An information-based network approach for protein classification

Wan, Xiaogeng; Zhao, Xiaohui; Yau, Stephen S.-T.

doi:10.1371/journal.pone.0174386

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Similar to CR, we can get a K×K nMIR matrix for each of the structural classes: where is the nMIR value between X i and X j ( i , j = 1,2,…, K ) [ 36 ], is the maximum entropy for all X i (i = 1,2,…,K), is the mutual information rate between X i and X j , and when i = j it degenerates to the Shannon Entropy of X i [ 50 ]: …”

Section: Methodsmentioning

confidence: 99%

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A protein structural study based on the centrality analysis of protein sequence feature networks

Wan

Tan

2021

PLoS ONE

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In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features.

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Section: Methodsmentioning

confidence: 99%

“…The nMIR is a model-free measure that evaluates mutual relations no matter linear or not. Higher nMIR values may indicate stronger symmetric relations between the feature series [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

A protein structural study based on the centrality analysis of protein sequence feature networks

Wan

Tan

2021

PLoS ONE

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“…Sequence space can be modelled through mutual information (Wan, Zhao & Yau, 2017 ). These values were used to construct a matrix of similarities between sequences, which were then converted to adjacency in a network.…”

Section: Networkmentioning

confidence: 99%

A review of visualisations of protein fold networks and their relationship with sequence and function

2022

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Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

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“…This data set contains 50 beta-globin protein sequences from 50 species studied in [46,[49][50][51][52][53], and the accession numbers have been shown in Additional file 1: Notes 1.2. After extracting features by the method DCGR and reducing the dimensionality using PCA, the Cosine distance was used to calculate the distance matrix of 50 beta-globin protein sequences, and the phylogenetic tree was also constructed in Fig.…”

Section: Similarity Analysis Of 50 Beta-globin Protein Sequencesmentioning

confidence: 99%

“…The phylogenetic trees of other methods [46,[49][50][51][52][53] including ClustalW have also been shown in Additional file 1: Figures S9-S15. After comparison, we found that ClustalW achieves very similar results with our method DCGR, while the other methods performs much worse since even the mammals and non-mammals cannot be correctly separated by the methods in [46,[49][50][51][52][53], and lots of proteins are erroneously clustered by the methods in [46,[51][52][53].…”

Section: Similarity Analysis Of 50 Beta-globin Protein Sequencesmentioning

confidence: 99%

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information

Zhang

et al. 2019

BMC Bioinformatics

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Background Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design. Results We introduce DCGR, a novel method for extracting features from protein sequences based on the chaos game representation, which is developed by constructing CGR curves of protein sequences according to physicochemical properties of amino acids, followed by converting the CGR curves into multi-dimensional feature vectors by using the distributions of points in CGR images. Tested on five data sets, DCGR was significantly superior to the state-of-the-art feature extraction methods. Conclusion The DCGR is practically powerful for extracting effective features from protein sequences, and therefore important in similarity analysis of protein sequences, study of protein-protein interactions and prediction of protein functions. It is freely available at https://sourceforge.net/projects/transcriptomeassembly/files/Feature%20Extraction . Electronic supplementary material The online version of this article (10.1186/s12859-019-2943-x) contains supplementary material, which is available to authorized users.

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An information-based network approach for protein classification

Cited by 6 publications

References 32 publications

A protein structural study based on the centrality analysis of protein sequence feature networks

A protein structural study based on the centrality analysis of protein sequence feature networks

A review of visualisations of protein fold networks and their relationship with sequence and function

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information

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