A Hierarchical Approach to Protein Molecular Evolution

TL;DR

This paper introduces a hierarchical method for exploring protein sequence space, demonstrating its effectiveness through simulations that show potential for generating new protein folds and understanding molecular evolution.

Contribution

The paper presents a novel hierarchical approach to protein evolution, combining structure and sequence analysis, with simulation results showing significant improvements in protein design and evolution modeling.

Findings

Non-homologous structure swapping enhances protein binding affinity.

Hierarchical search accelerates the discovery of new protein folds.

Simulations quantify the evolutionary potential of the approach.

Abstract

Biological diversity has evolved despite the essentially infinite complexity of protein sequence space. We present a hierarchical approach to the efficient searching of this space and quantify the evolutionary potential of our approach with Monte Carlo simulations. These simulations demonstrate that non-homologous juxtaposition of encoded structure is the rate-limiting step in the production of new tertiary protein folds. Non-homologous ``swapping'' of low energy secondary structures increased the binding constant of a simulated protein by $\approx10^7$ relative to base substitution alone. Applications of our approach include the generation of new protein folds and modeling the molecular evolution of disease.