ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay.

Abstract

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.