Macromolecular crystallographic estructure refinement

Afonine, Pavel V.; Urzhumtsev, Alexandre; Adams, Paul D.

doi:10.3989/arbor.2015.772n2005

Cited by 4 publications

(4 citation statements)

References 63 publications

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“…Model refinement is an optimization problem and as such it requires the definition of three entities (for reviews, see Tronrud, 2004;Watkin, 2008;Afonine et al, 2012Afonine et al, , 2015. Firstly, the model, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…A key methodological difference is that for RSR each term depends on only a few atoms, while for FSR each term depends on all model parameters. Most modern macromolecular refinement programs were developed for crystallographic data and therefore perform refinement in reciprocal space, at least as their main mode of operation (see Table 1 in Afonine et al, 2015). This work focuses on the realspace refinement of coordinates of atomic models.…”

Section: Introductionmentioning

confidence: 99%

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Real-space refinement inPHENIXfor cryo-EM and crystallography

Afonine

Poon

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et al. 2018

Acta Crystallogr D Struct Biol

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2,629

1,524

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-space refinement inPHENIXfor cryo-EM and crystallography

Afonine

Poon

Read

et al. 2018

Acta Crystallogr D Struct Biol

Self Cite

2,629

1,524

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“…The fragment binding data used in this study was a subset of the 55 complex structures and 1 apo structure with associated affinity data published by L. Öster et al 53 All 55 complex structures were re-refined using phenix.refine (version 1.14.3283) 54,55 with AFITT 56 generated MMFF94s gradients on the small molecule ligand-complexes used in phenix.refine (PHENIX-AFITT). All refinements using phenix.refine or PHENIX-AFITT used six refinement macrocycles.…”

Section: Crystal Structure Selection and Refinementmentioning

confidence: 99%

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

Lim¹,

Osato²,

Warren³

et al. 2019

Preprint

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<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

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“…Model refinement against experimental data is an optimization process of changing the parameters that describe the model to satisfy a goal (or target) function. A target function relates the model parameters to experimental data and, if needed, a priori knowledge (for reviews, see Tronrud, 2004;Watkin, 2008;Afonine et al, 2015). In the case of biomacromolecules the data are almost always of insufficient quality to be used alone in refinement, and thus the use of a priori knowledge is almost always needed, with the exception being ultrahigh-resolution data, which constitute less than 0.5% of all entries in the PDB.…”

Section: Introductionmentioning

confidence: 99%

Q|R: quantum-based refinement

Zheng

Reimers

Waller

et al. 2017

Acta Crystallogr D Struct Biol

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Quantum-based refinement utilizes chemical restraints derived from quantum-chemical methods instead of the standard parameterized library-based restraints used in refinement packages. The motivation is twofold: firstly, the restraints have the potential to be more accurate, and secondly, the restraints can be more easily applied to new molecules such as drugs or novel cofactors. Here, a new project called Q|R aimed at developing quantum-based refinement of biomacromolecules is under active development by researchers at Shanghai University together with PHENIX developers. The central focus of this long-term project is to develop software that is built on top of open-source components. A development version of Q|R was used to compare quantum-based refinements with standard refinement using a small model system.

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Macromolecular crystallographic estructure refinement

Cited by 4 publications

References 63 publications

Real-space refinement inPHENIXfor cryo-EM and crystallography

Real-space refinement inPHENIXfor cryo-EM and crystallography

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

Q|R: quantum-based refinement

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