The interactome: Predicting the protein-protein interactions in cells

Abstract

Abstract:The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

The effect of hyperosmolar stimuli and cyclophosphamide on the culture of normal rat urothelial cells in vitro

“…The mode of action of hyperosmolar saline and cyclophosphamide is distinctive. Protein-protein interactions and different proteases are crucial for most cellular processes, cell motility and apoptosis [18,19]. In the case of hyperosmolar solution, the urothelial cells may die as a consequence of dehydration, caused by changes in molecular protein-protein interaction and nucleic acid structure.…”

PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

“…The novelty of this work lies in the use of two unique features, namely domain frequency, and protein pair interaction affinity values. We consider all possible combinations of constituent domains between two proteins; therefore our method can be applied not only for single-domain proteins, but also multi-domain protein-protein interaction, which is frequently observed in real biological data [26]. Not only considering all possible interactions between constituent domains of interacting protein pairs but also determining interaction affinity between a protein pair by distinguishing the dominant interacting domain pair is also significant in this work.…”

“…Following the Brainstorming approach [27], we achieved considerable predictive accuracy with the use of these features without using any probabilistic computation. It dominates over classical tools such as PID, or PPI by Zaki et al Moreover, we are able to provide a list of highly interacting domain pairs, which is useful to form a domain interaction map to be further used in order to predict PPI [26]. Although the proposed domain combination based prediction method certainly improves the prediction accuracy of the conventional domain based prediction method, it has limitations.…”

Protein-protein Interactions: Network Analysis and Applications in Drug Discovery

“…for mapping binary interactions [66,67], biochemical technologies to identify (in)directly interacting components of protein complexes [68], curation of published low-throughput experiments [69][70][71][72][73][74][75], or computational predictions using orthogonal information such as sequence similarity, gene-order conservation, phylogenetic profiling and expression data, which is exponentially growing since the advent of next generation sequencing (NGS) [76,77]. A list of online available tools to explore interactomics maps can be found in Table 1 [75], the Molecular INTeraction database (MINT) [70], the protein InterAction database (IntAct) [71,85], the Biological General Repository for Interaction Datasets (BioGRID) [86,87], and the Human Protein Reference Database (HPRD) [72,88].…”