# Leveraging Consensus Docking Approaches for Human Mitochondrial Complexes I and III

## TL;DR

This study shows that docking methods can help predict toxicity by analyzing how compounds interact with mitochondrial complexes.

## Contribution

The study introduces consensus docking and interaction fingerprints for toxicity screening of mitochondrial complex inhibitors.

## Key findings

- Consensus scoring achieved high correlation with experimental data for CIII (Spearman r: 0.89 and 0.86).
- Interaction fingerprints captured differences between inhibitor subtypes at CIII.
- In vitro testing confirmed isomerism-dependent activity cliffs at CI.

## Abstract

Although recent progress has been made, structure-based
methods
such as molecular docking are still underexplored in the context of
toxicity prediction. These approaches offer added value, particularly
in addressing challenges such as activity cliffsi.e., caused
by stereoisomerismthat are difficult to capture by conventional
Quantitative Structure–Activity Relationship (QSAR) methods.
In this study, we investigated the ability of docking scoring functions
and protein–ligand interaction fingerprints to rank the potential
hazard of compounds targeting the human mitochondrial complexes I
and III (CI, NADH:ubiquinone oxidoreductase and CIII, cytochrome bc1 complex). We applied an induced fit docking protocol to account
for binding site flexibility and performed a set of binding energy
minimizations for rescoring of representative binding modes. Both
individual scoring functions and consensus scoring approaches achieved
acceptable rank correlation to experimentally derived data from CIII
(Spearman r: 0.89 and 0.86). Moreover, consensus
interaction fingerprints that combine molecular interactions from
both docking outputs captured differences of inhibitor subtypes at
CIII. Follow-up in vitro testing confirmed an isomerism-dependent
activity cliff of E-/Z-Fenpyroximate at CI. These findings support
the utility of using consensus docking and scoring as a screening-level
tool for prioritizing compounds based on interpretable predicted relative
binding affinities at CI and CIII.