Binding Affinity Ranking at the Molecular Initiating Event (BARMIE): an open- source computational pipeline for ecological hazard ranking of endocrine disrupting chemicals

Abstract

One of the key challenges in ecological risk assessment lies in identifying the chemicals that pose the greatest threat and determining the species that are most vulnerable to their effects. Computational prediction of protein binding affinity can help in assessing the risk of chemicals to species. In this study we developed and validated an open-source tool called BARMIE (Binding Affinity Ranking at the Molecular Initiating Event) to rank chemical hazards and identify species that are most susceptible based on the binding affinity of the chemical to steroid receptor proteins. As an exemplar of BARMIE’s output we focus on 163 teleost fish glucocorticoid receptors (GRs) and the natural ligand cortisol and 10 synthetic glucocorticoid (GCs) drugs and five other potential chemical GR agonists. The hazard ranking is based on the likelihood that the chemicals with the highest binding affinity are likely to outcompete cortisol at the receptor binding site. In this analysis, halcinonide, a GC, was predicted to be the most hazardous based on its binding affinity and the superorder Protacanthopterygii species, including the Esociformes and Salmoniformes, were identified as the most vulnerable. This computational pipeline can be expanded to evaluate more chemicals, species, and proteins as part of an in silico chemical hazard assessment tool.

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ORCID for Nic Bury: orcid.org/0000-0001-6048-6338

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