Email: Paolo Marcatili - paolo. Background: Protein-protein interactions are at the basis of most cellular processes and crucial for many bio-technological applications. During the last few years the development of high-throughput technologies has produced several large-scale protein-protein interaction data sets for various organisms. It is important to develop tools for dissecting their content and analyse the information they embed by data-integration and computational methods. Results: Interactions can be mediated by the presence of specific features, such as motifs, surface patches and domains. The co-occurrence of these features on proteins interacting with the same protein can indicate mutually exclusive interactions and, therefore, can be used for inferring the involvement of the proteins in common biological processes.
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Protein Expression and Purification - Journal - Elsevier
Genetic variations may change the structure and function of individual proteins as well as affect their interactions with other proteins and thereby impact metabolic processes dependent on protein-protein interactions. For example, cytochrome P proteins, which metabolize a vast array of drugs, steroids and For example, cytochrome P proteins, which metabolize a vast array of drugs, steroids and other xenobiotics, are dependent on interactions with redox and allosteric partner proteins for their localization, stability, catalytic function and metabolic diversity reactions. More generally, research on single gene defects and their role in disease, as well as recent large scale sequencing studies suggest that a large number of genetic variations may contribute to disease not only by affecting gene function or expression but also by modulating complex protein interaction networks. The aim of this research topic is to bring together researchers working in the area of drug, steroid and xenobiotic metabolism who are studying protein-protein interactions, to describe their recent advances in the field. We are aiming for a comprehensive analysis of the subject from different approaches including genetics, proteomics, transcriptomics, structural biology, biochemistry and pharmacology.
NanoBiT® PPI Starter Systems
Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies GWAS. Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms SNPs in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction PPI networks have been used to define gene pairs for the gene-gene interaction tests.
Metrics details. The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions.