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. 2021 Jun 5:19:3599-3608.
doi: 10.1016/j.csbj.2021.06.004. eCollection 2021.

Protein conformational switch discerned via network centrality properties

David Foutch  1 , Bill Pham  2 , Tongye Shen  2   3
Affiliations

Protein conformational switch discerned via network centrality properties

David Foutch et al. Comput Struct Biotechnol J. .

Abstract

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Keywords: Conformational switch; Network analysis; Network centrality; Protein structure network.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
The connection between protein structures, contact matrices, and networks is illustrated using the ligand-sensing domain of a nuclear hormone receptor CAR (PDBs: 1XLS and 1XNX). In the 3D representation, the regions with major structural changes are colored (green, agonist-bound and purple, inverse agonist-bound). In the network representation of the agonist-bound structure using PSN and PSN*, each node represents an amino acid residue and each edge indicates a contact formed between residues.
Fig. 2
Fig. 2
(a) A pedagogical graph composed of 25 nodes and 27 edges is shown. (b-d) The same graph is used to illustrate different centrality measurements. The darker nodes correspond to the relatively higher values. BC and CC are path-oriented measurements. BC counts the paths (such as red line connecting two ending green circled nodes) that go through a target node (blue circle) while CC focuses on the red paths terminating at the target node. In contrast, DC and EC focus on the number of neighboring nodes of the target node. DC is a direct count of the number of the nearest neighbors (green circles), whereas EC is a weighted version with further neighbors being considered.
Fig. 3
Fig. 3
Network centrality properties of nuclear receptor CAR. (a) The scatter plot of individual residue’s distance to the protein center versus the CC value of the corresponding residue in PSN (left panel) and PSN* (right panel) for the ligand-free protein. The scatter plots for the apo versus agonist-bound CAR systems are shown for BC, CC, DC, and EC in the respective (b-e) panels. In the 3D representation, the top 15% ranked residues are color-coded: yellow indicates residues that are top ranked for the apo system only, blue for the ligand-bound only, and green residues are top ranked for both networks. Additionally, these top ranked residues are indicated by red dashed lines, which separate the scatter plot into four quadrants. The residues in the lower-right, upper-left, and upper-right quadrants are interpreted as yellow, blue, and green residues respectively.
Fig. 4
Fig. 4
Network centrality properties of RNR. (a) The 3D representation of RNR shows the structural differences between the ligand-free form (yellow) vs the ligand-bound form (pink, dTTP at the s-site). The ligand-binding status of the four systems, which we term RNR1-4, are indicated on the right panel. The scatter plots of PSN* comparing RNR1 vs RNR2 for BC, CC, DC, EC are in (b-e), respectively. The top 15% ranked residues are also displayed. (f) The comparison of the top 15% residues for EC values of RNR1 versus RNR2 (top), RNR3 (middle), or RNR4 (bottom). Here, the yellow-blue-green color scheme for the apo only, the ligand-bound only, and both networks is applied similarly to Fig. 3.
Fig. 5
Fig. 5
The overlap function compares how (dis) similar two structural networks are. The normalized number of the common residues between the two networks (y-axis) increases when the selecting criterion (x-axis) is more relaxed, i.e, selecting residues that are the top ranked residues (using the specified centrality value).
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