Protein Interaction Networks In Health And Disease

The identification and mapping of protein-protein interactions (PPIs) is a major goal in systems biology. Experimental data are currently produced in large scale using a variety of high-throughput assays in yeast or mammalian systems. Analysis of these data using computational tools leads to the construction of large protein interaction networks, which help researchers identify novel protein functions. However, our current view of protein interaction networks is still limited and there is an active field of research trying to further develop this concept to include important processes: the topology of interactions and their changes in real time, the effects of competition for binding to the same protein region, PPI variation due to alternative splicing or post-translational modifications, etc. In particular, a clinically relevant topic for development of the concept of protein interactions networks is the consideration of mutant isoforms, which may be responsible for a pathological condition. Mutations in proteins may result in loss of normal interactions and appearance of novel abnormal interactions that may affect a protein’s function and biological cycle. This Research Topic presents novel findings and recent achievements in the field of protein interaction networks with a focus on disease. Authors describe methods for the identification and quantification of PPIs, the annotation and analysis of networks, considering PPIs and protein complexes formed by mutant proteins associated with pathological conditions or genetic diseases.

The identification and mapping of protein-protein interactions (PPIs) is a major goal in systems biology. Experimental data are currently produced in large scale using a variety of high-throughput assays in yeast or mammalian systems. Analysis of these data using computational tools leads to the construction of large protein interaction networks, which help researchers identify novel protein functions. However, our current view of protein interaction networks is still limited and there is an active field of research trying to further develop this concept to include important processes: the topology of interactions and their changes in real time, the effects of competition for binding to the same protein region, PPI variation due to alternative splicing or post-translational modifications, etc. In particular, a clinically relevant topic for development of the concept of protein interactions networks is the consideration of mutant isoforms, which may be responsible for a pathological condition. Mutations in proteins may result in loss of normal interactions and appearance of novel abnormal interactions that may affect a protein's function and biological cycle. This Research Topic presents novel findings and recent achievements in the field of protein interaction networks with a focus on disease. Authors describe methods for the identification and quantification of PPIs, the annotation and analysis of networks, considering PPIs and protein complexes formed by mutant proteins associated with pathological conditions or genetic diseases.

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 P450 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). Genetic variations may impact such interactions by changing the splicing and/or amino acid sequence which in turn may impact protein topology, localization, post translational modifications and three dimensional structure. 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. Of particular interest are papers dealing with translational research describing the role of novel genetic variations altering protein-protein interaction. Authors may submit original articles, reviews and opinion or hypothesis papers dealing with the role of protein-protein interactions in health and disease. Potential topics include, but are not limited to: • Role of protein-protein interactions in xenobiotic metabolism by cytochrome P450s and other drug metabolism enzymes. • Role of classical and novel interaction partners for cytochrome P450-dependent metabolism which may include interactions with redox partners, interactions with other P450 enzymes to form P450 dimers/multimers, P450-UGT interactions and proteins involved in posttranslational modification of P450s. • Effect of genetic variations (mutations and polymorphisms) on metabolism affected by protein-protein interactions. • Structural implications of mutations and polymorphisms on protein-protein interactions. • Functional characterization of protein-protein interactions. • Analysis of protein-protein interaction networks in health and disease. • Regulatory mechanisms governing metabolic processes based on protein-protein interactions. • Experimental approaches for identification of new protein-protein interactions including changes caused by mutations and polymorphisms.

This volume explores techniques that study interactions between proteins in different species, and combines them with context-specific data, analysis of omics datasets, and assembles individual interactions into higher-order semantic units, i.e., protein complexes and functional modules. The chapters in this book cover computational methods that solve diverse tasks such as the prediction of functional protein-protein interactions; the alignment-based comparison of interaction networks by SANA; using the RaptorX-ComplexContact webserver to predict inter-protein residue-residue contacts; the docking of alternative confirmations of proteins participating in binary interactions and the visually-guided selection of a docking model using COZOID; the detection of novel functional units by KeyPathwayMiner and how PathClass can use such de novo pathways to classify breast cancer subtypes. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary hardware- and software, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Protein-Protein Interaction Networks: Methods and Protocols is a valuable resource for both novice and expert researchers who are interested in learning more about this evolving field.

The Autoimmune Diseases comprehensively describes the clinical expressions of all known autoimmune diseases, as well as the experimental bases of autoimmunity and failure of tolerance. The scientific chapters include mechanisms of natural tolerance, the genetic basis of autoimmunity, the significance of apoptosis, the influence of cytokines, environmental influences, and experimental models. The clinical chapters cover autoimmune endocrine deficiencies, insulin-dependent diabetes, rheumatic disorders, neurological diseases, and diseases of the blood, skin, eye, kidney, and liver.

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This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences. ​