Ensemble models of proteins and protein domains based on distance distribution restraints

Jeschke, Gunnar

doi:10.1002/prot.25000

Cited by 34 publications

(54 citation statements)

References 56 publications

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“…Distance distributions are particularly valuable for recognizing disorder, as illustrated in Figure 1, and for restraining the ensemble of internal or terminal intrinsically disordered domains [52]. They can be complemented by restraints on bilayer immersion depth from accessibility measurements and on secondary structure from spin labelling site scans [26].…”

Section: Model Building With Epr Restraintsmentioning

confidence: 99%

The contribution of modern EPR to structural biology

Jeschke

2018

Emerging Topics in Life Sciences

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Electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labelling is applicable to biomolecules and their complexes irrespective of system size and in a broad range of environments. Neither short-range nor long-range order is required to obtain structural restraints on accessibility of sites to water or oxygen, on secondary structure, and on distances between sites. Many of the experiments characterize a static ensemble obtained by shock-freezing. Compared with characterizing the dynamic ensemble at ambient temperature, analysis is simplified and information loss due to overlapping timescales of measurement and system dynamics is avoided. The necessity for labelling leads to sparse restraint sets that require integration with data from other methodologies for building models. The double electron-electron resonance experiment provides distance distributions in the nanometre range that carry information not only on the mean conformation but also on the width of the native ensemble. The distribution widths are often inconsistent with Anfinsen's concept that a sequence encodes a single native conformation defined at atomic resolution under physiological conditions.

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Section: Model Building With Epr Restraintsmentioning

confidence: 99%

The contribution of modern EPR to structural biology

Jeschke

2018

Emerging Topics in Life Sciences

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111

133

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“…[29] In determining the dimer structurei nthe membrane-associated BAX assembly,numerous DEER distance distributions,obtained from spin-diluted double-labeled BAX samples, wereu sed to derive the relevant 42-kDa dimer structurea tm itochondria with aid of the multiscale modeling of macromolecules (MMM) program. [31,35] Almost at the same time, the dimer and tetramer structures of BAX were also determined using DEER resultsa sc onstraints in the modeling programso fM tsslSuite and MMM;c onsequently the integrated approach leads to the self-consistent iterative (SCI) procedure as detailed below. [33,[36][37][38] At this point, it is noteworthy to briefly describe the two usefulm odeling programs.…”

Section: Dimer Structure Of Activated Bax Proteinmentioning

confidence: 99%

Identifying Protein Conformational Dynamics Using Spin‐label ESR

Lai

Kuo

Chiang

2019

Chemistry An Asian Journal

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Spin‐label electron spin resonance (ESR) has emerged as a powerful tool to characterize protein dynamics. One recent advance is the development of ESR for resolving dynamical components that occur or coexist during a biological process. It has been applied to study the complex structural and dynamical aspects of membranes and proteins, such as conformational changes in protein during translocation from cytosol to membrane, conformational exchange between equilibria in response to protein‐protein and protein‐ligand interactions in either soluble or membrane environments, protein oligomerization, and temperature‐ or hydration‐dependent protein dynamics. As these topics are challenging but urgent for understanding the function of a protein on the molecular level, the newly developed ESR methods to capture individual dynamical components, even in low‐populated states, have become a great complement to other existing biophysical tools.

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“…Although NMR, fluorescence and scattering-based methods have all been effectively utilized to characterize unstructured systems, the production of representative structural ensembles for IDPs using computational methods has proven far more challenging [4][5][6][7][8][9][10][11][12][13][14] . Many approaches have constructed ensembles by using experimental data to filter sets of potential structures generated from random-coil or Protein Data Bank (PDB) fragment libraries 6,9,15 . Molecular dynamics (MD) force fields and sampling methods, such as replica-exchange, have been developed and optimized to predict IDP ensembles [16][17][18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins

Ferrie

Petersson

2020

Preprint

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As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date accurate fold prediction from sequence has been dominated by Monte-Carlo (MC) based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have an accuracy comparable to state-of-the-art MD with vastly reduced computational demands.

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Ensemble models of proteins and protein domains based on distance distribution restraints

Cited by 34 publications

References 56 publications

The contribution of modern EPR to structural biology

The contribution of modern EPR to structural biology

Identifying Protein Conformational Dynamics Using Spin‐label ESR

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins

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