Co-evolution of interacting proteins through non-contacting and non-specific mutations

Abstract

Summary Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting, but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.

Protein design using structure-based residue preferences

“…We next asked whether site-wise preferences of these mutated binding residues can be changed by contacting residues. To do so, we examined the combinatorial variant effects in the three position (antitoxin residues D61, K64 and E80) library measured for neutralization of ten different single amino acid substitutions in the ParE3 toxin (data from Ding et al 19 ). We fit separate nonlinear, independent models to the antitoxin library in each toxin variant background.…”

“…co-expressed with the toxin ParE3 in Escherichia coli, only cells containing functional ParD3 are able to neutralize the toxin and proliferate. The in vivo function of thousands of antitoxin variants can be read out in bulk by following the variant frequencies over time using high-throughput sequencing [19][20][21] . Here, we measure random combinatorial variant effects at ten positions of the antitoxin ParD3 (Fig.…”

Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants

“…Mutual information (MI) is a classic measurement to quantify the dependence between two random variables [ 50 ]. In protein co-evolution studies, it has been proved as the most effective computational principle widely used to design tools for mining co-evolution scenarios from the large corpus of protein data [ 5 , 7 ]. The original design of MI considers phylogenetic relationships, whereas a corrected version was developed with the absence of phylogenetic information [ 39 ].…”

“…As reported in bacteria and mammals, various known proteins exhibit highly conserved interactive relationships [ 3 , 4 ]. In addition, due to natural selection pressure during evolution, the conservation of such interactions is retained due to non-contacting mutations [ 5 ]. From a structural point of view, the binding conformation characteristics of interactive interfaces remain untackled because only a few coupling events have been captured using co-crystallography technology.…”

“…Proteins not only interact with each other, but they also co-evolve under suitable natural selection pressure [ 5 , 6 , 7 , 8 ]. Among all the amino acid compositions, some residues exhibit significant and similar co-varying patterns [ 4 , 9 ] within single proteins and between individual proteins.…”