Utilizing Computational Techniques to Accelerate Discovery in Peanut Allergenicity: A Case Study

Computational tools and techniques are attractive due to the low-cost and scope of application and are becoming more prevalent and accepted across a variety of fields. With the advancement of high performance computing resources and the recent utility of GPU architecture, molecular dynamics (MD) simulations have become a nearly ubiquitous tool to interrogate protein structure-function relationships. Understanding these structure-function relationships is fundamental to protein biochemistry and facilitates avenues to modulate activity in disease states. Herein, we studied and compared the ability of several GPU- and CPU-based clusters to accelerate mechanistic and therapeutic discovery with MD simulations. MD simulations on the peanut protein Ara h 2, the causative agent of peanut allergenicity, from 10 different species of the genus Arachis were conducted in triplicate for 1 μs to probe structural dynamics and epitope solvent accessibility relevant to allergenicity. These simulations provide molecular insight into the causes and potential potency of the allergenic response. Ultimately, these computational findings and insights can guide experimental design, increasing the impact of therapeutics and understanding of disease states. Further, integrating GPUs into these parallel computing operations accelerates computations and provides researchers with tools to perform MD simulations without large CPU centers. We have thus provided an increased understanding of protein structure-function relationships through the usage of high performance computing and have shown GPU scaling power with MD simulations.