Dataset Information

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.

ABSTRACT: BACKGROUND: The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. RESULTS: To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date. CONCLUSIONS: The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.

SUBMITTER: Sugaya N

PROVIDER: S-EPMC3228542 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.

Sugaya Nobuyoshi N Furuya Toshio T

BMC bioinformatics 20110209

<h4>Background</h4>The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.<h4>Results</h4>To address this need, we have developed the 'Druggable Pro ...[more]

PMID: 21303559

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Project description:BackgroundProtein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data.ResultsIn this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach.ConclusionThis study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application.

Dataset Information

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.

Publications

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.

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