Weighted protein residue networks based on joint recurrences between residues

Abstract

BackgroundWeighted 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.ResultsThe 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.ConclusionsProtein 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.

The adaptive nature of protein residue networks

“…We generate six PRNs with weighted edges at 150 K, 180 K, 200 K, 220 K, 250 K, and 310 K respectively. The edge weight is equal to the number of joint recurrences for the two corresponding nodes, divided by the geometric distance between them . The and values are calculated at each temperature, using the MatlabBGL network toolbox .…”

“…We have recently introduced PRNs that use joint recurrences, based on molecular dynamics MD simulations, as edge weights . The specific solvated protein system investigated in that work is the 165 residue β‐lactamase inhibitory protein BLIP at 310 K. This protein is secreted by the soil bacterium Streptomyces clavuligerus , and it inhibits β‐lactam enzymes, which hydrolyze β‐lactam antibiotics and nullify their effect .…”

“…A RP multivariate version is used to detect simultaneous recurrences, such as those between residues in a protein. A residue pair with a large number of joint recurrences is assumed to interact strongly, and this is then reflected in the edge weight connecting these two nodes in the PRN …”

“…A residue pair with a large number of joint recurrences is assumed to interact strongly, and this is then reflected in the edge weight connecting these two nodes in the PRN. 14 We hypothesize that the dynamic nature of proteins and its coupling to the surrounding environment affect a PRN. 24 This is based on the fact that the coupling between the protein and its surrounding water plays an essential role in the dynamics of the protein.…”

THz frequency spectrum of protein–solvent interaction energy using a recurrence plot‐based Wiener–Khinchin method

“…8 A quantitative version known as recurrence quantification analysis RQA 9 has found extensive use in various applications, including protein structure and dynamics. [10][11][12] A suitable context to compare the performance of WK and RPWK in the field of protein molecular dynamics simulations is the protein-solvent complex energy landscape, which has long been a topic of intense research due to the importance of the role played by the hydration water on the functioning of proteins. 4,5,[13][14][15] This is especially true for the protein dynamical transition, which occurs at temperatures ranging between 200 and 240 K, and is strongly coupled to the solvent.…”