scholarly journals Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

2015 ◽  
Vol 143 (20) ◽  
pp. 204106 ◽  
Author(s):  
Kelin Xia ◽  
Kristopher Opron ◽  
Guo-Wei Wei
Keyword(s):  
Network Model ◽  
Gaussian Network Model ◽  
Anisotropic Network ◽  
Gaussian Network ◽  
Anisotropic Network Model

Gaussian Network Model Revisited: Effects of Mutation and Ligand Binding on Protein Behavior

2021 ◽  
Author(s):  
Burak Erman
Keyword(s):  
Ligand Binding ◽  
Network Model ◽  
Adjacency Matrix ◽  
In Silico ◽  
Coarse Grained ◽  
Published Data ◽  
Specific Information ◽  
Gaussian Network Model ◽  
Gaussian Network ◽  
Alpha Carbon

The coarse-grained Gaussian Network model, GNM, considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including the local effect of heavy atoms in the neighborhood of each alpha carbon into the Kirchoff Adjacency Matrix. The presence of other atoms in the coordination shell of each alpha carbon diminishes the magnitude of fluctuations of that alpha carbon. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff Adjacency Matrix of the whole system which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modelled in analogy with phantom network theory of rubber elasticity. With this modification, it is now possible to make predictions on the effects of mutation and ligand binding on residue fluctuations and their pair-correlations. We refer to the new model as all-atom GNM. Using examples from published data we show that the all-atom GNM applied to in silico mutated proteins and to their laboratory mutated structures gives similar results. Thus, loss and gain of correlations, which may be related to loss and gain of function, may be studied by using simple in silico mutations only.

scholarly journals Gaussian network model can be enhanced by combining solvent accessibility in proteins

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Hua Zhang ◽  
Tao Jiang ◽  
Guogen Shan ◽  
Shiqi Xu ◽  
Yujie Song
Keyword(s):  
Network Model ◽  
Solvent Accessibility ◽  
Gaussian Network Model ◽  
Gaussian Network

Wavelet transformed Gaussian network model

2014 ◽  
Vol 13 (06) ◽  
pp. 1450053 ◽  
Author(s):  
Meng Zhan ◽  
Suhong Li ◽  
Fan Li
Keyword(s):  
Protein Dynamics ◽  
Network Model ◽  
Protein Structures ◽  
Network Models ◽  
Experimental Results ◽  
Gaussian Network Model ◽  
The Mean ◽  
Dynamics And Function ◽  
And Function ◽  
Gaussian Network

Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.

scholarly journals Causality, Transfer Entropy and Allosteric Communication Landscapes in Proteins with Harmonic Interactions

2016 ◽  
Author(s):  
Aysima Hacisuleyman ◽  
Burak Erman
Keyword(s):  
Network Model ◽  
Protein Function ◽  
Information Transfer ◽  
Protein Activity ◽  
Additional Information ◽  
Gaussian Network Model ◽  
Allosteric Communication ◽  
Rapid Characterization ◽  
Dynamics Simulations ◽  
Gaussian Network

AbstractA fast and approximate method of generating allosteric communication landscapes is presented by using Schreiber's entropy transfer concept in combination with the Gaussian Network Model of proteins. Predictions of the model and the allosteric communication landscapes generated show that information transfer in proteins does not necessarily take place along a single path, but through an ensemble of pathways. The model emphasizes that knowledge of entropy only is not sufficient for determining allosteric communication and additional information based on time delayed correlations has to be introduced, which leads to the presence of causality in proteins. The model provides a simple tool for mapping entropy sink-source relations into pairs of residues. Residues that should be manipulated to control protein activity may be determined with this approach. This should be of great importance for allosteric drug design and for understanding the effects of mutations on protein function. The model is applied to determine allosteric communication in two proteins, Ubiquitin and Pyruvate Kinase. Predictions are in agreement with detailed molecular dynamics simulations and experimental evidence.SignificanceProteins perform their function by an exchange of information within themselves and with their environments through correlated fluctuations of their atoms. Fluctuations of one atom may drive the fluctuations of another. Information transmitted in this way leads to allosteric communication which is described as the process in which action at one site of the protein is transmitted to another site at which the protein performs its activity. Disruption of allosteric communication by mutation for example leads to disease. The present paper incorporates information theoretic concepts into the well known Gaussian Network Model of proteins and allows for rapid characterization of allosteric communication landscapes for normal functioning as well as malfunctioning proteins.

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