Two-component systems interface discrimination in Actinobacillus pleuropneumoniae

Author:

Martin Eduardo M1,Guerrero-Barrera Alma L1,Avelar-Gonzalez F Javier1,Salinas-Gutierrez Rogelio1,Jacques Mario2

Affiliation:

1. Universidad Autónoma de Aguascalientes

2. Université de Montréal

Abstract

Abstract

Background Pathogenic bacteria grow in different environments and have developed signaling systems known as two-component systems that allow them to thrive in distinct habitats efficiently. Actinobacillus pleuropneumoniae is an obligate pig pathogen that colonizes its host and survives outside it by forming biofilms. The small number of the two-component systems in this pathogen makes it a suitable model to assess the interaction specificity of these systems. Results This was done through multiple sequence alignments, mutual information, heterodimer modeling, structural data, molecular dynamics, and the interface coupling index, which were used to evaluate molecular recognition. For the study, more than two thousand homologue sequences were collected from a diverse range of bacteria. Four different clusters of specificity-determining residues were found for all evaluated systems. The system-wide discrimination capability of twocomponent systems relies on the composition of these clusters composed of 3, 3, 4, and 5 residue pairs, from systems CpxAR, NarQP, PhoRB, and QseCB, respectively. These residue pairs are spatially nearby, the shape and composition of each cluster are systemspecific and have minimal overlap among them. Conclusions The interaction interface composition of the twocomponent systems network in A. pleuropneumoniae was defined and their discriminatory components were described. In summary, molecular recognition depends on specific components from the interaction interface called orthologue interaction specificity clusters. These clusters enable the emergence of specificity, which allows the system to differentiate between cognate and non-cognate components, thereby enabling the system to recognize highly similar components through molecular recognition.

Publisher

Research Square Platform LLC

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