Amino-acid network clique analysis of protein mutation correlation effects: a case study of lysozme

TL;DR

This paper introduces a topology-based network model using amino-acid clique analysis to understand mutation correlations in enzymes, aiding in protein design by distinguishing additive from non-additive mutations.

Contribution

The study presents a novel network topology model based on amino-acid k-cliques to quantify mutation correlations in enzymes, advancing understanding of mutation effects.

Findings

Clique-based model distinguishes correlated from non-correlated mutations

Mutation effects depend on amino-acid network topology

Third mutations can significantly alter mutation correlations

Abstract

Optimizing amino-acid mutations has been a most challenging task in modern bio- industrial enzyme designing. It is well known that many successful designs often hinge on extensive correlations among mutations at different sites within the enzyme, however, the underpinning mechanism for these correlations is far from clear. Here, we present a topology-based model to quantitively characterize correlation effects between mutations. The method is based on the molecular dynamic simulations and the amino-acid network clique analysis that simply examines if two single mutation sites belong to some 3-clique. We analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based model successfully distinguishes highly correlated or non-additive double-site mutations from those with less correlation or additive mutations. We also applied the model to the protein Eglin c whose topology is significantly distinct from that of T4 phage lysozyme, and found that the model can, to some extension, still identify non-additive mutations from additive ones. Our calculations showed that mutation correlation effects may heavily depend on topology relationship among mutation sites, which can be quantitatively characterized using amino-acid network k-cliques. We also showed that double-site mutation correlations can be significantly altered by exerting a third mutation, indicating that more detailed physico-chemistry interactions might be considered with the network model for better understanding of the elusive mutation-correlation principle.