Designing bacterial signaling interactions with coevolutionary landscapes.

Ryan R Cheng,Ellinor Haglund,José N Onuchic,Joseph A Adams,Faruck Morcos,Herbert Levine,Nicholas S Tiee,Patricia A Jennings

doi:10.1371/journal.pone.0201734

Abstract

Selecting amino acids to design novel protein-protein interactions that facilitate catalysis is a daunting challenge. We propose that a computational coevolutionary landscape based on sequence analysis alone offers a major advantage over expensive, time-consuming brute-force approaches currently employed. Our coevolutionary landscape allows prediction of single amino acid substitutions that produce functional interactions between non-cognate, interspecies signaling partners. In addition, it can also predict mutations that maintain segregation of signaling pathways across species. Specifically, predictions of phosphotransfer activity between the Escherichia coli histidine kinase EnvZ to the non-cognate receiver Spo0F from Bacillus subtilis were compiled. Twelve mutations designed to enhance, suppress, or have a neutral effect on kinase phosphotransfer activity to a non-cognate partner were selected. We experimentally tested the ability of the kinase to relay phosphate to the respective designed Spo0F receiver proteins against the theoretical predictions. Our key finding is that the coevolutionary landscape theory, with limited structural data, can significantly reduce the search-space for successful prediction of single amino acid substitutions that modulate phosphotransfer between the two-component His-Asp relay partners in a predicted fashion. This combined approach offers significant improvements over large-scale mutations studies currently used for protein engineering and design.

Highlights

Designing mutations that encode new interactions between non-partner proteins is a powerful strategy for developing new approaches to problems in diverse areas in systems biology
Towards the goal of streamlining design efforts for new protein partners, we focus on the ubiquitous bacterial signal transduction systems, twocomponent signaling (TCS) systems, and the wealth of available sequence data
Candidate mutations of the RR Spo0F predicted to encode preferential interaction with the histidine kinase (HK) EnvZ were selected from a subset of mutations for which DHTCS 1⁄4 HTCSð~smutantÞ À HTCSð~sw:t:Þ < 0 (Eq 1), where ~smutant and ~sw:t: are the mutated and wild-type sequences, respectively

Summary

Introduction

Designing mutations that encode new interactions between non-partner proteins is a powerful strategy for developing new approaches to problems in diverse areas in systems biology. Moving forward, it is essential that the design of new noncognate protein-protein interactions include economical, data-driven approaches Towards this goal, we model abundant sequence data using Direct Coupling Analysis (DCA) as a guide to design interactions between enzymes and their targets that are non-cognate partners. Identifying regions and residue identities amenable to rewiring of non-cognate interactions has remained a significant challenge Towards this goal, we use data-driven Direct Coupling Analysis (DCA) as a method of choice. Our experimental results validate that a co-evolutionary approach, purely based on sequence, for selecting mutations with specific predicted changes in activity offers significant improvements over large-scale mutations studies currently used for protein engineering and design

Results

Discussion

Materials and methods