Bayesian Inference of Bond Parameters from Interactions between Single Filaments

We map interaction forces between two vimentin filaments (cytoskeletal components crucial for cell mechanics) using optical tweezers, while controlling the relative velocity. We introduce a powerful Bayesian inference framework to learn bond parameters directly from force trajectories. The information gained about the bond parameters is maximized by an optimal relative velocity and further by distributing measurements across multiple velocities. Our Bayesian framework is broadly applicable to a large range of biomolecular interactions and force spectroscopy techniques.