bioRxivBiochemistry
The structure of HiSiaQM defines the architecture of tripartite ATP-independent periplasmic (TRAP) transporters
M. F. Peter, P. Depping, N. Schneberger, E. Severi, K. Gatterdam, S. Tindall, A. Durand, V. Heinz, P. A. Koenig, M. Geyer, C. Ziegler, G. H. Thomas, G. Hagelueken
Abstract
Tripartite ATP-independent periplasmic (TRAP) transporters are widespread in bacteria and archaea and provide important uptake routes for many metabolites 1-3. They consist of three structural domains, a soluble substrate-binding protein (P-domain), and two transmembrane domains (Q- and M-domains) that form a functional unit 4. While the structures of the P-domains are well-known, an experimental structure of any QM-domain has been elusive. HiSiaPQM is a TRAP transporter for the monocarboxylate sialic acid, which plays a key role in the virulence of pathogenic bacteria 5. Here, we present the first cryo-electron microscopy structure of the membrane domains of HiSiaPQM reconstituted in lipid nanodiscs. The reconstruction reveals that TRAP transporters consist of 15 transmembrane helices and are structurally related to elevator-type transporters, such as GltPh and VcINDY 6, 7. Whereas the latter proteins function as multimers, the idiosyncratic Q-domain of TRAP transporters enables the formation of a monomeric elevator architecture. Structural and mutational analyses together with an AlphaFold 8 model of the tripartite (PQM) complex reveal the structural and conformational coupling of the substrate-binding protein to the transporter domains. Furthermore, we characterize high-affinity VHHs that bind to the periplasmic side of HiSiaQM and inhibit sialic acid uptake in vivo. Thereby, they also confirm the orientation of the protein in the membrane. Our study provides the first structure of any binding-protein dependent secondary transporter and provides starting points for the development of specific inhibitors.
- organism: bacteria (other)
- assay: cryo-EM
- research focus: protein structure prediction
- approach: experimental assay
- approach: deep learning
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