Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model

Dutagaci, Bercem; Feig, Michael

doi:10.1021/acs.jcim.7b00510

Cited by 9 publications

(11 citation statements)

References 71 publications

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“…We tested different variations of the refinement protocol with an explicit lipid bilayer, where we used either DPPC for all targets or varied the lipid type based on predicted hydrophobic lengths of the membrane proteins calculated using the MEMHLength program. 55 The lipid choice followed the experimental hydrocarbon region thicknesses reported by Kucerka et al; 56 21.7 Å for DLPC, 25.7 Å for DMPC (measured at a temperature of 30˚C), and 28.5 Å for DPPC (measured at a temperature of 50˚C). Hydrophobic lengths were predicted for homology models after equilibration in DPPC lipid bilayer (see Table 1).…”

Section: Methodsmentioning

confidence: 99%

Structure refinement of membrane proteins via molecular dynamics simulations

2018

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A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

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Section: Methodsmentioning

confidence: 99%

Structure refinement of membrane proteins via molecular dynamics simulations

2018

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“…These qualities make membrane systems good candidates for implicit solvent simulations, such as simulation of whole membrane proteins(104, 115, 119, 114). Recent applications include predicting the hydrophobic length of membrane proteins(19), and protein structure refinement(20).…”

Section: Application Examplesmentioning

confidence: 99%

Generalized Born Implicit Solvent Models for Biomolecules

Onufriev

Case

2019

Annu. Rev. Biophys.

197

210

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It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.

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“…Further, we benchmarked our energy function against both thermodynamic and structure prediction data. Previous studies have evaluated membrane energy functions on a single test such as tilt angles (53), native structure discrimination (58,72), predicting hydrophobic lengths (73), ∆∆G prediction (74) and sequence recovery (75). Simultaneously performing the benchmarks enables a well-rounded evaluation of the energy function for diverse biomolecular modeling tasks.…”

Section: R a F Tmentioning

confidence: 99%

Protein structure prediction and design in a biologically-realistic implicit membrane

Alford

Fleming

et al. 2019

Preprint

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Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets including prediction of protein orientations in the bilayer, ∆∆G calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.membrane proteins | lipid composition | energy function | structure prediction | protein design

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Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model

Cited by 9 publications

References 71 publications

Structure refinement of membrane proteins via molecular dynamics simulations

Structure refinement of membrane proteins via molecular dynamics simulations

Generalized Born Implicit Solvent Models for Biomolecules

Protein structure prediction and design in a biologically-realistic implicit membrane

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