---
title: "Binding Affinity Ranking at the Molecular Initiating Event (BARMIE): An open-source computational pipeline for the rapid screening of chemical interactions with steroid receptors from many species"
id: "plos-one-15-binding-affinity-ranking-at-the-molecular-initiating-event-barmie-an-open"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-15-binding-affinity-ranking-at-the-molecular-initiating-event-barmie-an-open"
content_type: "clinical_feed_article"
specialty: "Research Highlights"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0353622"
published_at: "2026-07-15T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Binding Affinity Ranking at the Molecular Initiating Event (BARMIE): An open-source computational pipeline for the rapid screening of chemical interactions with steroid receptors from many species
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-15-binding-affinity-ranking-at-the-molecular-initiating-event-barmie-an-open
- **Specialty:** [Research Highlights](https://medichelpline.com/clinical-feed/research-highlights.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0353622)
- **Published At:** 2026-07-15T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
by Fernando Calahorro, Parsa Fouladi, Alessandro Pandini, Matloob Khushi, Yogendra Gaihre, Nic R. Bury A challenge in ecological risk assessment is identifying the chemicals that pose the greatest threat and determining which species are most vulnerable to them. To help address this, this study has developed an in-silico open-source tool called BARMIE (Binding Affinity Ranking at the Molecular Initiating Event) to rapidly predict the chemical binding affinity of steroid receptor proteins to synthetic steroids to identify potentially vulnerable species and chemicals of concern. BARMIE was used to screen 163 teleost fish glucocorticoid receptors (GRs) for binding to the natural ligand cortisol and to 10 synthetic glucocorticoid drugs (GCs) designed to interact within the ligand-binding pocket (LBP) of GRs. BARMIE identified species from the superorder Protacanthopterygii with high-affinity GRs to synthetic GCs (e.g., vulnerable species).. BARMIE was also used to screen binding profiles of compounds in the Medicine for Malaria Venture Global Health Priority Box to rainbow trout GRs (rtGR1 and rtGR2). Of the 178 compounds, 24 and 36 bind within the LBP of rtGR1 and rtGR2, respectively.
## Clinical Analysis & Structured Key Points
# BARMIE: An open-source pipeline for rapid in‑silico screening of chemical binding to steroid receptors across species Introduction: Environmental risk assessment faces a major bottleneck: too many synthetic chemicals, too few biologically relevant assays. The authors present BARMIE (Binding Affinity Ranking at the Molecular Initiating Event), an open-source computational workflow designed to rapidly predict ligand binding to steroid receptors across many species. The tool aims to prioritize species and chemicals for follow-up testing by estimating receptor–ligand binding affinities at the molecular initiating event. GEO Definition Block: BARMIE is an automated, open-source docking pipeline that aligns predicted receptor structures, docks compounds into a defined ligand-binding pocket, and reports averaged binding affinities to rank chemicals and identify potentially vulnerable species based on receptor interaction profiles. The code and instructions are available on GitHub. Background: - Rationale: Tens to hundreds of thousands of synthetic chemicals exist with sparse ecological hazard data. Traditional toxicity testing is resource-intensive and raises ethical concerns with vertebrate models. New Approach Methodologies (NAMs), including in silico screening, are being developed to triage chemicals and species for ecological risk assessment. - Biological context: Vertebrate steroid receptors (glucocorticoid, mineralocorticoid, androgen, estrogen, progesterone) are conserved and mediate key physiological processes via ligand binding and downstream transcriptional responses. Differences in receptor sequence and structure across species can influence binding affinity, and thus susceptibility to endocrine-active compounds. - Prior work and positioning: Other computational tools (for example, sequence conservation platforms and network-based approaches) identify potential non-target species by homology to human drug targets and related pathways. BARMIE targets the structural interaction step—the molecular initiating event—by estimating receptor–ligand binding affinity across many receptor variants to augment species-sensitivity predictions. Clinical Methodology: - Pipeline components: BARMIE integrates open-source resources and software including UniProt (for receptor sequences/AlphaFold-derived structures), ChEMBL (for ligands), OpenBabel, PyMOL, and AutoDock Vina. The repository with code and installation guidance is publicly available, and a training video is provided by the authors. - Receptor structure sourcing and handling: Receptor structures are obtained from AlphaFold models linked via UniProt annotations derived from Ensembl genomes. The pipeline retains annotated isoforms, recognizing that many fish splice variants lack experimental characterization. - Alignment and docking region standardization: Because AlphaFold-derived structures can possess differing spatial orientations, BARMIE performs an automated alignment step to position ligand-binding pockets (LBPs) consistently across proteins. A single reference structure—rainbow trout GR1 in this demonstration—defines the docking box coordinates. Users must set box dimensions and coordinates once per reference; for the glucocorticoid receptor (GR) examples, a 20 × 20 × 20 Å box centered at specified X, Y, Z values was used. - Docking protocol and repeatability: AutoDock Vina searches are stochastic; the authors ran five replicates per receptor–ligand pair and averaged binding energies. Three levels of exhaustiveness (8, 32, 128) were tested; results were consistent across exhaustiveness settings, and results from exhaustiveness 32 were reported. - Validation approach: Predicted binding energies from BARMIE were compared to previously published empirical data including dexamethasone binding affinities (Kd) and cortisol/dexamethasone EC50s from rainbow trout GR1/GR2 (including chimeras and mutants), Pantodon buchholzi, Acipenser ruthenus, and Cyprinus carpio. Empirical Kd values were converted to Gibbs free energy (ΔG) at room temperature for comparison with predicted docking affinities, and linear regression analyses were performed. - Demonstration screens: Two demonstration applications were performed: 1. Screening of 163 teleost fish GR proteins against the endogenous ligand cortisol and ten synthetic glucocorticoids (including dexamethasone, triamcinolone, halcinonide, and others). 2. Screening of 178 compounds from the Medicines for Malaria Venture Global Health Priority Box (GHPB) against rainbow trout GR1 and GR2 to identify non‑steroidal compounds that dock within the LBP. A threshold of −7.5 kcal/mol was used to flag candidate binders based on an observed average for a non‑steroidal selective glucocorticoid receptor agonist. - Experimental follow-up for the GHPB subset: For a subset of predicted binders, the authors performed in vitro transactivation assays in COS-7 cells expressing rainbow trout GR1 or GR2. Cells were transfected with reporter constructs, exposed to compounds at 1 μM alone or combined with 1 μM cortisol, and luciferase activity was measured and normalized to control. Experiments were run in duplicate wells with three independent repeats for most compounds; statistical comparisons used one-way ANOVA with Dunnett’s post hoc test. Key Findings: - Correlation with empirical data: Predicted binding affinities from BARMIE correlated with published dexamethasone binding data and EC50 values for dexamethasone and cortisol across the tested species and receptor variants. Reported linear models showed statistically significant relationships (for example, R2 values cited in the source), indicating BARMIE predictions relate to experimental measures of receptor activation and ligand binding. - Species and ligand sensitivity patterns: Screening 163 teleost GRs revealed variability in predicted affinities across species and ligands. Among the synthetic glucocorticoids tested, halcinonide yielded among the most favorable predicted interactions, while prednicarbate ranked among the least favorable. Receptors from several members of the Protacanthopterygii (including Esox lucius, Salmo trutta, Coregonus sp.) were repeatedly predicted to have high-affinity interactions with multiple synthetic glucocorticoids; Northern pike was frequently ranked among the top predicted susceptibilities for several compounds. - GHPB screen outcomes and in vitro validation: Of 178 GHPB compounds screened against rainbow trout GR1 and GR2, a subset was predicted to dock within the LBP with binding energies at or above the chosen threshold. Visual inspection confirmed binding within the pocket for selected compounds. Subsequent transactivation assays on 30 of these compounds assessed agonist or antagonist activity at 1 μM and identified two compounds exhibiting agonistic properties in this assay context. Practical Implications: - Utility as a first-tier screen: BARMIE can rapidly process hundreds of receptor sequences and compounds to generate comparative binding-affinity rankings. This capacity supports prioritization of species and chemicals for targeted experimental testing, consistent with NAM-driven strategies that seek to reduce workload and focus resources. - Integration into broader ERA pipelines: Structural binding predictions at the molecular initiating event provide an orthogonal evidence stream to sequence conservation and pathway-based approaches. Combining BARMIE outputs with exposure, pharmacokinetic, and higher-level organismal data could improve prioritization accuracy. - Limitations and scope: The authors emphasize empirical data for fish GR ligand binding and functional activation remain limited, particularly for non‑glucocorticoid chemicals. The representativeness of available genomic resources is uneven—only a small fraction of teleost diversity has annotated genomes, and certain clades may be overrepresented—so apparent species sensitivity patterns may reflect dataset composition. BARMIE predicts receptor–ligand affinity but does not alone predict organismal effects; follow-up in vitro and in vivo studies are required to establish adverse outcomes. Key Takeaways: - BARMIE is an open-source docking pipeline that aligns predicted receptor structures and reports averaged binding affinities to rank chemical–receptor interactions. - Predicted affinities from BARMIE correlated with published binding and transactivation data across several fish species and receptor variants. - In demonstrations, BARMIE screened 163 teleost GRs against endogenous and synthetic glucocorticoids and 178 GHPB compounds against rainbow trout GRs, identifying candidate vulnerable species and potential chemical binders. - The pipeline is designed as a rapid first-pass tool to prioritize chemicals and species for further experimental hazard and risk assessment work. FAQ: Q1: What receptors and species can BARMIE handle? A1: The pipeline uses AlphaFold-derived receptor structures accessed via UniProt and can be applied to any species with an annotated protein model. The published demonstration focused on teleost fish glucocorticoid receptors, but the workflow is adaptable to other proteins and taxa provided structural models are available. Q2: How does BARMIE address variability in docking results? A2: BARMIE runs multiple stochastic docking replicates and reports averaged binding affinities. The authors also tested different AutoDock Vina exhaustiveness settings and found consistent results across tested levels, selecting an intermediate exhaustiveness for routine use. Q3: Does a predicted high binding affinity equate to environmental harm? A3: No. Predicted receptor binding identifies molecular-level interaction potential but does not by itself establish organismal toxicity or ecological risk. The authors used BARMIE to prioritize compounds for in vitro functional assays; empirical testing and exposure context are necessary to evaluate hazard. Q4: Where can users access the software and guidance? A4: The authors made the code, installation instructions, and a training video publicly available via a GitHub repository and supporting resources referenced in the article. Conclusion: BARMIE provides an automated, reproducible approach to estimate receptor–ligand interactions across many sequences using open-source structural models and docking tools. In the demonstrations presented, predicted binding affinities aligned with available empirical binding and activity data, and the pipeline flagged specific species and compounds for follow-up. The method is intended as an early‑stage triage tool within ecological risk assessment workflows. Important caveats include limited empirical datasets for many receptors and uneven genomic representation among taxa; therefore, BARMIE outputs should be interpreted as prioritization signals requiring experimental validation.
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