Results 1 - 10
of
47
Identifying network of drug mode of action by gene expression profiling
- J. Comput. Biol
, 2009
"... Drug mode of action (MOA) of novel compounds has been predicted using phenotypic features or, more recently, comparing side effect similarities. Attempts to use gene expres-sion data in mammalian systems have so far met limited success. Here, we built a drug similarity network starting from a public ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
(Show Context)
Drug mode of action (MOA) of novel compounds has been predicted using phenotypic features or, more recently, comparing side effect similarities. Attempts to use gene expres-sion data in mammalian systems have so far met limited success. Here, we built a drug similarity network starting from a public reference dataset containing genome-wide gene expression profiles (GEPs) following treatments with more than a thousand compounds. In this network, drugs sharing a subset of molecular targets are connected by an edge or lie in the same community. Our approach is based on a novel similarity distance between two compounds. The distance is computed by combining GEPs via an original rank-aggregation method, followed by a gene set enrichment analysis (GSEA) to compute similarity between pair of drugs. The network is obtained by considering each compound as a node, and adding an edge between two compounds if their similarity distance is below a given significance threshold. We show that, despite the complexity and the variety of the experimental condi-tions, our approach is able to identify similarities in drug mode of action from GEPs. Our approach can also be used for the identification of the MOA of new compounds. Key words: connectivity map, drug mode of action, gene set enrichment analysis, ranks merging, similarity networks.
Predicting drug-target interactions using probabilistic matrix factorization
- J. Chem. Inf. Model
, 2013
"... ABSTRACT: Quantitative analysis of known drug−target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful ..."
Abstract
-
Cited by 4 (1 self)
- Add to MetaCart
(Show Context)
ABSTRACT: Quantitative analysis of known drug−target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently largewhich is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70 % of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug−target pairs implicated in neurobiological disorders are overrepresented among de novo predictions. 1.
Metabolomics and systems pharmacology: Why and how to model the human metabolic network for drug discovery. Drug Discovery Today
, 2014
"... Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and ..."
Abstract
-
Cited by 4 (2 self)
- Add to MetaCart
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
Characterization of graphs for protein structure modeling and recognition of solubility. arXiv preprint arXiv:1407.8033
, 2014
"... This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensities of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists bas ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensities of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists basically in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins. Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of several numerical features, which we extracted from their structural representation. We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility. We finally analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems.
Bioinformatics Analysis for the Antirheumatic Effects of Huang-Lian-Jie-Du-Tang from a Network Perspective
"... Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear "heat" and "poison" that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target pro ..."
Abstract
- Add to MetaCart
Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear "heat" and "poison" that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT's components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT's component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT's target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.
An Integrative Thrombosis Network: Visualization and Topological Analysis
"... A comprehensive understanding of the integrative nature of the molecular network in thrombosis would be very helpful to develop multicomponent and multitarget antithrombosis drugs for use in traditional Chinese medicine (TCM). This paper attempts to comprehensively map the molecular network in thro ..."
Abstract
- Add to MetaCart
(Show Context)
A comprehensive understanding of the integrative nature of the molecular network in thrombosis would be very helpful to develop multicomponent and multitarget antithrombosis drugs for use in traditional Chinese medicine (TCM). This paper attempts to comprehensively map the molecular network in thrombosis by combining platelet signaling, the coagulation cascade, and natural clot dissolution systems and to analyze the topological characteristics of the network, including the centralities of nodes, network modules, and network robustness. The results in this research advance understanding of functions of proteins in the thrombosis network and provide a reference for predicting potential therapeutic antithrombotic targets and evaluating their influence on the network.
Biases of drug-target interaction network data
"... Abstract. Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug-target interaction data has boosted the development of machine learning methods for the in silico prediction of drugtarget interactions ..."
Abstract
- Add to MetaCart
Abstract. Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug-target interaction data has boosted the development of machine learning methods for the in silico prediction of drugtarget interactions. In this paper we focus on the crucial issue of data bias. We show that four popular datasets contain a bias because of the way they have been constructed: all drug compounds and target proteins have at least one interaction and some of them have only a single interaction. We show that this bias can be exploited by prediction methods to achieve an optimistic generalization performance as estimated by crossvalidation procedures, in particular leave-one-out cross validation. We discuss possible ways to mitigate the effect of this bias, in particular by adapting the validation procedure. In general, results indicate that the data bias should be taken into account when assessing the generalization performance of machine learning methods for the in silico prediction of drug-target interactions. The datasets and source code for this article are available at
multi-layered regulatory networks
"... Background: Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs ..."
Abstract
- Add to MetaCart
(Show Context)
Background: Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description: We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download
