Solvent dramatically affects protein structure refinement

Chopra, Gaurav; Summa, Christopher M.; Levitt, Michael

doi:10.1073/pnas.0810818105

Cited by 100 publications

(111 citation statements)

References 47 publications

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“…3-7) indicate that our potentials might be promising for refinement, since they show strong funnels toward the native state. However, being able to refine a structure well also depends on the energy landscape close to the native structure (Chopra et al 2008)-we cannot visualize this by the simple scoring scheme we have adopted here.…”

Section: Fully Differentiable Potentials For Refinement and Modelingmentioning

confidence: 99%

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

Bernauer¹,

Huang

Sim³

et al. 2011

RNA

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128

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RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA-in particular the nonhelical regions-is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.

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Section: Fully Differentiable Potentials For Refinement and Modelingmentioning

confidence: 99%

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

Bernauer¹,

Huang

Sim³

et al. 2011

RNA

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128

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“…With the exception of some small, tightly packed proteins, [2][3][4] the uMD approach generally fails to improve the models in the range 1-10 Å from the target structure. [5][6][7][8][9] Initially, this situation was blamed on short uMD trajectories that could not adequately sample the conformational phase space. However, the latest results suggest that "the structure that realizes the global free-energy minimum for the force field employed is not the X-ray or NMR structure," 1 i.e.…”

Section: Introductionmentioning

confidence: 99%

Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

Xue

Skrynnikov

2014

Protein Science

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Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15 N R 1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields.

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“…An important feature of a virus model is the structure of the shell of ordered water molecules associated with the virus. [77,78,180] This does not include the bulk water in the crystal interstices, or at the centre of the particles, but those waters that have fixed positions by virtue of hydrogen bonding interactions with the virus. In the case of STMV, for example, it was found that ordered water molecules amounted to between 10% and 15% of the total mass of the capsid.…”

Section: Refinement Of Virus Crystal Structuresmentioning

confidence: 99%

A guide to the crystallographic analysis of icosahedral viruses

McPherson

Larson

2015

Crystallography Reviews

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Determining the structure of an icosahedral virus crystal by X-ray diffraction follows very much the same course as conventional protein crystallography. The major differences arise from the relatively large sizes of the particles, which significantly affect the data collection process, data processing and management, and later, the refinement of a model. Most of the other differences are due to the high 5 3 2 point group symmetry of icosahedral viruses. This alters dramatically the means by which initial phases are obtained by molecular substitution, extended to higher resolution by electron density averaging and density modification, and the refinement of the structure in the light of high non-crystallographic symmetry. In this review, we attempt to lead the investigator through the various steps involved in solving the structure of a virus crystal. These steps include the purification of viruses, their crystallization, the recording of X-ray diffraction data, and its reduction to structure amplitudes. It further addresses the problems attending phase determination and ultimately the refinement of a model. Finally, we describe the unique properties of virus crystals and the factors that influence their physical and diffraction properties.

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Solvent dramatically affects protein structure refinement

Cited by 100 publications

References 47 publications

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

A guide to the crystallographic analysis of icosahedral viruses

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