Computational protocol: Network topology of NaV1.7 mutations in sodium channel-related painful disorders

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Protocol publication

[…] NaV1.7 structures were transformed into mathematical graphs by identifying interatomic bonds between the amino acids. The amino acid residues form the nodes and inter-node contact interaction form the edges of the graph (Fig. ). We identified the interatomic bonds (hydrophobic, hydrogen bonds, salt-bridges, cation-π and π-π stacking interactions) between two residues i and j as long as the atom-atom distance between them was less than 5.0 Å using the commands “ListIntAtom” and “ListIntBo” via YASARA software (Yet Another Scientific Artificial Reality Application, Hydrophobic contacts between residues were considered in the following atom groups: (a) the first carbon of CH3-, -CH2- and CHC3 (b) sp2 carbons (phenolic rings). π-π stacking were considered between (a) sp2 carbons with a hydrogen and (b) carbon, nitrogen, oxygen or sulphur atoms in planar phenolic rings. Cation-π formation was considered to be a π-π contact with the difference being that one of the interaction partners is a cation. The de novo network construction for each mutant and WT models is achieved considering the predicted binary interatomic bonds identified through YASARA software. [...] We computed some of the most well-known network centrality measures for each mutant and WT network NaV1.7 graph using the Cytoscape plugin NetworkAnalyzer [], namely:Betweenness Centrality (Bct) and edge Betweenness centrality (EBct): Bct [] is defined as the fraction of shortest pathways between all pairs of nodes of the network that go through that node. Let G = (N, E) a graph, where N is the set of the nodes and E is the set of the edges. For each node n and m in N, let d (n, m) the distance between n and m. We define1Betweennesscentralityn=∑s≠n≠tσstnσst,where s, t ∈N, σst (n) is the number of shortest paths from s to t that n lies on, and σst denotes the number of shortest paths from s to t. It accounts the importance of a node facilitating interactions between other nodes. For example, a node with high Bct can operate as a bridge on many shortest paths between other nodes in the network. It is a measure of how powerful a node is able to transfer (high Bct) or interrupt (low Bct) the spread of information on the fastest connection between two nodes. Similarly, the EBct of an edge is the number of shortest paths between pairs of nodes that run along it. We define:2EdgeBetweennesse=∑ni∈N∑nj∈N\ni∑σninjeσninj, Where N = set of nodes; E = set of edges; σninj = number of shortest paths between ni and nj; σninje = number of shortest paths between ni and nj which pass through e ∊ E;Degree (D): D [] of a node (k) is defined as the total number of nodes that it is directly connected to;Clustering Coefficient (CCct): Clustering Coefficient [] is a metric commonly employed to identify well-connected sub-components in network which represents the interconnectivity of neighbors of the node. It measures the degree to which nodes tend to cluster together and is defined as the fraction of triangles around a node among the total number of possible triangles. We define3ClusteringCoefficientn=2enknkn−1,where kn is the number of neighbors of n and en is the number of connected pairs between all neighbors of n;Closeness centrality (Cct): Cct is defined as the sum of the inverted distances, i.e. farness, to all other nodes in the graph. It captures the basic intuition that the closer a node is to all other nodes in terms of path length, the more important it is. Mathematically, Cct of a node n is defined as the inverse of the sum of shortest paths from n to all other nodes m in network. We define4Closenessn=1averagedn,m Eccentricity (Ect): Ect measures the distance between a node n and the most distance node m; if the Ect of the node n is low, this means that all other nodes are in proximity whereas a high Ect means that there is at least one node (and all its neighbors) that is far from node n. We define Ect maximum non-infinite length of a shortest path between n and another node in the network. We define5Eccentricityn=maxdn,m:m∈N […]

Pipeline specifications

Software tools YASARA, NetworkAnalyzer
Applications Drug design, Protein interaction analysis
Organisms Homo sapiens
Chemicals Amino Acids, Sodium