Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Togashi, Yuichi; Flechsig, Holger

doi:10.3390/ijms19123899

Cited by 35 publications

(31 citation statements)

References 117 publications

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“…It can be furthermore interesting to discuss the effects of sequence- and distance-dependent stiffness in dynamical simulations of elastic networks, where conformational changes in a protein are directly resolved as over-damped relaxation motions of network residues (e.g., [37,38]). These descriptions include the full non-linear network dynamics beyond the harmonic approximation assumed in the analysis of normal modes, and allow dynamical probing of anisotropic responses of protein elastic networks generated by external perturbations or binding of ligands [39,40]. Effects of heterogeneous interaction parameters in those models have not yet been considered.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

2019

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Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.

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Section: Discussionmentioning

confidence: 99%

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

2019

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“…A notable example of this second class of implicit solvent CG models is represented by structure-based ones, such as Gō-like models (GLM) ( Hills and Brooks, 2009 ; Takada, 2019 ) or elastic network models (ENM) ( Sanejouand, 2013 ; Togashi and Flechsig, 2018 ). Here, the external macroscopic input involved in the construction of the effective CG potential is the static, either stable or metastable, three-dimensional spatial conformation assumed by the protein of interest.…”

Section: Coarse-grained Modeling: Resolution Levelmentioning

confidence: 99%

“…Sticking to a structure-based CG’ing protocol but further reducing the complexity of the interaction basis set one encounters elastic network models ( Sanejouand, 2013 ; Togashi and Flechsig, 2018 ). ENMs stem from the pioneering observation, made by Monique Tirion ( Tirion, 1996 ), that the low-frequency dynamics of globular proteins, in the vicinity of their native conformation , can be accurately reproduced by replacing the system’s complex interaction network by a set of Hookean springs of equal strength connecting neighboring atoms up to a given cutoff distance.…”

Section: Coarse-grained Modeling: Resolution Levelmentioning

confidence: 99%

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

Giulini

Rigoli

Mattiotti

et al. 2021

Front. Mol. Biosci.

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The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

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“…Such coarse-grained analyses of biomolecular dynamics have developed as a viable alternative to traditional molecular dynamics simulations. 23,[39][40][41][42] It should be noted that NMA have proved also useful in structure refinements based on experimental studies in which dynamics is considered, such as X-ray crystallography 43,44 and cryo-electron microscopy. [45][46][47]…”

Section: Computational Approaches To Studying Protein Dynamicsmentioning

confidence: 99%

Parameterizing elastic network models to capture the dynamics of proteins

2021

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Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

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Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Cited by 35 publications

References 117 publications

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

Parameterizing elastic network models to capture the dynamics of proteins

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