Robust elastic network model: A general modeling for precise understanding of protein dynamics

Kim, Min Hyeok; Lee, Byung Ho; Kim, Moon Ki

doi:10.1016/j.jsb.2015.04.007

Cited by 20 publications

(12 citation statements)

References 58 publications

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“…In the last decades, a large number of studies have shown the usefulness of Normal Mode Analysis (NMA) and Principal Component Analysis (PCA) for the prediction and analysis of protein cooperative motions [13,14,[54][55][56][57][58][59][60][61][62][63]. Many of these studies were conducted using computationally simple Elastic Network Models (ENM) which use uniform harmonic potentials for interacting atom (or residue) pairs instead of more complicated potentials.…”

Section: Elastic Network Modelsmentioning

confidence: 99%

“…Except as mentioned above, ENMs have been successfully used in many combinations with different input information and methods, such as for example data from electron microscopy [14], atomistic MD simulations [17,78], Brownian simulations [79], structure-based models [80,81] and many other combinations that have been thoroughly reviewed [13,14,[54][55][56][57][58][59][60][61][62][63]. When the range of protein structural dynamics studies can be limited to a roughly defined vicinity of a specific folded structure (determined by experiment), ENM, CG models or their modifications can be very effective in predicting protein flexibility [8,36,[82][83][84].…”

Section: Elastic Network Modelsmentioning

confidence: 99%

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Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

Kmiecik

Kouza

Badaczewska-Dawid

et al. 2018

Preprint

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Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.Graphical Abstract

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Section: Elastic Network Modelsmentioning

confidence: 99%

Section: Elastic Network Modelsmentioning

confidence: 99%

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

Kmiecik

Kouza

Badaczewska-Dawid

et al. 2018

Preprint

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show abstract

“…4 ). Modifications and improvements to ENM include the Gaussian network model (GNM) [80] , the anisotropic network method (ANM) [81] , the robust elastic network model (RENM) and the heterogeneous anisotropic elastic network model (HANM) [82] , [83] .…”

Section: The Importance Of Internal Motions To Amino Acid Networkmentioning

confidence: 99%

Biophysical and computational methods to analyze amino acid interaction networks in proteins

O’Rourke

Gorman

Boehr

2016

Computational and Structural Biotechnology Journal

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Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

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“…Due to the sparser network they need to explicitly model chemical interactions, such as disulfide bridges, hydrogen bonds, or van-der-Waals forces. Recently, a mass-weighted variant has been proposed [ 48 ], which was further extended by symmetry constraints to better capture the packed state of protein crystals when their structure is determined experimentally [ 49 ]. These models are particularly accurate in terms of B-factor prediction.…”

Section: Background: Elastic Network Modelsmentioning

confidence: 99%

Elastic network model of learned maintained contacts to predict protein motion

Putz

Brock

2017

PLoS ONE

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We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein’s contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a “deformation-invariant” contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.

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Robust elastic network model: A general modeling for precise understanding of protein dynamics

Cited by 20 publications

References 58 publications

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

Biophysical and computational methods to analyze amino acid interaction networks in proteins

Elastic network model of learned maintained contacts to predict protein motion

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