pH‐sensitive residues in the p19 RNA silencing suppressor protein from carnation Italian ringspot virus affect siRNA binding stability

Law, Sean M.; Zhang, Bin W.; Brooks, Charles L.

doi:10.1002/pro.2243

Cited by 18 publications

(17 citation statements)

References 80 publications

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“…For example, CHARMM‐GUI currently supports a system building up to 3 million atoms, but as larger spatial scales, longer time scales, and higher levels of realism are becoming possible and necessary, we plan to dynamically adjust this limit in the future. In addition, more modules will be available in Input Generator for advanced simulations techniques, such as QM and QM/MM calculations, NMR structure calculation and refinement, multisite λ‐dynamics, constant pH simulations, grid‐based energy correction map, and various replica‐exchange enhanced sampling methods . In terms of system building, our effort will be placed toward making Glycolipid Modeler and LPS Modeler available in Membrane Builder and other modules, enabling crowding and cellular‐scale system modeling, and extending CHARMM‐GUI for nanoscale material sciences.…”

Section: Concluding Discussionmentioning

confidence: 99%

CHARMM‐GUI 10 years for biomolecular modeling and simulation

et al. 2016

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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, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for 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 molecular details 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.

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Section: Concluding Discussionmentioning

confidence: 99%

CHARMM‐GUI 10 years for biomolecular modeling and simulation

et al. 2016

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“…Through p K a calculations of the monomers and dimer, they also identified key residues responsible for the stability of the HdeA dimer [59, 60]. A similar study was also conducted by Brooks and coworkers to find specific residues in control of the pH-dependent binding of P19 protein to siRNA [61]. …”

Section: Recent Applications Of Constant Ph Molecular Dynamicsmentioning

confidence: 99%

Recent development and application of constant pH molecular dynamics

Chen

Shi

Shen

2014

Molecular Simulation

114

128

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Solution pH is a critical environmental factor for chemical and biological processes. Over the last decade, significant efforts have been made in the development of constant pH molecular dynamics (pHMD) techniques for gaining detailed insights into pH-coupled dynamical phenomena. In this article we review the advancement of this field in the past five years, placing a special emphasis on the development of the all-atom continuous pHMD technique. We discuss various applications, including the prediction of pKa shifts for proteins, nucleic acids and surfactant assemblies, elucidation of pH-dependent population shifts, protein-protein and protein-RNA binding, as well as the mechanisms of pH-dependent self-assembly and phase transitions of surfactants and peptides. We also discuss future directions for the further improvement of the pHMD techniques.

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“…Continuous CPHMD was originally implemented in implicit solvent, improved to account for proton tautomerism, and it provided the first demonstration of using enhanced sampling strategies to accelerate sampling and convergence in CPHMD simulations . The effectiveness of continuous CPHMD has been demonstrated on numerous pH‐dependent systems, inclusive of protein folding, aggregation of Alzheimer's beta‐amyloid peptides, pH‐triggered chaperon activity of HdeA dimers, electrostatic effects on protein stability, self‐assembly of spider silk proteins, and RNA silencing in the carnation italian ringspot virus . Other investigators in the field have also seen a number of successes using discrete CPHMD simulations …”

Section: Introductionmentioning

confidence: 99%

Constant pH molecular dynamics of proteins in explicit solvent with proton tautomerism

et al. 2014

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pH is a ubiquitous regulator of biological activity, including protein-folding, protein-protein interactions and enzymatic activity. Existing constant pH molecular dynamics (CPHMD) models that were developed to address questions related to the pH-dependent properties of proteins are largely based on implicit solvent models. However, implicit solvent models are known to underestimate the desolvation energy of buried charged residues, increasing the error associated with predictions that involve internal ionizable residue that are important in processes like hydrogen transport and electron transfer. Furthermore, discrete water and ions cannot be modeled in implicit solvent, which are important in systems like membrane proteins and ion channels. We report on an explicit solvent constant pH molecular dynamics framework based on multi-site λ-dynamics (CPHMDMSλD). In the CPHMDMSλD framework, we performed seamless alchemical transitions between protonation and tautomeric states using multi-site λ-dynamics, and designed novel biasing potentials to ensure that the physical end-states are predominantly sampled. We show that explicit solvent CPHMDMSλD simulations model realistic pH-dependent properties of proteins such as the Hen-Egg White Lysozyme (HEWL), binding domain of 2-oxoglutarate dehydrogenase (BBL) and N-terminal domain of ribosomal L9 (NTL9), and the pKa predictions are in excellent agreement with experimental values, with a RMSE ranging from 0.72 to 0.84 pKa units. With the recent development of the explicit solvent CPHMDMSλD framework for nucleic acids, accurate modeling of pH-dependent properties of both major class of biomolecules – proteins and nucleic acids is now possible.

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pH‐sensitive residues in the p19 RNA silencing suppressor protein from carnation Italian ringspot virus affect siRNA binding stability

Cited by 18 publications

References 80 publications

CHARMM‐GUI 10 years for biomolecular modeling and simulation

CHARMM‐GUI 10 years for biomolecular modeling and simulation

Recent development and application of constant pH molecular dynamics

Constant pH molecular dynamics of proteins in explicit solvent with proton tautomerism

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