Wave space sonification of the folding pathways of protein molecules modeled as hyper-redundant robotic mechanisms

Investigation of the folding pathways of protein molecules plays a key role in studying diseases such as Alzheimer’s and designing viral drugs at the molecular level. Despite recent advances in visualization techniques, effective sonification (i.e., non-speech auditory representation) of large datasets associated with protein folding pathways is still an open question. This paper investigates the problem of sonification of protein folding pathway datasets by using the wave space sonification (WSS) framework due to Hermann ( 2018 ). In particular, this paper utilizes the powerful WSS framework to develop a sonification methodology for the dihedral angle folding trajectories of protein molecules, which are modeled as hyper-redundant robotic mechanisms with many rigid nano-linkages. As an example, the developed sonification methodology is applied to a protein molecule backbone chain with a dihedral angle space of dimension 82, where a canonical wave space function based on a sum-of-sinusoids with conformation-dependent frequencies and a sample-based wave space function based on Mozart’s Alla Turca are utilized for sonification of the folding trajectories of this peptide chain.