<i>doGlycans</i>–Tools for Preparing Carbohydrate Structures for Atomistic Simulations of Glycoproteins, Glycolipids, and Carbohydrate Polymers for GROMACS

Danne, Reinis; Poojari, Chetan; Martinez‐Seara, Hector; Rissanen, Sami; Lolicato, Fabio; Róg, Tomasz; Vattulainen, Ilpo

doi:10.1021/acs.jcim.7b00237

Cited by 83 publications

(70 citation statements)

References 49 publications

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“…All of these additional loops and regions were built using MODELLER (Eswar et al, 2006). Additionally, based on the glycan composition defined previously (Sparrow et al, 2007(Sparrow et al, , 2008, we added 17 N-linked and 6 O-linked glycans on each monomer (Table S3) using the doGlycans tool (Danne et al, 2017). The OPLS-AA force field (Kaminski et al, 2001;Danne et al, 2017) was used for proteins, glycans, and ions.…”

Section: Atomistic MD Simulationsmentioning

confidence: 99%

“…Additionally, based on the glycan composition defined previously (Sparrow et al, 2007(Sparrow et al, , 2008, we added 17 N-linked and 6 O-linked glycans on each monomer (Table S3) using the doGlycans tool (Danne et al, 2017). The OPLS-AA force field (Kaminski et al, 2001;Danne et al, 2017) was used for proteins, glycans, and ions. The glycosylated IR-ECD was energyminimized in vacuum using the steepest descent algorithm to remove any steric clashes due to overlapping atoms.…”

Section: Atomistic MD Simulationsmentioning

confidence: 99%

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Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain

Gutmann

Schäfer

Poojari

et al. 2019

Journal of Cell Biology

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162

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Glucose homeostasis and growth essentially depend on the hormone insulin engaging its receptor. Despite biochemical and structural advances, a fundamental contradiction has persisted in the current understanding of insulin ligand–receptor interactions. While biochemistry predicts two distinct insulin binding sites, 1 and 2, recent structural analyses have resolved only site 1. Using a combined approach of cryo-EM and atomistic molecular dynamics simulation, we present the structure of the entire dimeric insulin receptor ectodomain saturated with four insulin molecules. Complementing the previously described insulin–site 1 interaction, we present the first view of insulin bound to the discrete insulin receptor site 2. Insulin binding stabilizes the receptor ectodomain in a T-shaped conformation wherein the membrane-proximal domains converge and contact each other. These findings expand the current models of insulin binding to its receptor and of its regulation. In summary, we provide the structural basis for a comprehensive description of ligand–receptor interactions that ultimately will inform new approaches to structure-based drug design.

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Section: Atomistic MD Simulationsmentioning

confidence: 99%

Section: Atomistic MD Simulationsmentioning

confidence: 99%

Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain

Gutmann

Schäfer

Poojari

et al. 2019

Journal of Cell Biology

Self Cite

162

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“…Very recently, the DoGlycan set of tools [31] was developed in order to perform the same function with the Glycam forcefield, which is dedicated to the study of carbohydrate structures.…”

Section: Glycosylation and Modellingmentioning

confidence: 99%

Umbrella Visualization: A method of analysis dedicated to glycan flexibility with UnityMol

Besancon

Guillot

Blaise

et al. 2020

Methods

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A B S T R A C TN-glycosylation is a post-translational modification heavily impacting protein functions. Some alterations of glycosylation, such as sialic acid hydrolysis, are related to protein dysfunction. Because of their high flexibility and the many reactive groups of the glycan chains, studying glycans with in vitro methods is a challenging task. Molecular dynamics is a useful tool and probably the only one in biology able to overcome this problem and gives access to conformational information through exhaustive sampling. To better decipher the impact of Nglycans, the analysis and visualization of their influence over time on protein structure is a prerequisite. We developed the Umbrella Visualization, a graphical method that assigns the glycan intrinsic flexibility during a molecular dynamics trajectory. The density plot generated by this method brought relevant informations regarding glycans dynamics and flexibility, but needs further development in order to integrate an accurate description of the protein topology and its interactions. We propose here to transform this analysis method into a visualization mode in UnityMol. UnityMol is a molecular editor, viewer and prototyping platform, coded in C#. The new representation of glycan chains presented in this study takes into account both the main positions adopted by each antenna of a glycan and their statistical relevance. By displaying the collected data on the protein surface, one is then able to investigate the protein/glycan interactions. Thr and Asn-X-Ser, where X is any amino acid but not proline [10]. In

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“…Another option is the CHARMM‐GUI Martini Maker, which currently offers two Escherichia coli LPS molecules (Re and Ra LPS) for the MARTINI Coarse‐Grained force field but does not, at present, support any other species. Some glycolipid and glycan modeling tools can also be used to model LPS molecules, such as doGlycans for the AMBER/GLYCAM and OPLS force fields; however, these methods are not able to construct LPS‐containing bilayers, either homogeneous or mixed. As such, apart from CHARMM‐GUI, there are very limited options to automatically create LPS‐containing membranes with force fields or molecules outside the aforementioned systems (e.g., GROMOS, or MARTINI with LPSs from species other than E. coli ), even though valid Lipid A and LPS parameters exist .…”

Section: Introductionmentioning

confidence: 99%

The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

2019

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Outer membranes are a crucial component of Gram‐negative bacteria, containing standard lipids in their inner leaflet, lipopolysaccharides (LPSs) in their outer leaflet, and transmembrane β‐barrels known as outer membrane proteins (OMPs). OMPs regulate functions such as substrate transport and cell movement, while LPSs act as a protective barrier for bacteria and can cause toxic reactions in humans. However, the experimental study of outer membranes is challenging. Molecular dynamics simulations are often used for the computational study of membrane systems, but the preparation of complex, LPS‐rich outer membranes is not straightforward. The Gram‐Negative Outer Membrane Modeler (GNOMM) is an automated pipeline for preparing simulation systems of OMPs embedded in LPS‐containing membranes in four different force fields. Given the physiological and clinical importance of outer membranes and their components, GNOMM can be a useful tool in the study of their structure, function, and implications in diseases. GNOMM is available at http://bioinformatics.biol.uoa.gr/GNOMM. © 2019 Wiley Periodicals, Inc.

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doGlycans–Tools for Preparing Carbohydrate Structures for Atomistic Simulations of Glycoproteins, Glycolipids, and Carbohydrate Polymers for GROMACS

Cited by 83 publications

References 49 publications

Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain

Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain

Umbrella Visualization: A method of analysis dedicated to glycan flexibility with UnityMol

The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

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