Involvement of the <i>Streptococcus mutans</i> PgfE and GalE 4-epimerases in protein glycosylation, carbon metabolism, and cell division

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Abstract

Streptococcus mutans is a key pathogen associated with dental caries and is often implicated in infective endocarditis. This organism forms robust biofilms on tooth surfaces and can use collagen-binding proteins (CBPs) to efficiently colonize collagenous substrates, including dentin and heart valves. One of the best characterized CBPs of S. mutans is Cnm, which contributes to adhesion and invasion of oral epithelial and heart endothelial cells. These virulence properties were subsequently linked to post-translational modification (PTM) of the Cnm threonine-rich repeat region by the Pgf glycosylation machinery, which consists of 4 enzymes: PgfS, PgfM1, PgfE, and PgfM2. Inactivation of the S. mutans pgf genes leads to decreased collagen binding, reduced invasion of human coronary artery endothelial cells, and attenuated virulence in the Galleria mellonella invertebrate model. The present study aimed to better understand Cnm glycosylation and characterize the predicted 4-epimerase, PgfE. Using a truncated Cnm variant containing only 2 threonine-rich repeats, mass spectrometric analysis revealed extensive glycosylation with HexNAc2. Compositional analysis, complemented with lectin blotting, identified the HexNAc2 moieties as GlcNAc and GalNAc. Comparison of PgfE with the other S. mutans 4-epimerase GalE through structural modeling, nuclear magnetic resonance, and capillary electrophoresis demonstrated that GalE is a UDP-Glc-4-epimerase, while PgfE is a GlcNAc-4-epimerase. While PgfE exclusively participates in protein O-glycosylation, we found that GalE affects galactose metabolism and cell division. This study further emphasizes the importance of O-linked protein glycosylation and carbohydrate metabolism in S. mutans and identifies the PTM modifications of the key CBP, Cnm.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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UCSF Chimera--a visualization system for exploratory research and analysis.

Eric F. Pettersen, Thomas Goddard, Conrad Huang … (2004)

The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.

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Effect of use of 13-valent pneumococcal conjugate vaccine in children on invasive pneumococcal disease in children and adults in the USA: analysis of multisite, population-based surveillance.

Matthew R. Moore, Ruth Link-Gelles, William Schaffner … (2015)

In 2000, seven-valent pneumococcal conjugate vaccine (PCV7) was introduced in the USA and resulted in dramatic reductions in invasive pneumococcal disease (IPD) and moderate increases in non-PCV7 type IPD. In 2010, PCV13 replaced PCV7 in the US immunisation schedule. We aimed to assess the effect of use of PCV13 in children on IPD in children and adults in the USA.