# Comparative Dynamics Enables Discovery of Embedded Bacterial Ferredoxin Domains in Large Redox Enzymes

## TL;DR

Researchers used protein dynamics to find ancient bacterial ferredoxin domains inside larger enzymes, suggesting a new way to study protein evolution.

## Contribution

The study introduces comparative dynamics as a novel method to detect embedded ferredoxin domains with limited structural or sequence homology.

## Key findings

- Fragments of bacterial ferredoxin domains were identified in larger enzymes using comparative dynamics.
- Dynamical similarity reveals functional relationships even when structural or sequence homology is limited.
- Protein dynamics are more evolutionarily constrained than structure, suggesting functional conservation.

## Abstract

Bacterial ferredoxins are small iron–sulfur binding proteins that function as soluble electron shuttles between redox enzymes in the cell. Their simple 2×(β–α–β) fold, central metabolic function, and ubiquity across all kingdoms of life have led to the proposal that ferredoxins were likely among the earliest proteins. Today, ferredoxin‐like folds are embedded in large, multidomain enzymes, suggesting ancient gene duplication and fusion events. In some cases, these embedded domains may have scant sequence or even structural homology to soluble counterparts, challenging the use of traditional phylogenetic tools to establish evolutionary relationships. In this study, we identify fragments of bacterial ferredoxins within larger oxidoreductases by integrating comparative sequence, structure, and dynamical attributes. Dynamics are computed using an elastic network model and analyzed for similarity of major normal modes. Using comparative dynamics, fragments of ferredoxin domains are found within larger proteins, even in cases of limited structural homology. This study also reveals a non‐linear relationship between dynamical and structural similarities, suggesting that protein dynamics are more constrained than structure through evolutionary time. We propose that dynamical similarity is indicative of functional similarity, and since nature selects for function, that the inclusion of dynamical similarity, in addition to sequence and structure similarities, provides a more robust framework for inferring homology. Inclusion of dynamical attributes in comparative analysis will lead to a greater understanding of the deep‐time evolution of modern protein nanomachines.