ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

Abstract

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Rational design of allosteric modulators: Challenges and successes

“…In graph theory, proteins are represented by an atomistic graph network model, where nodes are atoms and weighted edges are the covalent and non-covalent bonds, 123 or by a coarse-grained network model, which is the most usual case, where nodes are residue Cα atoms, or residue center of mass, connected with edges based on a distance cutoff maintained in the MD trajec-tory at a certain percent (e.g., 75% of the MD trajectory) 108,124 or based on other interresidue properties. 125,126 The edge weights are usually based on the covariance matrix of the displacement of Cα atoms, or from mutual information analysis from MD simulations, and the allosteric hot spots and communities are identified using centrality measures, for example, betweenness centrality, eigenvector centrality and other metrics. 124,[127][128][129][130][131][132] The allosteric pathway is then detected using the Dijkstra algorithm, 133 or the Bellman-Ford algorithm, 134 which finds the shortest path between two nodes in a graph.…”

“…Graph theory has been also employed, which provides a way of representing proteins in a reduced form, identifying allosteric pathways from the dynamic couplings between the residues of the orthosteric and the allosteric sites. In graph theory, proteins are represented by an atomistic graph network model, where nodes are atoms and weighted edges are the covalent and noncovalent bonds, 123 or by a coarse‐grained network model, which is the most usual case, where nodes are residue Cα atoms, or residue center of mass, connected with edges based on a distance cutoff maintained in the MD trajectory at a certain percent (e.g., 75% of the MD trajectory) 108,124 or based on other inter‐residue properties 125,126 . The edge weights are usually based on the covariance matrix of the displacement of Cα atoms, or from mutual information analysis from MD simulations, and the allosteric hot spots and communities are identified using centrality measures, for example, betweenness centrality, eigenvector centrality, and other metrics 124,127–132 .…”

The Bio3D packages for structural bioinformatics

“…A number of previously implemented software solutions (including multiple web servers [31][32][33][34][35] and standalone software packages [36][37][38][39][40][41][42] ) offer related solutions including single structure NMA, MD or protein structure-based network analysis. However, these typically lack extensive coupling to major biomolecular databases and methods for evolutionary and comparative analysis intrinsic to Bio3D.…”

Web-based tools for computational enzyme design

“…ProSNEx [27] [*] models inter-residue interaction networks from the input 3D coordinates of the protein to be studied. Such contacts are weighted according to dynamical cross-correlation maps either obtained from elastic network models or other normal mode applications, the graph theory based spectral clustering of side chains, or molecular dynamic simulations derived energies.…”