Understanding the Structure and Dynamics of Peptides and Proteins Through the Lens of Network Science

Fossépré, Mathieu; Leherte, Laurence; Laaksonen, Aatto; Vercauteren, Daniel P.

doi:10.1002/9783527806836.ch6

Cited by 5 publications

(11 citation statements)

References 290 publications

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“…With only few additions, the application can be extended by user-defined residue features, which could be used to better capture the structural features of the protein [32], [33]. The application could serve to explore how community detection algorithms can aid in coarse graining the protein structure, to make simulations of larger protein systems at reduced resolution computationally tractable [28]. Finally, it is also possible to integrate the application into Machine Learning workflows, using tools from the Python data-science domain.…”

Section: Discussionmentioning

confidence: 99%

“…To analyze entire trajectories from MD simulations, each frame of the simulation can be translated into a graph representing the underlying protein structure. Such a graph is referred to as an amino acid network, side-chain network, or residue interaction network (RIN) [28]. In a RIN, the nodes of the graph represent the amino acids and the edges represent their interactions.…”

Section: Residue Interaction Network (Rins) For Molecular Dynamics Si...mentioning

confidence: 99%

“…When starting the analysis of a new protein system, it can be difficult to decide which translation algorithm and which network analysis algorithm is best suited to answer the underlying biological question about it [28], [39]. There is not one unique recognized way to best use network analysis for investigating a protein.…”

Section: Residue Interaction Network (Rins) For Molecular Dynamics Si...mentioning

confidence: 99%

“…Other tools for RIN analysis: There are numerous tools available for constructing, analyzing and visualizing RINs (see [28], [29], [58] for detailed overviews). There are plug-ins for established visualization software, such as RINalyzer [59], which ties in with structureViz [60] to link Cytoscape [61] and the molecular structure viewer Chimera [62].…”

Section: Related Workmentioning

confidence: 99%

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Interactive Visualization of Protein RINs using NetworKit in the Cloud

Angriman¹,

Fabian²,

Franke³

et al. 2022

Preprint

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Network analysis has been applied in diverse application domains. In this paper, we consider an example from protein dynamics, specifically residue interaction networks (RINs). In this context, we use NETWORKIT -an established package for network analysis -to build a cloud-based environment that enables domain scientists to run their visualization and analysis workflows on large compute servers, without requiring extensive programming and/or system administration knowledge. To demonstrate the versatility of this approach, we use it to build a custom Jupyter-based widget for RIN visualization. In contrast to existing RIN visualization approaches, our widget can easily be customized through simple modifications of Python code, while both supporting a good feature set and providing near real-time speed. It is also easily integrated into analysis pipelines (e.g., that use Python to feed RIN data into downstream machine learning tasks).

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Section: Discussionmentioning

confidence: 99%

Section: Residue Interaction Network (Rins) For Molecular Dynamics Si...mentioning

confidence: 99%

Section: Residue Interaction Network (Rins) For Molecular Dynamics Si...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Interactive Visualization of Protein RINs using NetworKit in the Cloud

Angriman¹,

Fabian²,

Franke³

et al. 2022

Preprint

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show abstract

“…Once the network is constructed, an analysis of residue-based local or global network metrics including centrality measures can be useful to detect non-evident functions of residues such as relative importances for protein stability (19,30,34–36), allosteric communication between parts of protein (37–42) and other family-specific functions (e.g. catalytics sites in enzymes) (19,43–45).…”

Section: Introductionmentioning

confidence: 99%

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

Aydinkal

Serçinoğlu

Ozbek

2019

Nucleic Acids Research

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ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

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Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

Franke

Peter

2023

J. Chem. Theory Comput.

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Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce lowdimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.

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Understanding the Structure and Dynamics of Peptides and Proteins Through the Lens of Network Science

Cited by 5 publications

References 290 publications

Interactive Visualization of Protein RINs using NetworKit in the Cloud

Interactive Visualization of Protein RINs using NetworKit in the Cloud

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding

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