Lipid Trafficking in Diverse Bacteria.

Lipids are essential for life and serve as cell envelope components, signaling molecules, and nutrients. For lipids to achieve their required functions, they need to be correctly localized. This requires the action of transporter proteins and an energy source. The current understanding of bacterial lipid transporters is limited to a few classes. Given the diversity of lipid species and the predicted existence of specific lipid transporters, many more transporters await discovery and characterization. These proteins could be prime targets for modulators that control bacterial cell proliferation and pathogenesis. One overarching goal of our research is to understand the molecular mechanisms of bacterial metabolite trafficking, including lipids, and to leverage that understanding to identify or engineer inhibitory ligands. In recent years, our work has revealed two novel lipid transport systems in bacteria: bacterial sterol transporters (Bst) A, B, and C in and the TatT proteins in and . Both systems are composed of transporters bioinformatically identified as being involved in the transport of other metabolites, but substrates were never revealed. However, the genetic colocalization of the genes encoding BstABC with sterol biosynthetic enzymes in suggested that they might recognize sterols as substrates. Also, homologues of TatTs are present in diverse bacteria but are overrepresented in bacteria deficient in de novo lipid synthesis or residing in nutrient-poor environments; we reasoned that these proteins might facilitate the transport of lipids. Our efforts to reveal the substrate scope of two TatT proteins revealed their engagement with long-chain fatty acids. Enabling the discovery of the BstABC system and the TatT proteins were bioinformatic analyses, quantitative measurements of protein-ligand equilibrium affinities, and high-resolution structural studies that provided remarkable insights into ligand binding cavities and the structural basis for ligand interaction. These approaches, in particular our bioinformatics and structural work, highlighted the diversity of protein sequence and structures amenable to lipid engagement. These observations allowed the hypothesis that lipid handling proteins, in general and especially so in the bacterial domain, can have diverse amino acid compositions and three-dimensional structures. As such, bioinformatics geared at identifying them in poorly characterized genomes is likely to miss many candidates that diverge from well-characterized family members. This realization spurred efforts to understand the unifying features in all of the lipid handling proteins we have characterized to date. To do this, we inspected the ligand binding sites of the proteins: they were remarkably hydrophobic and sometimes displayed a dichotomy of hydrophobic and hydrophilic amino acids, akin to the ligands that they accommodate in those cavities. Because of this, we reasoned that the physicochemical features of ligand binding cavities could be accurate predictors of a protein's propensity to bind lipids. This finding was leveraged to create structure-based lipid-interacting pocket predictor (SLiPP), a machine-learning algorithm capable of identifying ligand cavities with physico-chemical features consistent with those of known lipid binding sites. SLiPP is especially useful in poorly annotated genomes (such as with bacterial pathogens), where it could reveal candidate proteins to be targeted for the development of antimicrobials.