Domain‐mediated protein interaction prediction: From genome to network

Reimand, Jüri; Hui, Shirley; Jain, Shobhit; Law, Brian K.; Bader, Gary D.

doi:10.1016/j.febslet.2012.04.027

Cited by 43 publications

(26 citation statements)

References 160 publications

(194 reference statements)

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“…PDZ domains are typically 80-90 amino acids in length, containing 5-6 β strands and 2 α helices. The second β strand, second α helix and a GLGF loop of the PDZ domains collectively form the binding pocket, which recognizes the hydrophobic C-terminal peptide of a target protein [9, 10]. Albeit many PDZ domains preferentially recognize the C-terminal tails of their target proteins, other interaction patterns have also been described [3, 11].…”

Section: Introductionmentioning

confidence: 99%

GWAS and drug targets

Cao

Moult

2014

BMC Genomics

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BackgroundGenome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets.ResultsWe find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities.ConclusionsAlthough GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.

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Section: Introductionmentioning

confidence: 99%

GWAS and drug targets

Cao

Moult

2014

BMC Genomics

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“…The central idea behind our approach is that proteins interact with each other through domains. The domain interaction provide framework for prediction model of PPI [52]. The validity of this assumption stems from the fact that evolutionarily conserved polypeptide domains characterize structural building blocks regulating the functionality of proteins.…”

Section: D) Basis Of Domain-domain Interactionmentioning

confidence: 99%

Protein-protein interaction network prediction using stochastic learning automata induced differential evolution

Chowdhury

Rakshit

Konar

2016

Applied Soft Computing

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“…Computational methods for predicting these interactions, which are of particular importance for cellular signalling 4 , have recently been reviewed elsewhere 97 .…”

Section: Computational Approachesmentioning

confidence: 99%

High-resolution network biology: connecting sequence with function

et al. 2013

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Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein– protein, genetic and drug–gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

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Domain‐mediated protein interaction prediction: From genome to network

Cited by 43 publications

References 160 publications

GWAS and drug targets

GWAS and drug targets

Protein-protein interaction network prediction using stochastic learning automata induced differential evolution

High-resolution network biology: connecting sequence with function

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