Auditory Analytics for Pattern Discovery in Protein Folding Dynamics

ABSTRACT We introduce Auditory Analytics, a methodological framework that utilizes data sonification for scientific discovery. Auditory Analytics describes a cycle of collecting and deriving datasets, mapping data to audible signals (sonification), analytical listening, hypothesis formulation, and tool building, where human insights from any stage of the cycle can feed back into further iterations of the cycle in the form of new datasets, alternative mappings, and new models of the original phenomenon. In Auditory Analytics, the remarkable capacity of the human auditory system to extract meaningful information from complex soundscapes across multiple timescales is repurposed for exploring, interpreting, and analyzing data. As an illustration of how Auditory Analytics can be used to uncover relationships and dynamics in physical systems, we describe an earlier study in which we applied this methodology to investigate state transition passages in a molecular dynamics simulation of a small protein. Auditory Analytics helped us identify distinct hydrogen‐bonding patterns associated with different rates of transit between folded and unfolded states, leading to a deeper understanding of the process of protein folding. A single, isolated data mapping—whether visual, auditory, haptic, mathematical, or verbal—provides an incomplete picture of reality; by adding the Auditory Analytics cycle to our portfolio of data interpretation tools, we can build a more complete picture of physical phenomena.