The Human Plasma Membrane Peripherome: Visualization and Analysis of Interactions

A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with non-covalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.Comment: 39 pages, 5 figures, 3 supplement figures, under review in BMR

The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.</p

The amyloid interactome: Exploring protein aggregation

<div><p>Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.</p></div

The dataset of 28 proteins related to in vivo amyloid fibril formation.

<p>The dataset of 28 proteins related to in vivo amyloid fibril formation.</p

The amyloid interactome.

<p>Interaction data for the creation of this network were gathered from the publicly available database IntAct [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.ref045" target="_blank">45</a>] and Cytoscape [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.ref051" target="_blank">51</a>] was used as a visualization tool (Interactive network available at <a href="http://83.212.109.111/amyloid_interactome" target="_blank">http://83.212.109.111/amyloid_interactome</a>). The network consists of 353 nodes and 1178 edges. Proteins are depicted as nodes and interactions as edges. Red-coloured nodes represent known <i>in vivo</i> amyloidogenic proteins, whereas yellow-coloured nodes represent <i>in vitro</i> amyloid-forming proteins or proteins related to amyloid fibril formation (see also Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.t001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.t002" target="_blank">2</a>). Green-coloured nodes are proteins, listed as other interaction partners. Hubs and bottlenecks are depicted as triangles (▲) and squares (■), respectively. Protein-nodes, which are both hubs and bottlenecks are shown as diamonds (◆). Important molecular chaperones are highlighted with a blue outline.</p

Clustering analysis of the amyloid interactome.

<p>The 11 clusters with 3 or more nodes of the amyloid interaction network, derived utilizing the MCL algorithm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.ref097" target="_blank">97</a>]. Cytoscape [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.ref051" target="_blank">51</a>] was used as a visualization tool. The visual legend summarizes the shortcuts of node colour and node shape (See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.g001" target="_blank">Fig 1</a>). The highly connected subnetwork of the first cluster within the amyloid interactome reveals the strong affinity between 7 amyloidogenic proteins (cluster 1—red nodes) and the integral representation of the proteins presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173163#pone.0173163.t002" target="_blank">Table 2</a> (cluster 1—yellow nodes) (Interactive cluster subnetworks available at <a href="http://83.212.109.111/amyloid_interactome" target="_blank">http://83.212.109.111/amyloid_interactome</a>).</p

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp

Subnetworks of molecular chaperones participating in the amyloid interactome.

<p>3 important subnetworks were isolated from the entire amyloid interactome: (A) Subnetwork of Hsp90 co-chaperone Cdc37, Hsc70-interacting protein, Hsp 90-alpha, Hsc71 and their first neighbors, (B) Subnetwork of Serum albumin and Hsc70-interacting protein and their first neighbors and (C) Subnetwork of Clusterin, Large proline-rich protein BAG6 and their first neighbors. The aforementioned proteins, having chaperone or co-chaperone activity, were found to play a pivotal role in the integrity of the interactome (See section Network Analysis Based on Graph Theory). A highly selective and direct correlation of Serum albumin and 6 amyloidogenic proteins was observed (B), whereas indirect interactions between Serum albumin and 2 amyloidogenic proteins were recorded (A). Hsc70-interacting protein is a significant element of the interactome, since it conciliates interactions between Apolipoproteins and ACys or ATTR (A,B). Clusterin synergistically with Large proline-rich protein BAG6 interferes with APrp and Aβ2M (C). The finding that more than one chaperones mediate the interconnection between different amyloidogenic proteins deserves further investigation.</p