Top-down machine learning approach for high-throughput single-molecule analysis
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
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Data Associated with Top-Down Machine Learning for High-Throughput Single-Molecule AnalysisZenodo, doi:10.5281/zenodo.3727917.
Article and author information
Author details
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
- Leon D Islas, Universidad Nacional Autónoma de México, Mexico
Version history
- Received: November 6, 2019
- Accepted: April 8, 2020
- Accepted Manuscript published: April 8, 2020 (version 1)
- 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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