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Structure and dynamics of molecular networks: A novel paradigm of drug discovery -- A . . .
- PHARMACOLOGY THERAPEUTICS
, 2013
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A Lock-and-Key Model for Protein-Protein Interactions
, 2006
"... Motivation: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we sugges ..."
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Cited by 33 (9 self)
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Motivation: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. Results: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data is taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. Availability: The algorithm (in Matlab format) is available (see
Discovering signal transduction networks using signaling domain-domain interactions." Genome Inform 17(2
, 2006
"... The objective of this paper is twofold. One objective is to present a method of predicting signaling domain-domain interactions (signaling DDI) using inductive logic programming (ILP), and the other is to present a method of discovering signal transduction networks (STN) using signaling DDI. The res ..."
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Cited by 5 (0 self)
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The objective of this paper is twofold. One objective is to present a method of predicting signaling domain-domain interactions (signaling DDI) using inductive logic programming (ILP), and the other is to present a method of discovering signal transduction networks (STN) using signaling DDI. The research on computational methods for discovering signal transduction networks (STN) has received much attention because of the importance of STN to transmit inter- and intra-cellular signals. Unlike previous STN works functioning at the protein/gene levels, our STN method functions at the protein domain level, on signal domain interactions, which allows discovering more reliable and stable STN. We can mostly reconstruct the STN of yeast MAPK pathways from the inferred signaling domain interactions, with coverage of 85%. For the problem of prediction of signaling DDI, we have successfully constructed a database of more than twenty four thousand ground facts from ve popular genomic and proteomic databases. We also showed the advantage of ILP in signaling DDI prediction from the constructed database, with high sensitivity (88%) and accuracy (83%). Studying yeast MAPK STN, we found some new signaling domain interactions that do not exist in the well-known InterDom database. Supplementary materials are now available from
networks using PSIMAP (protein structural interactome map)
, 2005
"... Comparative interactomics analysis of protein family interaction ..."
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Received (Day Month Year)
"... Protein-protein interactions (PPI) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPI. To predict PPI, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domai ..."
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Protein-protein interactions (PPI) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPI. To predict PPI, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domains. Integrating both protein domain features and genomic/proteomic features from multiple databases could be more powerful in PPI prediction. Moreover, it can allow discovering reciprocal relationships between PPI and biological features of their interacting partners. We develop a novel integrative domain-based method for predicting protein-protein interactions using inductive logic programming (ILP). Two principal domain features used are domain fusions and domain-domain interactions. Various relevant features of proteins are exploited from five popular genomic and proteomic databases. By integrating these features, we constructed biologically significant ILP background knowledge of more than 278,000 ground facts. The experimental results through multiple 10-fold crossvalidation demonstrated that our method can better predict protein-protein interactions than other computational methods in terms of typical performance measures. The proposed ILP framework can be applied to predict domain-domain interactions with high sensitivity and specificity. The induced ILP rules give us a lot of interesting biological reciprocal relationships among PPI, protein domains, and genomic/proteomic features related to PPI.
Education Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases
"... Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes; in particular, the distortion of protein interfaces may lead to the development of many diseases. To understand the mechanisms of protein recognition at the molecular l ..."
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Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes; in particular, the distortion of protein interfaces may lead to the development of many diseases. To understand the mechanisms of protein recognition at the molecular level and to unravel the global picture of protein interactions in the cell, different experimental techniques have been developed. Some methods characterize individual protein interactions while others are advanced for screening interactions on a genomewide scale. In this review we describe different experimental techniques of protein interaction identification together with various databases which attempt to classify the large array of experimental data. We discuss the main promises and pitfalls of different methods and present several approaches to verify and validate the diverse experimental data produced by highthroughput techniques.
unknown title
, 2006
"... Research article Comprehensive analysis of co-occurring domain sets in yeast proteins ..."
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Research article Comprehensive analysis of co-occurring domain sets in yeast proteins
