scholarly journals Analysing structural data to explore the function of an essential bacterial protein foldase

2021 ◽  
Author(s):  
Samuel F. Haysom
Keyword(s):  
Structural Biology ◽  
Protein Structures ◽  
Structural Data ◽  
Science Research ◽  
Gram Negative Bacteria ◽  
Bacterial Protein ◽  
Outer Membranes ◽  
Fundamental Part ◽  
3D Data ◽  
Shape Changes

Structural biology, or the study of how protein structures dictate their function, is a fundamental part of life science research, allowing the mechanisms underpinning life to be unravelled at the molecular level. Due to the complexity of 3D data, researchers often use special visualization methods to extract useful information from protein structures. This article uses the most common of these visualisation methods to examine different structures of the β-barrel assembly machinery complex (BAM), an essential protein that folds other proteins into the outer-membranes of Gram-negative bacteria. By exploring how BAM’s 3D shape changes as it interacts with its substrates throughout the folding process, it is possible to reconstruct a potential mechanism for this molecular machine that can be used to drive further research.

scholarly journals New applications of solid-state NMR in structural biology

2018 ◽  
Vol 2 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Patrick C.A. van der Wel
Keyword(s):  
Solid State ◽  
Lipid Bilayer ◽  
Structural Biology ◽  
Protein Structures ◽  
Structural Data ◽  
Magic Angle Spinning ◽  
Crystalline Lattice ◽  
Magic Angle ◽  
Lipid Bilayer Membranes ◽  
Solvent Exposure

Various recent developments in solid-state nuclear magnetic resonance (ssNMR) spectroscopy have enabled an array of new insights regarding the structure, dynamics, and interactions of biomolecules. In the ever more integrated world of structural biology, ssNMR studies provide structural and dynamic information that is complementary to the data accessible by other means. ssNMR enables the study of samples lacking a crystalline lattice, featuring static as well as dynamic disorder, and does so independent of higher-order symmetry. The present study surveys recent applications of biomolecular ssNMR and examines how this technique is increasingly integrated with other structural biology techniques, such as (cryo) electron microscopy, solution-state NMR, and X-ray crystallography. Traditional ssNMR targets include lipid bilayer membranes and membrane proteins in a lipid bilayer environment. Another classic application has been in the area of protein misfolding and aggregation disorders, where ssNMR has provided essential structural data on oligomers and amyloid fibril aggregates. More recently, the application of ssNMR has expanded to a growing array of biological assemblies, ranging from non-amyloid protein aggregates, protein–protein complexes, viral capsids, and many others. Across these areas, multidimensional magic angle spinning (MAS) ssNMR has, in the last decade, revealed three-dimensional structures, including many that had been inaccessible by other structural biology techniques. Equally important insights in structural and molecular biology derive from the ability of MAS ssNMR to probe information beyond comprehensive protein structures, such as dynamics, solvent exposure, protein–protein interfaces, and substrate–enzyme interactions.

scholarly journals A structural biology community assessment of AlphaFold 2 applications

2021 ◽  
Author(s):  
Mehmet Akdel ◽  
Douglas EV Pires ◽  
Eduard Porta-Pardo ◽  
Jurgen Janes ◽  
Arthur O Zalevsky ◽  
...  
Keyword(s):  
Structural Biology ◽  
Protein Complexes ◽  
Homology Modelling ◽  
Structural Data ◽  
Science Research ◽  
Structural Features ◽  
Structural Elements ◽  
Recent Developments ◽  
Transformative Impact ◽  
The Impact

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

Characterization of lipopolysaccharide transport protein complex

2014 ◽  
Vol 9 (2) ◽  
pp. 131-138
Author(s):  
Quanju Xiang ◽  
Haiyan Wang ◽  
Zhongshan Wang ◽  
Yizheng Zhang ◽  
Changjiang Dong
Keyword(s):  
Transport Mechanism ◽  
Transport Proteins ◽  
Gram Negative Bacteria ◽  
Inner Membrane ◽  
Toxic Compounds ◽  
Outer Membranes ◽  
Harsh Conditions ◽  
Cytoplasmic Side

AbstractLipopolysaccharide (LPS) is an essential component of the outer membranes (OM) of most Gram-negative bacteria, which plays a crucial role in protection of the bacteria from toxic compounds and harsh conditions. The LPS is biosynthesized at the cytoplasmic side of inner membrane (IM), and then transported across the aqueous periplasmic compartment and assembled correctly at the outer membrane. This process is accomplished by seven LPS transport proteins (LptA-G), but the transport mechanism remains poorly understood. Here, we present findings by pull down assays in which the periplasmic component LptA interacts with both the IM complex LptBFGC and the OM complex LptDE in vitro, but not with complex LptBFG. Using purified Lpt proteins, we have successfully reconstituted the seven transport proteins as a complex in vitro. In addition, the LptC may play an essential role in regulating the conformation of LptBFG to secure the lipopolysaccharide from the inner membrane. Our results contribute to the understanding of lipopolysaccharide transport mechanism and will provide a platform to study the detailed mechanism of the LPS transport in vitro.

scholarly journals Insights into AMS/PCAT transporters from biochemical and structural characterization of a double Glycine motif protease

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Silvia C Bobeica ◽  
Shi-Hui Dong ◽  
Liujie Huo ◽  
Nuria Mazo ◽  
Martin I McLaughlin ◽  
...  
Keyword(s):  
Structural Data ◽  
Leader Peptide ◽  
Gram Negative Bacteria ◽  
Protease Domain ◽  
Peptide Recognition ◽  
Gram Positive Bacteria ◽  
Dual Functional ◽  
Signaling Peptides

The secretion of peptides and proteins is essential for survival and ecological adaptation of bacteria. Dual-functional ATP-binding cassette transporters export antimicrobial or quorum signaling peptides in Gram-positive bacteria. Their substrates contain a leader sequence that is excised by an N-terminal peptidase C39 domain at a double Gly motif. We characterized the protease domain (LahT150) of a transporter from a lanthipeptide biosynthetic operon in Lachnospiraceae and demonstrate that this protease can remove the leader peptide from a diverse set of peptides. The 2.0 Å resolution crystal structure of the protease domain in complex with a covalently bound leader peptide demonstrates the basis for substrate recognition across the entire class of such transporters. The structural data also provide a model for understanding the role of leader peptide recognition in the translocation cycle, and the function of degenerate, non-functional C39-like domains (CLD) in substrate recruitment in toxin exporters in Gram-negative bacteria.

Molecular Biology for Structural Biology

1999 ◽  
Author(s):  
P. F. Berne ◽  
S. Doublié
Keyword(s):  
Molecular Biology ◽  
Recombinant Protein ◽  
Structural Biology ◽  
Protein Complexes ◽  
Recombinant Expression ◽  
Expression System ◽  
Structural Data ◽  
Natural Source ◽  
Native Form ◽  
Number Of Publications

The number of published 3D structures has increased exponentially in the last decade and the resulting mass of structural data has contributed significantly to the understanding of mechanisms underlying the biology of living cells. However, these mechanisms are so complex that structural biologists face still greater challenges, such as the study of higher-order functional complexes. As an example, we can mention the protein complexes that assemble around activated growth factor receptors to allow the transduction of extracellular signals through the membrane and inside the cell (1). Because of their diverse intrinsic properties, proteins exhibit variable difficulty for structural biology studies. Before the rise of recombinant expression methods, only a minority of protein structures were determined, representing mainly favourable cases: proteins of high abundance in their natural source which could be purified and crystallized, in contrast to rare proteins that were often refractory to crystallization. The advent of methods for recombinant protein overexpression was a breakthrough in this area. It was followed by an increasing number of publications describing the crystallization of proteins, not under their native form, but in modified versions after sequence engineering. First we will consider the classical use of molecular biology applied to optimize the expression system for a recombinant protein for structural biology, without modification of its sequence. In the second part, we will deal with molecular biology procedures aimed at engineering the properties of a protein through sequence modifications in order to make its crystallization possible. In the last part we will give an example where molecular biology can help solve a crystallographic problem, namely that of phase determination by introducing anomalous scatterers (e.g. selenium atoms) into the protein of interest. Whenever extraction of a protein from its natural source appears unsuitable for structural studies, molecular biology resources can be brought in, initially aiming at choosing and setting up an appropriate expression system. This initial approach could involve comparing various expression hosts and vectors and deciding if the protein is to be produced as a fusion to facilitate its purification.

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

scholarly journals Folding Rate Optimization Promotes Frustrated Interactions in Entangled Protein Structures

2019 ◽  
Vol 21 (1) ◽  
pp. 213
Author(s):  
Federico Norbiato ◽  
Flavio Seno ◽  
Antonio Trovato ◽  
Marco Baiesi
Keyword(s):  
Amino Acids ◽  
Structural Biology ◽  
Weak Interactions ◽  
Protein Structures ◽  
Protein Sequences ◽  
Control Case ◽  
Folding Rate ◽  
Empirical Observation ◽  
Rate Optimization ◽  
Kinetic Traps

Many native structures of proteins accomodate complex topological motifs such as knots, lassos, and other geometrical entanglements. How proteins can fold quickly even in the presence of such topological obstacles is a debated question in structural biology. Recently, the hypothesis that energetic frustration might be a mechanism to avoid topological frustration has been put forward based on the empirical observation that loops involved in entanglements are stabilized by weak interactions between amino-acids at their extrema. To verify this idea, we use a toy lattice model for the folding of proteins into two almost identical structures, one entangled and one not. As expected, the folding time is longer when random sequences folds into the entangled structure. This holds also under an evolutionary pressure simulated by optimizing the folding time. It turns out that optmized protein sequences in the entangled structure are in fact characterized by frustrated interactions at the closures of entangled loops. This phenomenon is much less enhanced in the control case where the entanglement is not present. Our findings, which are in agreement with experimental observations, corroborate the idea that an evolutionary pressure shapes the folding funnel to avoid topological and kinetic traps.

scholarly journals Protein Structure Determination in Living Cells

2019 ◽  
Vol 20 (10) ◽  
pp. 2442 ◽  
Author(s):  
Teppei Ikeya ◽  
Peter Güntert ◽  
Yutaka Ito
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Sample Tube ◽  
In Cells

To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

Conformational attractors on the Ramachandran map

1999 ◽  
Vol 55 (2) ◽  
pp. 506-517 ◽  
Author(s):  
Dirk Walther ◽  
Fred E. Cohen
Keyword(s):  
Protein Structures ◽  
Structural Data ◽  
Random Coil ◽  
Helical Conformation ◽  
Structure Quality ◽  
Frequency Distributions ◽  
Ramachandran Map ◽  
Structural Types ◽  
Apparent Correlation ◽  
Independent Parameters

Frequency distributions of protein backbone dihedral angles φ and ψ have been analyzed systematically for their apparent correlation with various crystallographic parameters, including the resolution at which the protein structures had been determined, the R factor and the free R factor, and the results have been displayed in novel differential Ramachandran maps. With improved sensitivity compared with conventionally derived heuristic Ramachandran maps, such differential maps automatically reveal conformational `attractors' to which φ/ψ distributions converge as the crystallographic resolution improves, as well as conformations tied specifically to low-resolution structures. In particular, backbone angular combinations associated with residues in α-helical conformation show a pronounced consolidation with substantially narrowed φ/ψ distributions at higher (better) resolution. Convergence to distinct conformational attractors was also observed for all other secondary-structural types and random-coil conformations. Similar resolution-dependent φ/ψ evolutions were obtained for different crystallographic refinement packages, documenting the absence of any significant artificial biases in the refinement programs investigated here. A comparison of differential Ramachandran maps derived for the R factor and the free R factor as independent parameters proved the better suitability of the free R factor for structure-quality assessment. The resolution-based differential Ramachandran map is available as a reference for comparison with actual protein structural data under WebMol, a Java-based structure viewing and analysis program (http://www.cmpharm.ucsf.edu/cgi-bin/webmol.pl).

scholarly journals Metabolic phospholipid labeling of intact bacteria enables a fluorescence assay that detects compromised outer membranes

2020 ◽  
Vol 61 (6) ◽  
pp. 870-883 ◽  
Author(s):  
Inga Nilsson ◽  
Sheng Y. Lee ◽  
William S. Sawyer ◽  
Christopher M. Baxter Rath ◽  
Guillaume Lapointe ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Gram Negative Bacteria ◽  
Metabolic Labeling ◽  
Wild Type ◽  
Transport Pathways ◽  
Gram Negative ◽  
E Coli ◽  
Outer Membranes ◽  
Outer Leaflet ◽  
Promising Tool

Gram-negative bacteria possess an asymmetric outer membrane (OM) composed primarily of lipopolysaccharides (LPSs) on the outer leaflet and phospholipids (PLs) on the inner leaflet. The loss of this asymmetry due to mutations in the LPS biosynthesis or transport pathways causes the externalization of PLs to the outer leaflet of the OM and leads to OM permeability defects. Here, we used metabolic labeling to detect a compromised OM in intact bacteria. Phosphatidylcholine synthase expression in Escherichia coli allowed for the incorporation of exogenous propargylcholine into phosphatidyl(propargyl)choline and exogenous 1-azidoethyl-choline (AECho) into phosphatidyl(azidoethyl)choline (AEPC), as confirmed by LC/MS analyses. A fluorescent copper-free click reagent poorly labeled AEPC in intact wild-type cells but readily labeled AEPC from lysed cells. Fluorescence microscopy and flow cytometry analyses confirmed the absence of significant AEPC labeling from intact wild-type E. coli strains and revealed significant AEPC labeling in an E. coli LPS transport mutant (lptD4213) and an LPS biosynthesis mutant (E. coli lpxC101). Our results suggest that metabolic PL labeling with AECho is a promising tool for detecting a compromised bacterial OM, revealing aberrant PL externalization, and identifying or characterizing novel cell-active inhibitors of LPS biosynthesis or transport.­

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