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91探花
CMP
Credit: Jack Hobhouse

Professor Achillefs Kapanidis

Professor of Biological Physics

Research theme

  • Biological physics

Sub department

  • Condensed Matter Physics

Research groups

  • Gene machines
Achillefs.Kapanidis@physics.ox.ac.uk
Telephone: 01865 (2)72226
Biochemistry Building
  • About
  • Publications

Cover Feature: Bleaching鈥恟esistant, Near鈥恈ontinuous Single鈥恗olecule Fluorescence and FRET Based on Fluorogenic and Transient DNA Binding (ChemPhysChem 12/2023)

ChemPhysChem Wiley 24:12 (2023)

Authors:

Mirjam K眉mmerlin, Abhishek Mazumder, Achillefs N Kapanidis

Bleaching-resistant,near-continuous single-molecule fluorescence and fret based on fluorogenic and transient DNA binding

ChemPhysChem Wiley 24:12 (2023) e202300175

Authors:

Mirjam K眉mmerlin, Abhishek Mazumder, Achillefs N Kapanidis

Abstract:

Graphical Abstract
A general strategy to circumvent photobleaching by replenishing fluorescent probes via transient binding of fluorogenic DNAs to complementary DNA strands attached to a target molecule is presented. Using two orthogonal sequences, the authors show that their method is adaptable to F枚rster resonance energy transfer (FRET) and can be used to continuously study the conformational transitions of dynamic structures for extended periods (>1鈥卙谤).

Abstract
Photobleaching of fluorescent probes limits the observation span of typical single-molecule fluorescence measurements and hinders observation of dynamics at long timescales. Here, we present a general strategy to circumvent photobleaching by replenishing fluorescent probes via transient binding of fluorogenic DNAs to complementary DNA strands attached to a target molecule. Our strategy allows observation of near-continuous single-molecule fluorescence for more than an hour, a timescale two orders of magnitude longer than the typical photobleaching time of single fluorophores under our conditions. Using two orthogonal sequences, we show that our method is adaptable to F枚rster Resonance Energy Transfer (FRET) and that can be used to study the conformational dynamics of dynamic structures, such as DNA Holliday junctions, for extended periods. By adjusting the temporal resolution and observation span, our approach enables capturing the conformational dynamics of proteins and nucleic acids over a wide range of timescales.

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

Nature Methods Springer Nature 20:4 (2023) 523-535

Authors:

Ganesh Agam, Christian Gebhardt, Milana Popara, Rebecca M盲chtel, Julian Folz, Benjamin Ambrose, Neharika Chamachi, Sang Yoon Chung, Timothy D Craggs, Marijn de Boer, Dina Grohmann, Taekjip Ha, Andreas Hartmann, Jelle Hendrix, Verena Hirschfeld, Christian G H眉bner, Thorsten Hugel, Dominik Kammerer, Hyun-Seo Kang, Achillefs N Kapanidis, Georg Krainer, Kevin Kramm, Edward A Lemke, Eitan Lerner, Emmanuel Margeat, Kirsten Martens, Jens Michaelis, Jaba Mitra, Gabriel G Moya Mu帽oz, Robert B Quast, Nicole C Robb, Michael Sattler, Michael Schlierf, Jonathan Schneider, Tim Schr枚der, Anna Sefer, Piau Siong Tan, Johann Thurn, Philip Tinnefeld, John van Noort, Shimon Weiss, Nicolas Wendler, Niels Zijlstra, Anders Barth, Claus AM Seidel, Don C Lamb, Thorben Cordes

A new twist on PIFE: photoisomerisation-related fluorescence enhancement

(2023)

Authors:

Evelyn Ploetz, Benjamin Ambrose, Anders Barth, Richard B枚rner, Felix Erichson, Achillefs N Kapanidis, Harold D Kim, Marcia Levitus, Timothy M Lohman, Abhishek Mazumder, David S Rueda, Fabio D Steffen, Thorben Cordes, Steven W Magennis, Eitan Lerner

Deep Antimicrobial Susceptibility Phenotyping (DASP) Training and Evaluation Dataset, and Trained Models.

91探花 (2023)

Authors:

Aleksander Zagajewski, Piers Turner, Conor Feehily, Nicole Stoesser, Christoffer Nellaker, Achillefs Kapanidis

Abstract:

Dataset of microscopy images of untreated and treated E.coli lab strains and clinical isolates, and machine learning models trained on them. Corresponding publications: https://doi.org/10.1101/2022.12.08.22283219 Corresponding analysis code: https://github.com/KapanidisLab/Deep-Learning-and-Single-Cell-Phenotyping-for-Rapid-Antimicrobial-Susceptibility-Testing

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