POTATO

POTATO automates analysis of optical tweezers data for quantitative characterization of (un)folding events in force-ramp single-molecule experiments.

Key Features:

• Automated Data Processing: A Python-based pipeline that automates processing of high-frequency raw data from force-ramp optical tweezers experiments.
• Event Identification and Segmentation: Identifies (un)folding events using predefined parameters and segments force-distance trajectories at critical points.
• Curve Fitting Models: Independently fits segmented sections of force-distance curves to worm-like chain and freely-jointed chain models to extract mechanical properties.
• Work Calculation: Calculates work applied on molecules by numerical integration of force-distance data.
• Data Visualization: Plots constant-force data and fits Gaussian distance distributions over time to visualize state populations.

Scientific Applications:

• Molecular motors and protein–nucleic acid interactions: Analysis of force-ramp data relevant to studies of molecular motors and protein–nucleic acid interactions.
• Protein and RNA folding: Quantitative characterization of (un)folding events in protein and RNA folding experiments.
• High-throughput statistical analysis: Unbiased, high-throughput processing suitable for statistical analysis of large optical tweezers datasets collected under varied experimental conditions.

Methodology:

Preprocessing of high-frequency force-ramp raw data, identification and segmentation of (un)folding events using predefined parameters, independent fitting of segmented force-distance sections to worm-like chain and freely-jointed chain models, numerical integration to compute work, and plotting of constant-force distance distributions with Gaussian fits.