TopoStats – a program for automated tracing of biomolecules from AFM images

Beton, Joseph G.; Moorehead, Robert; Helfmann, Luzie; Gray, Robert; Hoogenboom, Bart W.; Joseph, Agnel Praveen; Topf, Maya; Pyne, Alice L. B.

doi:10.1101/2020.09.23.309609

Cited by 3 publications

(5 citation statements)

References 34 publications

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“…The methods used for automated processing and tracing of DNA are described fully here 59 , with the code available at (https://github.com/AFM-SPM/TopoStats) 60 . Here AFM images were processed using a userdesigned Python script (pygwytracing.py), which utilises the Gwyddion 'pygwy' module 45 for automated image correction, DNA molecule identification and morphological analysis.…”

Section: Afm Image Processingmentioning

confidence: 99%

Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and major groove recognition by triplex-forming oligonucleotides

Pyne

Noy

Main

et al. 2019

Preprint

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In the cell, DNA is arranged into highly-organised and topologically-constrained (supercoiled) structures.It remains unclear how this supercoiling affects the double-helical structure of DNA, largely because of limitations in spatial resolution of the available biophysical tools. Here, we overcome these limitations by a combination of atomic force microscopy (AFM) and atomistic molecular dynamics (MD) simulations, to resolve structures of negatively-supercoiled DNA minicircles at base-pair resolution. We observe that negative superhelical stress induces local variation in the canonical B-form DNA structure by introducing kinks and defects that affect global minicircle structure and flexibility. We probe how these local and global conformational changes affect DNA interactions through the binding of triplex-forming oligonucleotides to DNA minicircles. We show that the energetics of triplex formation is governed by a delicate balance 2 between electrostatics and bonding interactions. Our results provide mechanistic insight into how DNA supercoiling can affect molecular recognition of diverse conformational substrates.

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Section: Afm Image Processingmentioning

confidence: 99%

Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and major groove recognition by triplex-forming oligonucleotides

Pyne

Noy

Main

et al. 2019

Preprint

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“…AutoSmarTrace complements a recently developed machine-learning algorithm for chain characterization, TopoStats, which was designed to determine contour length and topology of DNA (13). While both approaches were developed for analysis of AFM images, we were curious to see whether AutoSmarTrace could identify chains in images acquired with other approaches.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, it is possible that there is variation in backbone coordinates selected by different users, which could lead to biased results. For these reasons, a number of different approaches for automating chain tracing have been developed (6,(10)(11)(12)(13). Most of these involve an initial user input step to manually identify points along the chains (6,11,12); following the initial identification of points on or near the central line of the backbone, various approaches are implemented to automatically refine the backbone contour.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these involve an initial user input step to manually identify points along the chains (6, 11, 12); following the initial identification of points on or near the central line of the backbone, various approaches are implemented to automatically refine the backbone contour. Very few algorithms exist that provide fully automated chain detection and tracing (10, 13). We have found one of these algorithms (10) to be sensitive to background noise amplitude and correlation, and problematic for chains exhibiting irregular intensity along the contour (14).…”

Section: Introductionmentioning

confidence: 99%

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AutoSmarTrace: Automated Chain Tracing and Flexibility Analysis of Biological Filaments

Schneider

Al-Shaer

Forde

2021

Preprint

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Single-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed “AutoSmarTrace”, the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy (AFM). We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with the ground truth. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. While trained on AFM-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary shadowing electron microscopy and fluorescence imaging.Statement of SignificanceMachine learning presents powerful capabilities to the analysis of large data sets. Here, we apply this approach to the determination of bending flexibility – described through persistence length – from single-molecule images of biological filaments. We present AutoSmarTrace, a tool for automated tracing and analysis of chain flexibility. Built on a neural network trained via machine learning, we show that AutoSmarTrace can determine persistence lengths from AFM images of a variety of biological macromolecules including collagen and DNA. While trained on AFM-like images, the algorithm works well to identify filaments in other types of images. This technique can free researchers from tedious tracing of chains in images, removing user bias and standardizing determination of chain mechanical parameters from single-molecule conformational images.

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“…Another example includes the development of TopoStats, an open-source Python utility that allows for single molecule identification and tracing of complex heterogeneous DNA populations as well as biomimetic pores. 132 The creation of these utilities should not only facilitate and accelerate high-throughput AFM image processing and analysis but should foster community-led development toward more complex analysis.…”

Section: Other Factors Affecting Afm Image Acquisition and Analysismentioning

confidence: 99%

Atomic force microscopy—A tool for structural and translational DNA research

et al. 2021

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Atomic force microscopy (AFM) is a powerful imaging technique that allows for structural characterization of single biomolecules with nanoscale resolution. AFM has a unique capability to image biological molecules in their native states under physiological conditions without the need for labeling or averaging. DNA has been extensively imaged with AFM from early single-molecule studies of conformational diversity in plasmids, to recent examinations of intramolecular variation between groove depths within an individual DNA molecule. The ability to image dynamic biological interactions in situ has also allowed for the interaction of various proteins and therapeutic ligands with DNA to be evaluated—providing insights into structural assembly, flexibility, and movement. This review provides an overview of how innovation and optimization in AFM imaging have advanced our understanding of DNA structure, mechanics, and interactions. These include studies of the secondary and tertiary structure of DNA, including how these are affected by its interactions with proteins. The broader role of AFM as a tool in translational cancer research is also explored through its use in imaging DNA with key chemotherapeutic ligands, including those currently employed in clinical practice.

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TopoStats – a program for automated tracing of biomolecules from AFM images

Cited by 3 publications

References 34 publications

Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and major groove recognition by triplex-forming oligonucleotides

Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and major groove recognition by triplex-forming oligonucleotides

AutoSmarTrace: Automated Chain Tracing and Flexibility Analysis of Biological Filaments

Atomic force microscopy—A tool for structural and translational DNA research

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