• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.23-27
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• 2000

Protein interaction is an important research topic in Bioinformatics. A novel computational method of protein interaction was developed. It shows the diverse pattern of protein protein interaction,

• Genomics & Informatics
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• v.1 no.1
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• pp.7-19
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• 2003

The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.5-23
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• 2002

Motivation: Protein-protein interaction plays a critical role in the biological processes. The identification of interacting proteins by bioinformatical methods can provide new lead In the functional studies of uncharacterized proteins without performing extensive experiments. Results: Protein-protein interactions are predicted by a computational algorithm based on the weighted scoring system for domain interactions between interacting protein pairs. Here we propose potential interaction domain (PID) pairs can be extracted from a data set of experimentally identified interacting protein pairs. where one protein contains a domain and its interacting protein contains the other. Every combinations of PID are summarized in a matrix table termed the PID matrix, and this matrix has proposed to be used for prediction of interactions. The database of interacting proteins (DIP) has used as a source of interacting protein pairs and InterPro, an integrated database of protein families, domains and functional sites, has used for defining domains in interacting pairs. A statistical scoring system. named "PID matrix score" has designed and applied as a measure of interaction probability between domains. Cross-validation has been performed with subsets of DIP data to evaluate the prediction accuracy of PID matrix. The prediction system gives about 50% of sensitivity and 98% of specificity, Based on the PID matrix, we develop a system providing several interaction information-finding services in the Internet. The system, named PreDIN (Prediction-oriented Database of Interaction Network) provides interacting domain finding services and interacting protein finding services. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PreDIN system. This system can be also used as a new tool for functional prediction of unknown proteins.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.128-133
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• 2006

This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1602-1610
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• 2004

As various genome projects have produced enormous amount of biosequence data, functional sequence analysis in terms of tile nucleic acid and protein becomes very significant. In functional genomics and proteomics, the functional analysis of each individual gene and protein remains a big challenge. Contrary to traditional studies, which regard proteins as not components of a whole protein interaction network but individual entities, recent studies have focused on examining functions and roles of each individual gene and protein in view of a whole life system. In this regard, it has been recognized as an appropriate method to analyze protein function on the basis of synthetic information of its interaction and domain modularity. In this context, this paper introduces the PIVS (Protein-protein interaction Inference & Visualization System), which predicts the interaction relationship of input proteins by taking advantage of information on homology degree, domain modules which input sequences contain, and protein interaction relationship. The information on domain modules can increase the accuracy of the function and interaction relationship analysis in terms of the specificity and sensitivity.

• Journal of Life Science
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• v.14 no.6 s.67
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• pp.997-1002
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• 2004

This paper deals with the WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that can provide the comprehensive information on Protein-protein interaction and function concerned with function analysis. Different from existing systems for protein function and protein-protein interaction analysis, which provide fragmentary information restricted to specific field, our system furnishes end-user with comprehensive and synthetic information on the input sequence to be analyzed, including function and annotation information, domain information, and interaction relationship information. The synthetic information that our system contains as local databases has been extracted from many resources related to function, annotation, motif and domain by various pre-processing. Employing our system, end-users can evaluate and judge the synthetic results to do protein interaction and function analysis effectively. In addition, the WASPIFA system is equipped with automatic system management and data update function that facilitates system manager to maintain and manage it efficiently.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.332-336
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• 2009

Functional prediction of unannotated proteins is one of the most important tasks in yeast genomics. Analysis of a protein-protein interaction network leads to a better understanding of the functions of unannotated proteins. A number of researches have been performed for the functional prediction of unannotated proteins from a protein-protein interaction network. A chi-square method is one of the existing methods for the functional prediction of unannotated proteins from a protein-protein interaction network. But, the method does not consider the topology of network. In this paper, we propose a novel method that is able to predict specific molecular functions for unannotated proteins from a protein-protein interaction network. To do this, we investigated all protein interaction DBs of yeast in the public sites such as MIPS, DIP, and SGD. For the prediction of unannotated proteins, we employed a modified chi-square measure based on neighborhood counting and we assess the prediction accuracy of protein function from a protein-protein interaction network.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.313-318
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• 2005

Estimating the reliability of protein-protein interaction data sets obtained by high-throughput technologies such as yeast two-hybrid assays and mass spectrometry is of great importance. We develop a maximum likelihood estimation method that uses both protein localization and gene expression data to estimate the reliability of protein interaction data sets. By integrating protein localization data and gene expression data, we can obtain more accurate estimates of the reliability of various interaction data sets. We apply the method to protein physical interaction data sets and protein complex data sets. The reliability of the yeast two-hybrid interactions by Ito et al. (2001) is 27%, and that by Uetz et at.(2000) is 68%. The reliability of the protein complex data sets using tandem affinity purification-mass spec-trometry (TAP) by Gavin et at. (2002) is 45%, and that using high-throughput mass spectrometric protein complex identification (HMS-PCI) by Ho et al. (2002) is 20%. The method is general and can be applied to analyze any protein interaction data sets.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.431-436
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• 2005

Living cells are sustained not by individual activities but rather by coordinated summative efforts of different biological functional modules. While recent research works have focused largely on finding individual functional modules, this paper attempts to explore the connections or relationships between different cellular functions through cross-function domain interaction maps. Exploring such a domain interaction map can help understand the underlying inter-function communication mechanisms. To construct a cross-function domain interaction map from existing genome-wide protein-protein interaction datasets, we propose a two-step procedure. First, we infer conserved domain-domain interactions from genome-wide protein-protein interactions of yeast, worm and fly. We then build a cross-function domain interaction map that shows the connections of different functions through various conserved domain interactions. The domain interaction maps reveal that conserved domain-domain interactions can be found in most detected cross-functional relationships and a f9w domains play pivotal roles in these relationships. Another important discovery in the paper is that conserved domains correspond to highly connected protein hubs that connect different functional modules together.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.