Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database

Quaglia, Federica; Lázár, Tamás; Hatos, András; Tompa, Péter; Piovesan, Damiano; Tosatto, Silvio

doi:10.1002/cpz1.192

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…The switch from the inactive to the active (and vice versa ) is driven by allostery. 59,87,88 Allostery can be triggered by noncovalent or covalent binding events, 88–112 leading to conformational and dynamic changes, 36,41,94,95,113–120 by inciting local energetic frustration, or conflicts. 25,121–123 Conformational changes relieving the frustration propagate in the structure, shifting the ensemble from the inactive toward the stable, minimally frustrated active state (Fig.…”

Section: Allostery Is a Hallmark Of Function If No Protein Conformati...mentioning

confidence: 99%

Protein conformational ensembles in function: roles and mechanisms

Nussinov,

Liu,

Zhang

et al. 2023

RSC Chem. Biol.

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Section: Allostery Is a Hallmark Of Function If No Protein Conformati...mentioning

confidence: 99%

Protein conformational ensembles in function: roles and mechanisms

Nussinov,

Liu,

Zhang

et al. 2023

RSC Chem. Biol.

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“…One of the most efficient methods is to generate structural ensembles that reflect experimentally determined parameters, obtained typically from NMR and/or SAXS measurements [2,3]. The number of such ensembles is growing as reflected in the recently updated Protein Ensemble Database [4,5]. The distinctive feature of such ensembles is that they reflect the experimentally determined parameters as a whole, and no individual conformer is expected to correspond to all the data.…”

Section: Introduction 1ensemble-based Modeling Of Protein Internal Dynamicsmentioning

confidence: 99%

DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences

2021

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Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server.

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