ArticleUltramicroscopy2014
Improving single molecule force spectroscopy through automated real-time data collection and quantification of experimental conditions.
Article in Ultramicroscopy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Unraveling the Mechanical Unfolding Pathways of a Multidomain Protein: Phosphoglycerate Kinase.Biophysical journal · 2018Article
- Ligand Binding Stabilizes Cellulosomal Cohesins as Revealed by AFM-based Single-Molecule Force Spectroscopy.Scientific reports · 2018Article
- Single-molecule Force Spectroscopy Reveals the Calcium Dependence of the Alternative Conformations in the Native State of a βγ-Crystallin Protein.The Journal of biological chemistry · 2016Article
- Modular, Nondegenerate Polyprotein Scaffolds for Atomic Force Spectroscopy.Biomacromolecules · 2016Article
- Probing fibronectin-antibody interactions using AFM force spectroscopy and lateral force microscopy.Beilstein journal of nanotechnology · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The benefits of single molecule force spectroscopy (SMFS) clearly outweigh the challenges which include small sample sizes, tedious data collection and introduction of human bias during the subjective data selection. These difficulties can be partially eliminated through automation of the experimental data collection process for atomic force microscopy (AFM). Automation can be accomplished using an algorithm that triages usable force-extension recordings quickly with positive and negative selection. We implemented an algorithm based on the windowed fast Fourier transform of force-extension traces that identifies peaks using force-extension regimes to correctly identify usable recordings from proteins composed of repeated domains. This algorithm excels as a real-time diagnostic because it involves <30 ms computational time, has high sensitivity and specificity, and efficiently detects weak unfolding events. We used the statistics provided by the automated procedure to clearly demonstrate the properties of molecular adhesion and how these properties change with differences in the cantilever tip and protein functional groups and protein age.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.