ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay
The In Vitro Motility Assay (IVMA) is a widely used experimental system to study the chemical and mechanical activity of myosin and other cytoskeletal motor proteins. In the IVMA, myosin molecules are bound to a glass surface and propel fluorescently labeled actin filaments across the surface, which...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Biophysical Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667074725000266 |
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| author | Sebastian Duno-Miranda David M. Warshaw Shane R. Nelson |
| author_facet | Sebastian Duno-Miranda David M. Warshaw Shane R. Nelson |
| author_sort | Sebastian Duno-Miranda |
| collection | DOAJ |
| description | The In Vitro Motility Assay (IVMA) is a widely used experimental system to study the chemical and mechanical activity of myosin and other cytoskeletal motor proteins. In the IVMA, myosin molecules are bound to a glass surface and propel fluorescently labeled actin filaments across the surface, which are recorded using video fluorescence microscopy. The length and velocity of the actin filaments offer a measurement of the chemomechanical activity of the myosin motor proteins. Although the assay itself is well suited for high-throughput application, current video analysis approaches are slow, labor intensive, and subject to human bias. To address this shortfall, we introduce ATLAS, an open-source, platform independent software package that utilizes state-of-the-art machine learning algorithms to identify fluorescently labeled actin filaments and then track and analyze their motion in the IVMA. Utilizing both experimental data and a large array of simulated actomyosin motility movies, we demonstrate that ATLAS accurately and efficiently measures both the velocity and length of actin filaments across a broad range of experimental conditions. |
| format | Article |
| id | doaj-art-e49d9b8fcb134f919f8bdeda8d2e117d |
| institution | DOAJ |
| issn | 2667-0747 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biophysical Reports |
| spelling | doaj-art-e49d9b8fcb134f919f8bdeda8d2e117d2025-08-20T03:17:38ZengElsevierBiophysical Reports2667-07472025-09-015310022110.1016/j.bpr.2025.100221ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility AssaySebastian Duno-Miranda0David M. Warshaw1Shane R. Nelson2Department of Molecular Physiology and Biophysics, Cardiovascular Research Institute, University of Vermont, Burlington, Vermont; Corresponding authorDepartment of Molecular Physiology and Biophysics, Cardiovascular Research Institute, University of Vermont, Burlington, VermontDepartment of Molecular Physiology and Biophysics, Cardiovascular Research Institute, University of Vermont, Burlington, Vermont; Corresponding authorThe In Vitro Motility Assay (IVMA) is a widely used experimental system to study the chemical and mechanical activity of myosin and other cytoskeletal motor proteins. In the IVMA, myosin molecules are bound to a glass surface and propel fluorescently labeled actin filaments across the surface, which are recorded using video fluorescence microscopy. The length and velocity of the actin filaments offer a measurement of the chemomechanical activity of the myosin motor proteins. Although the assay itself is well suited for high-throughput application, current video analysis approaches are slow, labor intensive, and subject to human bias. To address this shortfall, we introduce ATLAS, an open-source, platform independent software package that utilizes state-of-the-art machine learning algorithms to identify fluorescently labeled actin filaments and then track and analyze their motion in the IVMA. Utilizing both experimental data and a large array of simulated actomyosin motility movies, we demonstrate that ATLAS accurately and efficiently measures both the velocity and length of actin filaments across a broad range of experimental conditions.http://www.sciencedirect.com/science/article/pii/S2667074725000266 |
| spellingShingle | Sebastian Duno-Miranda David M. Warshaw Shane R. Nelson ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay Biophysical Reports |
| title | ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay |
| title_full | ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay |
| title_fullStr | ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay |
| title_full_unstemmed | ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay |
| title_short | ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay |
| title_sort | atlas machine learning enhanced filament analysis for the in vitro motility assay |
| url | http://www.sciencedirect.com/science/article/pii/S2667074725000266 |
| work_keys_str_mv | AT sebastiandunomiranda atlasmachinelearningenhancedfilamentanalysisfortheinvitromotilityassay AT davidmwarshaw atlasmachinelearningenhancedfilamentanalysisfortheinvitromotilityassay AT shanernelson atlasmachinelearningenhancedfilamentanalysisfortheinvitromotilityassay |