Network approach for capturing ligand-induced subtle global changes in protein structures

Abstract

Ligand-induced conformational changes in proteins are of immense functional relevance. It is a major challenge to elucidate the network of amino acids that are responsible for the percolation of ligand-induced conformational changes to distal regions in the protein from a global perspective. Functionally important subtle conformational changes (at the level of side-chain noncovalent interactions) upon ligand binding or as a result of environmental variations are also elusive in conventional studies such as those using root-mean-square deviations (r.m.s.d.s). In this article, the network representation of protein structures and their analyses provides an efficient tool to capture these variations (both drastic and subtle) in atomistic detail in a global milieu. A generalized graph theoretical metric, using network parameters such as cliques and/or communities, is used to determine similarities or differences between structures in a rigorous manner. The ligand-induced global rewiring in the protein structures is also quantified in terms of network parameters. Thus, a judicious use of graph theory in the context of protein structures can provide meaningful insights into global structural reorganizations upon perturbation and can also be helpful for rigorous structural comparison. Data sets for the present study include high-resolution crystal structures of serine proteases from the S1A family and are probed to quantify the ligand-induced subtle structural variations.

Interaction Signatures Stabilizing the NAD(P)-Binding Rossmann Fold: A Structure Network Approach

“…We have recently shown that a comparison of cliques of non-covalent connections is a good metric to quantify structural similarity/dissimilarity [32]. In another study, we have shown that clique percolation is a unique property of a PSN [19].…”

“…The details of the construction of such a graph at a particular interaction cut-off (I min ) and the implications of such graphs have been previously discussed in detail [32], [40]. Protein structure networks are constructed by considering amino acid residues as nodes and edges are constructed between the nodes on the basis of non-covalent interactions between them (as evaluated from the normalized number of contacts between them) for each system.…”

Mismatch binding, ADP–ATP exchange and intramolecular signaling during mismatch repair

“…One approach in the works is to define the amino acid network quantitatively based on non-covalent interaction energies between individual residues and use that data to identify centers of high connectivity, in terms of numbers and/or strength of interactions, across the protein and over time (persistence over nanosecond scale MD ensembles). Such information can yield potential paths of communication and identify residues with significant roles as “nodes” or “hubs” for transmitting information (Bhattacharyya M and Vishveshwara S, unpublished data) [65,66]. These could be experimentally tested by mutational analysis to assess their contribution to the mechanism coupling the DNA binding/mismatch recognition and ATPase activities of MutS.…”

Network properties of protein-decoy structures

“…Details of construction and analysis of PSNs are given elsewhere (Bhattacharyya, Ghosh, Hansia, & Vishveshwara, 2010;Kannan & Vishveshwara, 1999;Sukhwal et al, 2011) and a brief description is given here. The amino acid residues in the structure, with all its sidechain atoms, are considered as 'nodes' and the noncovalent interactions between them are considered as 'edges' for the construction of PSNs from an all-atom perspective.…”

“…This approach goes beyond residue level or pair interaction level and provides a global perspective. It has been an attractive model for a number of investigations ranging from the identification of groups of interacting residues important for folding and function, to the characterization of general network properties of protein structures (Kannan & Vishveshwara, 1999;Sukhwal, Bhattacharyya, & Vishveshwara, 2011). The network analysis of non-covalent interactions of amino acid side-chains in proteins and a comparison with the interaction distributions to those derived from random distributions, in the large percolating unit, has shown the existence of statistical distribution (Brinda, Vishveshwara, & Vishveshwara, 2010), a result that is complementary to the work presented on the basis of backbone Cα analysis (Mittal et al, 2010).…”