Topic
Structural communication
About: Structural communication is a research topic. Over the lifetime, 23 publications have been published within this topic receiving 558 citations.
Papers
TL;DR: The relevant parts of the methodology are summarized and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks.
Abstract: Communication within and across proteins is crucial for the biological functioning of proteins. Experiments such as mutational studies on proteins provide important information on the amino acids, which are crucial for their function. However, the protein structures are complex
and it is unlikely that the entire responsibility of the function rests on only a few amino acids. A large fraction of the protein is expected to participate in its function at some level or other. Thus, it is relevant to consider the protein structures as a completely connected network and then deduce the properties, which are related to the global network features. In this direction, our laboratory has been engaged in representing the protein structure as a network of non-covalent connections and we have investigated a variety of problems in structural biology, such as the identification of functional and folding clusters, determinants of quaternary association and
characterization of the network properties of protein structures. We have also addressed a few important issues related to protein dynamics, such as the process of oligomerization in multimers, mechanism on protein folding, and ligand induced communications (allosteric effect).
In this review we highlight some of the investigations which we have carried out in the recent past. A review on protein structure graphs was presented earlier, in which
the focus was on the graphs and graph spectral properties and their implementation in the study of protein structure graphs/networks (PSN). In this article, we briefly summarize the relevant parts of the methodology and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks. The investigations of structural/biological problems are divided into two parts, in which the first part deals with the analysis of PSNs based on static structures obtained from x-ray crystallography. The second part highlights the changes in the network, associated with biological functions, which are deduced from the network analysis on the structures obtained from molecular dynamics simulations.
178 citations
TL;DR: PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions and allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs.
Abstract: In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory. PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions. PyInteraph also allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs, along with performing network analysis on the resulting interaction graphs. The program also integrates the network description with a knowledge-based ...
111 citations
TL;DR: Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems, and new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems.
Abstract: A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.
73 citations
TL;DR: Protein structure network analysis on wild-type LHR and two constitutively active mutants, combined with in vitro mutational analysis, served to identify key amino acids that are part of the regulatory network responsible for propagating communication between the extracellular and intracellular poles of the receptor.
Abstract: The luteinizing hormone receptor (LHR) is a G protein-coupled receptor (GPCR) particularly susceptible to spontaneous pathogenic gain-of-function mutations. Protein structure network (PSN) analysis on wild-type LHR and two constitutively active mutants, combined with in vitro mutational analysis, served to identify key amino acids that are part of the regulatory network responsible for propagating communication between the extracellular and intracellular poles of the receptor. Highly conserved amino acids in the rhodopsin family GPCRs participate in the protein structural stability as network hubs in both the inactive and active states. Moreover, they behave as the most recurrent nodes in the communication paths between the extracellular and intracellular sides in both functional states with emphasis on the active one. In this respect, non-conservative loss-of-function mutations of these amino acids is expected to impair the most relevant way of communication between activating mutation sites or hormone-binding domain and G protein recognition regions.
59 citations
TL;DR: A mixed Protein Structure Network and Elastic Network Model-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems, validated on the PDZ2 domain and able to reproduce the salient communication features of unbound and bound PTP1E inferred from molecular dynamics simulations.
Abstract: Graph theory is being increasingly used to study the structural communication in biomolecular systems. This requires incorporating information on the system's dynamics, which is time-consuming and not suitable for high-throughput investigations. We propose a mixed Protein Structure Network (PSN) and Elastic Network Model (ENM)-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems. PSN analysis and ENM-Normal Mode Analysis (ENM-NMA) are implemented in the structural analysis software Wordom, freely available at http://wordom.sourceforge.net/ . The method performs a systematic search of the shortest communication pathways that traverse a protein structure. A number of strategies to compare the structure networks of a protein in different functional states and to get a global picture of communication pathways are presented as well. The approach was validated on the PDZ2 domain from tyrosine phosphatase 1E (PTP1E) in its free (APO) and peptide-bound states. PDZ domains are, indeed, the systems whose structural communication and allosteric features are best characterized both in vitro and in silico. The agreement between predictions by the PSN-ENM method and in vitro evidence is remarkable and comparable to or higher than that reached by more time-consuming computational approaches tested on the same biological system. Finally, the PSN-ENM method was able to reproduce the salient communication features of unbound and bound PTP1E inferred from molecular dynamics simulations. High speed makes this method suitable for high throughput investigation of the communication pathways in large sets of biomolecular systems in different functional states.
53 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 3 |
| 2019 | 2 |
| 2017 | 2 |
| 2016 | 1 |
| 2015 | 1 |
| 2014 | 1 |