Exploring the structurial diversity and engineering potential of thermophilic periplasmic binding proteins

Abstract

The periplasmic binding protein (PBP) superfamily is found throughout the genosphere of both prokaryotic and eukaryotic organisms. PBPs function as receptors in bacterial solute transport and chemotaxis systems; however the same fold is also used in transcriptional regulators, enzymes, and eukaryotic neurotransmitter receptors. This versatility has been exploited for structure-based computational protein design experiments where PBPs have been engineered to bind novel ligands and serve as biosensors for the detection of small-molecule ligands relevant to biomedical or defense-related interests. In order to further understand functional adaptation from a structural biology perspective, and to provide a set of robust starting points for engineering novel biosensors by structure-based design, I have characterized the ligand-binding properties and solved the structure of nine PBPs from various thermophilic bacteria. Analysis of these structures reveals a variety of mechanisms by which diverse function can be encoded in a common fold. It is observed that re-modeling of secondary structure elements (such as insertions, deletions, and loop movements), and re-decoration of amino acid side-chains are common diversification mechanisms in PBPs. Furthermore, the relationship between hinge-bending motion and ligand binding is critical to understanding the function of natural or engineered adaptations in PBPs. Three of these proteins were solved in both the presence and absence of ligand which allowed for the first time the observation and analysis of ligand-induced structural rearrangements in thermophilic PBPs. This work revealed that the magnitude and transduction of local and global ligand-induced motions are diverse throughout the PBP superfamily. Through the analysis of the open-to-closed transition, and the identification of natural structural adaptations in thermophilic members of the PBP superfamily, I reveal strategies which can be applied to computational protein design to significantly improve current strategies.