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  1. Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). These cell-surface single-pass transmembrane (TM) glycoproteins regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated here using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to TM α-helices of the active TPOR dimer was proposed. The models also help elucidating the molecular basis of oncogenic mutations that may involve a non-canonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available. 
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    Free, publicly-accessible full text available September 11, 2024
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  5. Abstract Motivation

    Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis.

    Results

    The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures.

    Availability and implementation

    http://www.glycanstructure.org/glymdb.

    Contact

    wonpil@lehigh.edu

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  6. CHARMM‐GUI,http://www.charmm-gui.org, is a web‐based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM‐GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM‐GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1)PDB Reader & Manipulator,Glycan Reader, andLigand Reader & Modelerfor reading and modifying molecules; (2)Quick MD Simulator,Membrane Builder,Nanodisc Builder,HMMM Builder,Monolayer Builder,Micelle Builder, andHex Phase Builderfor building all‐atom simulation systems in various environments; (3)PACE CG BuilderandMartini Makerfor building coarse‐grained simulation systems; (4)DEER FacilitatorandMDFF/xMDFF Utilizerfor experimentally guided simulations; (5)Implicit Solvent Modeler,PBEQ‐Solver, andGCMC/BD Ion Simulatorfor implicit solvent related calculations; (6)Ligand Binderfor ligand solvation and binding free energy simulations; and (7)Drude Prepperfor preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM‐GUI, such asGlycolipid Modelerfor generation of various glycolipid structures, andLPS Modelerfor generation of lipopolysaccharide structures from various Gram‐negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM‐GUI development project. © 2016 Wiley Periodicals, Inc.

     
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