Topic
Interaction network
About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.
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TL;DR: This work presents a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing.
Abstract: UNLABELLED Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress. AVAILABILITY AND IMPLEMENTATION PPIXpress is available at https://sourceforge.net/projects/ppixpress/.
20 citations
TL;DR: A protein-protein interaction network of U. virens was built through two well-recognized approaches, interolog- and domain-domain interaction-based methods, which will provide new perspectives for finely dissecting interactions of genes related to its pathogenicity.
Abstract: Rice false smut, caused by Ustilaginoidea virens, produces significant losses in rice yield and grain quality and has recently emerged as one of the most important rice diseases worldwide. Despite its importance in rice production, relatively few studies have been conducted to illustrate the complex interactome and the pathogenicity gene interactions. Here a protein–protein interaction network of U. virens was built through two well-recognized approaches, interolog- and domain–domain interaction-based methods. A total of 20 217 interactions associated with 3305 proteins were predicted after strict filtering. The reliability of the network was assessed computationally and experimentally. The topology of the interactome network revealed highly connected proteins. A pathogenicity-related subnetwork involving up-regulated genes during early U. virens infection was also constructed, and many novel pathogenicity proteins were predicted in the subnetwork. In addition, we built an interspecies PPI network between...
20 citations
TL;DR: Relevance of P(s) is investigated both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.
Abstract: Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.
20 citations
TL;DR: This article used AlphaFold2 to predict structures for 65,484 human interactions and showed that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data.
Abstract: All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.
20 citations
07 Jan 2007
TL;DR: This work proposes several novel relational features for predicting protein-protein interaction that can be used in any classifier, and shows that it is able to get an accuracy of 81.7% when predicting new links from noisy high throughput data.
Abstract: Motivation: Proteins play a fundamental role in every process within the cell. Understanding how proteins interact, and the functional units they are part of, is important to furthering our knowledge of the entire biological process. There has been a growing amount of work, both experimetal and computational, on determining the protein-protein interaction network. Recently researchers have had success looking at this as a relational learning problem. Results: In this work, we further this investigation, proposing several novel relational features for predicting protein-protein interaction. These features can be used in any classifier. Our approach allows large and complex networks to be analyzed and is an alternative to using more expensive relational methods. We show that we are able to get an accuracy of 81.7% when predicting new links from noisy high throughput data. Contact: licamele@cs.umd.edu
20 citations