1. Structural Biology and Molecular Biophysics

Top-down machine learning approach for high-throughput single-molecule analysis

  1. David S White
  2. Marcel P Goldschen-Ohm
  3. Randall H Goldsmith  Is a corresponding author
  4. Baron Chanda  Is a corresponding author
  1. University of Wisconsin-Madison, United States
  2. University of Texas at Austin, United States
https://doi.org/10.7554/eLife.53357
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  1. David S White
  2. Marcel P Goldschen-Ohm
  3. Randall H Goldsmith
  4. Baron Chanda
(2020)
Top-down machine learning approach for high-throughput single-molecule analysis
eLife 9:e53357.
https://doi.org/10.7554/eLife.53357
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Abstract

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.

Data availability

Simulated and raw data in addition to analysis scripts are available at https://zenodo.org/record/3727917#.Xn0Fw9NKjq0DOI: 10.5281/zenodo.3727917

The following data sets were generated

Article and author information

Author details

  1. David S White

    Neuroscience, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.

  2. Marcel P Goldschen-Ohm

    Neuroscience, University of Texas at Austin, Austin, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1466-9808

  3. Randall H Goldsmith

    Chemistry, University of Wisconsin-Madison, Madison, United States
    For correspondence
    rhg@chem.wisc.edu
    Competing interests
    No competing interests declared.

  4. Baron Chanda

    Department of Neuroscience, University of Wisconsin-Madison, Madison, United States
    For correspondence
    chanda@wisc.edu
    Competing interests
    Baron Chanda, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4954-7034

Funding

National Institute of Neurological Disorders and Stroke (NS-101723)

  • Baron Chanda

National Institute of Neurological Disorders and Stroke (NS-081320)

  • Baron Chanda

National Institute of Neurological Disorders and Stroke (NS-081293)

  • Baron Chanda

National Institute of General Medical Sciences (GM007507)

  • David S White

National Institute of General Medical Sciences (GM127957)

  • Randall H Goldsmith

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Leon D Islas, Universidad Nacional Autónoma de México, Mexico

Version history

  1. Received: November 6, 2019
  2. Accepted: April 8, 2020
  3. Accepted Manuscript published: April 8, 2020 (version 1)
  4. Version of Record published: May 7, 2020 (version 2)

Copyright

© 2020, White et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. David S White
  2. Marcel P Goldschen-Ohm
  3. Randall H Goldsmith
  4. Baron Chanda
(2020)
Top-down machine learning approach for high-throughput single-molecule analysis
eLife 9:e53357.
https://doi.org/10.7554/eLife.53357

Share this article

https://doi.org/10.7554/eLife.53357

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