Centrality measures and H-bond clustering in proteins
Description
Dataset includes : 1. MATLAB workflow to compute and plot centrality measures in protein structures, 2. Tcl script to visualize centrality measures in protein structures in VMD, 3. Matlab workflow to compute H-bond clusters in protein structures, 4. Tcl script to visualize clusters in protein structures in VMD, 5. Folder sample_folder contains output results after running the scripts in folders centrality_measures & hbond_clusters for SARS-CoV-2 spike glycoprotein in closed conformation (PDB ID: 6VXX). Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3. Guidelines for running the scripts are in README text file in the analysis_code folder. "When using these scripts, please cite: Karathanou, K., Lazaratos, M., Bertalan, É., Siemers, M., Buzar, K., Schertler, G.F., Del Val, C. and Bondar, A.N., 2020. A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2. Journal of structural biology, p.107617." ################################################################################################# Betweenness & Degree centrality measures: The Betweenness Centrality (BC) of a node ni gives the number of shortest-distance paths between any two other nodes nj and nk that pass via node ni divided by the total number of shortest paths that connect nj and nk irrespective of whether they pass via node ni. The normalized BC value of node ni is computed by dividing its BC by the number of pairs of nodes not including ni. The Degree Centrality (DC) of a node ni gives the number of edges of the node. The normalized DC value of node ni is computed by dividing its DC by the maximum possible edges to ni (which is N-1, where N is the number of nodes in the graph). References: Freeman LC: A set of measures of centrality based on betweenness. Sociometry 1977, 40:35-41. Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239. Brandes U: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 2001, 25:163-177. ################################################################################################# The Connected Component search gives a sub-graph of H bonds, in which at least two nodes are connected to each other by H-bond pathways and no other nodes are connected in the sub-graph. We denote those sub-graphs as H-bond clusters. The cluster size is given by the total number of nodes (H-bonding amino-acid residues) of each cluster. References: Cormen TH, Leiserson CE, Rivest RL, Sten C (2009). Introduction to algorithms, 3rd edn. Massachusetts Institute of Technology