Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

A topological measurement for weighted protein interaction network

Abstract: High-throughput methods for detecting protein-protein interactions (PPl) have given researchers an initial global picture of protein interactions on a genomic scale. The usefulness of this understanding is, however, typically compromised by noisy data. The effective way of integrating and using these non-congruent data sets has received little attention to date. This paper proposes a model to integrate different data sets. We construct this model using our prior knowledge of data set reliability. Based on this model, we propose a topological measurement to select reliable interactions and to quantify the similarity between two proteins' interaction profiles. Our measurement exploits the small-world network topological properties of protein interaction network. Meanwhile, we discovered some additional properties of the network. We show that our measurement can be used to find reliable interactions with improved performance and to find protein pairs with higher function homogeneity.

Evaluation of Clustering Algorithms for Protein Complex and Protein Interaction Network Assembly

Abstract: Assembling protein complexes and protein interaction networks from affinity purification-based proteomics data sets remains a challenge. When little a priori knowledge of the complexes exists, it is difficult to place proteins in the proper locations and evaluate the results of clustering approaches. Here we have systematically compared multiple hierarchical and partitioning clustering approaches using a well-characterized but highly complex human protein interaction network data set centered around the conserved AAA+ ATPases Tip49a and Tip49b. This network provides a challenge to clustering algorithms because Tip49a and Tip49b are present in four distinct complexes, the network contains modules, and the network has multiple attachments. We compared the use of binary data, quantitative proteomics data in the form of normalized spectral abundance factors, and the Z-score normalization. In our analysis, a partitioning approach indicated the major modules in a network. Next, while Euclidian distance was sensitive to scaling, with data transformation, all the attachments in a data set were recovered in one branch of a dendrogram. Finally, when Pearson correlation and hierarchical clustering were used, complexes were well separated and their attachments were placed in the proper locations. Each of these three approaches provided distinct information useful for assembly of a network of multiple protein complexes.

Protein interaction evolution from promiscuity to specificity with reduced flexibility in an increasingly complex network.

Abstract: A key question regarding protein evolution is how proteins adapt to the dynamic environment in which they function and how in turn their evolution shapes the protein interaction network. We used extant and resurrected ancestral plant MADS-domain transcription factors to understand how SEPALLATA3, a protein with hub and glue properties, evolved and takes part in network organization. Although the density of dimeric interactions was saturated in the network, many new interactions became mediated by SEPALLATA3 after a whole genome triplication event. By swapping SEPALLATA3 and its ancestors between dimeric networks of different ages, we found that the protein lost the capacity of promiscuous interaction and acquired specificity in evolution. This was accompanied with constraints on conformations through proline residue accumulation, which made the protein less flexible. SHORT VEGETATIVE PHASE on the other hand (non-hub) was able to gain protein-protein interactions due to a C-terminal domain insertion, allowing for a larger interaction interface. These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection. Proteins can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity.

Quantitative analysis of protein interaction network dynamics in yeast.

Abstract: Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.

Dual Path Interaction Network for Video Moment Localization

Abstract: Video moment localization aims to localize a specific moment in a video by a natural language query. Previous works either use alignment information to find out the best-matching candidate (i.e., top-down approach) or use discrimination information to predict the temporal boundaries of the match (i.e., bottom-up approach). Little research has taken both the candidate-level alignment information and frame-level boundary information together and considers the complementarity between them. In this paper, we propose a unified top-down and bottom-up approach called Dual Path Interaction Network (DPIN), where the alignment and discrimination information are closely connected to jointly make the prediction. Our model includes a boundary prediction pathway encoding the frame-level representation and an alignment pathway extracting the candidate-level representation. The two branches of our network predict two complementary but different representations for moment localization. To enforce the consistency and strengthen the connection between the two representations, we propose a semantically conditioned interaction module. The experimental results on three popular benchmarks (i.e., TACoS, Charades-STA, and Activity-Caption) demonstrate that the proposed approach effectively localizes the relevant moment and outperforms the state-of-the-art approaches.