of turgescent cells.

Authors:  Marc Offroy; Angelina Razafitianamaharavo; Audrey Beaussart; Christophe Pagnout; Jérôme F L Duval
Journal:  RSC Adv       Date:  2020-05-20       Impact factor: 4.036

  7 in total

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