Protein–protein interactions and genetic diseases: The interactome

Lage, Kasper

doi:10.1016/j.bbadis.2014.05.028

Cited by 121 publications

(109 citation statements)

References 117 publications

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“…Thus, integrating protein interaction data with WGS and WES data can provide new insights into important diseases, including autism, type 2 diabetes 33 and heart disease 34 (additionally reviewed in REF. 35). …”

Section: Genetic Architecture Of Common Diseasementioning

confidence: 99%

Integrative omics for health and disease

2018

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Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

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Section: Genetic Architecture Of Common Diseasementioning

confidence: 99%

Integrative omics for health and disease

2018

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“…The functional analysis of genes within complexes suggests that these complexes could be hotspots for perturbations due to genetic or environmental factors, thereby driving common and rare diseases [137,138].…”

Section: Identifying Complexes In Human Diseasesmentioning

confidence: 99%

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

et al. 2015

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Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub-complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

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“…2-4 These versatile roles make PPIs attractive for therapeutic intervention and rational drug design. 5-7 Different classes of the approved therapeutic agents or those in development stages have been shown to interfere with PPIs in order to overcome the corresponding diseases.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the hotspots involved in RON-MSPβ complex formation using in silico alanine scanning mutagenesis and molecular dynamics simulation

Zarei¹,

Hamzeh‐Mivehroud²,

Benvenuti³

et al. 2017

Adv Pharm Bull

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Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d’Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in protein-protein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln193, Arg220, Glu287, Pro288, Glu289, and His424 residues from RON and Arg521, His528, Ser565, Glu658, and Arg683 from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required, and the design of experimental site directed mutagenesis studies.

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Protein–protein interactions and genetic diseases: The interactome

Cited by 121 publications

References 117 publications

Integrative omics for health and disease

Integrative omics for health and disease

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Characterizing the hotspots involved in RON-MSPβ complex formation using in silico alanine scanning mutagenesis and molecular dynamics simulation

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