title: Weighted protein residue networks based on joint recurrences between residues.
abstract: BACKGROUND
  Weighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.
  
  
  RESULTS
  The joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein.
  
  
  CONCLUSIONS
  Protein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.
authors.affiliation: Department of Physics, Birzeit University, Birzeit, Palestine. wqaran@birzeit.edu.
authors.forename: Wael I
authors.initials: WI
authors.lastname: Karain
authors.affiliation: Department of Computer Science, Birzeit University, Birzeit, Palestine. nqaraeen@birzeit.edu.
authors.forename: Nael I
authors.initials: NI
authors.lastname: Qaraeen
mesh_terms: D001426:Bacterial Proteins / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D056004:Molecular Dynamics Simulation; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D012997:Solvents / Q000737:chemistry*; D001618:beta-Lactamases / Q000737:chemistry*
chemical_list: D001426:Bacterial Proteins; D012997:Solvents; C065596:beta-lactamase-inhibitor protein, Streptomyces; D001618:beta-Lactamases