Conley, E. A.; Schaefer, K. G.; Davison, H. C.; King, G.

The fundamental molecules of life are polymers. Prominent examples include nucleic acids and proteins, both of which assume a vast array of mechanical properties and three-dimensional shapes. The persistence length represents a numerical value to classify the bending rigidity of individual polymers. The shape of a polymer, dictated by the topology of the polymer backbone - a line trace through the center of the polymer along the contour path - is also a critical metric. Common architectures include linear, ring-like or cyclic, and branched; combinations of these can also exist, as in complex polymer networks. Determination of persistence length and shape are largely informative to polymer function and stability in biological environments. Here we demonstrate PS Poly, a near-fully automated algorithm to obtain polymer persistence length and shape from single molecule images obtained in physiologically relevant fluid conditions via atomic force microscopy. The algorithm, which involves image reduction via skeletonization followed by end point and branch point detection via {-}filtering, is capable of rapidly analyzing thousands of polymers with subpixel precision. Algorithm outputs were verified by analysis of deoxyribose nucleic acid, a very well characterized macromolecule. The utility of method was further demonstrated by application to a recently discovered polypeptide chain named candidalysin. This toxic protein segment polymerizes in solution and represents the first human fungal pathogen yet discovered. PS poly is a robust and general algorithm. It can be used to extract fundamental information about polymer backbone stiffness, shape, and more generally, polymerization mechanisms.