Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico-chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain-domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π-π stacking and π-cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.

/pdf/the-ring-2-0-web-server-for-high-quality-residue-interaction-t73xgwkbib.pdf

The RING 2.0 web server for high quality residue interaction networks.

We investigated the gene coexpression network in Saccharomyces cerevisiae, in which genes are linked when they are coregulated. This network is shown to have a scale-free, small-world architecture. Such architecture is typical of biological networks in which the nodes are connected when they are involved in the same biological process. Current models for the evolution of intracellular networks do not adequately reproduce the features that we observe in the network. We therefore derive a new model for its evolution based on the observation that there is a positive correlation between the sequence similarity of paralogues and their probability of coexpression or sharing of transcription factor binding sites (TFBSs). The simple, neutralist's model consists of (1) coduplication of genes with their TFBSs, (2) deletion and duplication of individual TFBSs and (3) gene loss. A network is constructed by connecting genes that share multiple TFBSs. Our model reproduces the scale-free, small-world architecture of the coregulation network and the homology relations between coregulated genes without the need for selection either at the level of the network structure or at the level of gene regulation.

The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

/pdf/principles-and-overview-of-sampling-methods-for-modeling-znr1ol4lhn.pdf

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

http://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.5b00631

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

Protein contact network topology: a natural language for allostery.

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

The construction of an amino acid network for understanding protein structure and function.

Intrinsically Disordered Proteins Link Alternative Splicing and Post-translational Modifications to Complex Cell Signaling and Regulation.

Residue interaction network analysis of Dronpa and a DNA clamp.

Intra–molecular interactions within complex systems play a pivotal role in the biological function. They form amajor challenge to computational structural proteomics. The network paradigm treats any system as a set of nodes linkedby edges corresponding to the relations existing between the nodes. It offers a computationally efficient tool to meet thischallenge. Here, we review the recent advances in the use of network theory to study the topology and dynamics of protein–ligand and protein–nucleic acid complexes. The study of protein complexes networks not only involves the topologicalclassification in term of network parameters, but also reveals the consistent picture of intrinsic functional dynamics.Current dynamical analysis focuses on a plethora of functional phenomena: the process of allosteric communication, thebinding induced conformational changes, prediction and identification of binding sites of protein complexes, which willgive insights into intra–protein complexes interactions. Furthermore, such computational results may elucidate a variety ofknown biological processes and experimental data, and thereby demonstrate a huge potential for applications such as drugdesign and functional genomics. Finally we describe some web–based resources for protein complexes, as well as proteinnetwork servers and related bioinformatics tools.

The Topology and Dynamics of Protein Complexes: Insights from Intra– Molecular Network Theory

Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein-protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence-disorder-function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ➔ disorder ensemble ➔ function paradigm.

Jianhong Zhou

Papers

The construction of an amino acid network for understanding protein structure and function.

Intrinsically Disordered Proteins Link Alternative Splicing and Post-translational Modifications to Complex Cell Signaling and Regulation.

Residue interaction network analysis of Dronpa and a DNA clamp.

The Topology and Dynamics of Protein Complexes: Insights from Intra– Molecular Network Theory

Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships