RESUMO
In vitro motility (IVM) assays allow for the examination of the basic interaction between cytoskeletal filaments with molecular motors and the influence many physiological factors have on this interaction. Examples of factors that can be studied include changes in ADP and pH that emulate fatigue, altered phosphorylation that can occur with disease, and mutations within myofilament proteins that cause disease. While IVM assays can be analyzed manually, the main limitation is the ability to extract accurate data rapidly from videos collected without individual bias. While programs have been created in the past to enable data extraction, many are now out of date or require the use of proprietary software. Here, we report the generation of a Python-based tracking program, Philament, which automatically extracts data on instantaneous and average velocities, and allows for fully automated analysis of IVM recordings. The data generated are presented in an easily accessible spreadsheet-based, comma-separated values file. Philament also contains a novel method of quantifying the smoothness of filament motion. By fitting curves to standard deviations of velocity and average velocities, the influence of different experimental conditions can be compared relative to one another. This comparison provides a qualitative measure of protein interactions where steeper slopes indicate more unstable interactions and shallower slopes indicate more stable interactions within the myofilament. Overall, Philament's automation of IVM analysis provides easier entry into the field of cardiovascular mechanics and enables users to create a truly high-throughput experimental data analysis.