Cataloging the Relationships Between Proteins: A Review of Interaction Databases

Rohl, Carol A.; Price, Yancey; Fischer, Tiffany B.; Paczkowski, Melissa; Zettel, Michael F.; Tsai, Jerry

doi:10.1385/mb:34:1:69

Cited by 6 publications

(4 citation statements)

References 71 publications

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“…To date, hundreds of thousands of PPIs have been stored in these databases that cover hundreds of different organisms and contain interactions determined by tens of different methods (27,28). …”

Section: Introductionmentioning

confidence: 99%

PrePPI: a structure-informed database of protein–protein interactions

Zhang

Petrey

Garzón

et al. 2012

Nucleic Acids Research

224

205

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PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein–protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability > 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.

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Section: Introductionmentioning

confidence: 99%

PrePPI: a structure-informed database of protein–protein interactions

Zhang

Petrey

Garzón

et al. 2012

Nucleic Acids Research

224

205

View full text Add to dashboard Cite

show abstract

“…Networks may be constructed from many kinds of data, including, but not limited to, protein-protein interactions, transcriptional co-regulation, putative microRNA targets, or participation in annotated biological pathways [16], [18], [25]. Databases of such interactions exist for many species including yeast, worm, fly, mouse, and human [26]. Often, genes that serve essential cell functions are more connected than others and genes that contribute to a particular phenotype are more interconnected than would be expected by chance [19], [22], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Networks may be enriched by the integration of information from diverse species using homology as a means to overlay species-specific findings [26]. This technique could be applicable to aging because LAGs are highly conserved across species [7], [22], [28].…”

Section: Introductionmentioning

confidence: 99%

Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

et al. 2012

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Intricate and interconnected pathways modulate longevity, but screens to identify the components of these pathways have not been saturating. Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans. We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes. We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators.

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“…All these methods can help identify a smaller, druggable, and hot spot-containing sub-region of the interface, even if there is no open binding pocket detectable in the unbound state of the receptor [176]. Information about experimentally determined hot spots are available in several databases [57,[177][178][179][180][181], although the coverage is low when compared to the number of PPIs considered to be interesting drug targets.…”

Section: Hot Spot Detectionmentioning

confidence: 99%

Modulating Protein-Protein Interactions: From Structural Determinants of Binding to Druggability Prediction to Application

Metz¹,

Ciglia²,

Gohlke³

2012

CPD

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During the last decades, a large amount of evidence has been gathered on the importance of protein-protein interactions in tuning and regulating most important biological processes. Since many of these pathways are deeply involved in diseases, extensive research efforts have been undertaken towards the modulation of protein-protein interactions. At the early stage of this challenge most of the attention was drawn to the drawbacks of such a therapeutic approach. Encouragingly, however, several recent studies provided a proof of concept that protein-protein interactions are actually valuable targets and that they do have a promising therapeutic potential. This review is divided into three sections. In the first section we summarize the general features of protein-protein interfaces, focusing on the characteristics that make them different from classical protein-ligand binding sites, as well as on problematic aspects that hamper the application of classical drug discovery approaches. In the second section, we present how some of the characteristics of protein-protein interactions can be exploited fruitfully in drug design, hence focusing on the druggability of protein-protein interfaces. Methods successfully applied to protein-protein interactions will be introduced, giving special attention to the computational ones. In the third section, three case studies are presented. First, we describe protein-protein interaction modulators targeting HDM2 and the computational methods applied to identify them. Next, we present the retrospective application of the discussed approaches on the well-examined target IL-2. We conclude with a prospective application to the NHR2 protein, a target just recently validated experimentally with the aid of computational methods.

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Cataloging the Relationships Between Proteins: A Review of Interaction Databases

Cited by 6 publications

References 71 publications

PrePPI: a structure-informed database of protein–protein interactions

PrePPI: a structure-informed database of protein–protein interactions

Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

Modulating Protein-Protein Interactions: From Structural Determinants of Binding to Druggability Prediction to Application

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