Structure of an endogenous mycobacterial MCE lipid transporter

Chen, James; Fruhauf, Alice; Fan, Catherine; Ponce, Jackeline; Ueberheide, Beatrix; Bhabha, Gira; Ekiert, D.C.

doi:10.1038/s41586-023-06366-0

Cited by 21 publications

(11 citation statements)

References 82 publications

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“…At the level of quaternary structure, the top five predicted models aligned well with the experimental structure with average α‐carbon RMSDs of 0.75 Å for the portal, 0.88 Å for the ring, 1.64 Å for the ABC transporter, and 3.34 Å for the needle. For the portal domain, the top five predictions were similar to the cryo‐EM structure, with additional predicted segments for parts of the Mce1C, Mce1D, and Mce1F C‐termini that were unresolved in the cryo‐EM map 46 (Figure 1C). The ring domain was also well predicted in all five models with minor deviations in the loops lining the central pore (Figure 4D).…”

Section: Resultsmentioning

confidence: 73%

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Protein target highlights in CASP15: Analysis of models by structure providers

Alexander,

Durairaj,

Kryshtafovych

et al. 2023

Proteins

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We present an in‐depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three‐dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

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Section: Resultsmentioning

confidence: 73%

“…Our structure revealed how proteins from the mce1 operon assemble to form an unusual ATP-binding-cassette (ABC) transporter complex with a long hydrophobic tunnel for protected lipid transport across the bacterial cell envelope (Figure 4A). 46 4A).…”

Section: Mycobacterium Smegmatismentioning

confidence: 98%

Protein target highlights in CASP15: Analysis of models by structure providers

Alexander,

Durairaj,

Kryshtafovych

et al. 2023

Proteins

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“…A second key limitation is that the model only identifies LBPs that are embedded within monomeric proteins. If a lipid binds to the protein’s surface (such as with ApoA1 and ApoE), or if the binding site is formed upon the oligomerization of one or more subunits (an example is the recently reported MCE transport system 54 ), the classifier is unable to detect these as LBPs. Regardless, the high precision suggests that SLiPP is well suited as a tool to facilitate the identification of novel lipid interacting proteins and complements existing low-throughput discovery methods.…”

Section: Resultsmentioning

confidence: 99%

Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics

Chou,

Decosto,

Chatterjee

et al. 2024

Preprint

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Lipids are primary metabolites that play essential roles in multiple cellular pathways. Alterations in lipid metabolism and transport are associated with infectious diseases and cancers. As such, proteins involved in lipid synthesis, trafficking, and modification, are targets for therapeutic intervention. The ability to rapidly detect these proteins can accelerate their biochemical and structural characterization. However, it remains challenging to identify lipid binding motifs in proteins due to a lack of conservation at the amino acids level. Therefore, new bioinformatic tools that can detect conserved features in lipid binding sites are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), a structural bioinformatics algorithm that uses machine learning to detect protein cavities capable of binding to lipids in experimental and AlphaFold-predicted protein structures. SLiPP, which can be used at proteome-wide scales, predicts lipid binding pockets with an accuracy of 96.8% and a F1 score of 86.9%. Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. Use of the algorithm to detect lipid binding proteins in the proteomes of various bacteria, yeast, and human have produced hits annotated or verified as lipid binding proteins, and many other uncharacterized proteins whose functions are not discernable from sequence alone. Because of its ability to identify novel lipid binding proteins, SLiPP can spur the discovery of new lipid metabolic and trafficking pathways that can be targeted for therapeutic development.

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“…It is now possible to determine very large protein complexes such as the type VII secretion system or the mammalian cell entry (MCE) lipid transporter related to mycobacteria. , With these structures, it is necessary to take into account not only one membrane but also multiple layers in the cell envelope to propose a complete and accurate model of these super large assemblies. While modeling a simple bilayer is now routinely performed, modeling multilayered systems remains difficult.…”

Section: Realistic Mycobacterial Cell Envelopes: Multiple Layers and ...mentioning

confidence: 99%

Molecular Modeling and Simulation of the Mycobacterial Cell Envelope: From Individual Components to Cell Envelope Assemblies

Brown,

Chavent,

2023

J. Phys. Chem. B

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Unlike typical Gram-positive bacteria, the cell envelope of mycobacteria is unique and composed of a mycobacterial outer membrane, also known as the mycomembrane, a peptidoglycan layer, and a mycobacterial inner membrane, which is analogous to that of Gram-negative bacteria. Despite its importance, however, our understanding of this complex cell envelope is rudimentary at best. Thus, molecular modeling and simulation of such an envelope can benefit the scientific community by proposing new hypotheses about the biophysical properties of its different layers. In this Perspective, we present recent advances in molecular modeling and simulation of the mycobacterial cell envelope from individual components to cell envelope assemblies. We also show how modeling other types of cell envelopes, such as that of Escherichia coli , may help modeling part of the mycobacterial envelopes. We hope that the studies presented here are just the beginning of the road and more and more new modeling and simulation studies help us to understand crucial questions related to mycobacteria such as antibiotic resistance or bacterial survival in the host.

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Structure of an endogenous mycobacterial MCE lipid transporter

Cited by 21 publications

References 82 publications

Protein target highlights in CASP15: Analysis of models by structure providers

Protein target highlights in CASP15: Analysis of models by structure providers

Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics

Molecular Modeling and Simulation of the Mycobacterial Cell Envelope: From Individual Components to Cell Envelope Assemblies

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