Proteins across scales through graph partitioning: application to the major peanut allergen Ara h 1

The analysis of community structure in complex networks has been given much attention recently, as it is hoped that the communities at various scales can affect or explain the global behaviour of the system. A plethora of community detection algorithms have been proposed, insightful yet often restricted by certain inherent resolutions. Proteins are multi-scale biomolecular machines with coupled structural organization across scales, which is linked to their function. To reveal this organization, we applied a recently developed multi-resolution method, Markov Stability, which is based on atomistic graph partitioning, along with theoretical mutagenesis that further allows for hot spot identification using Gaussian process regression. The methodology finds partitions of a graph without imposing a particular scale a priori and analyses the network in a computationally efficient way. Here, we show an application on peanut allergenicity, which despite extensive experimental studies that focus on epitopes, groups of atoms associated with allergenic reactions, remains poorly understood. We compare our results against available experiment data, and we further predict distal regulatory sites that may significantly alter protein dynamics.