• Journal of Information Processing Systems
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• v.8 no.3
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• pp.459-470
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• 2012

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of $10^{-5}$, $10^{-3}$ respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

• BMB Reports
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• v.37 no.1
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• pp.45-52
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• 2004

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.

• BMB Reports
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• v.33 no.6
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• pp.427-439
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• 2000

The activity of the phagocyte respiratory burst oxidase is regulated by complex and dynamic alterations in protein-protein interactions that result in the rapid assembly of an active multicomponent NADPH oxidase enzyme on the plasma membrane. While the enzymatic activity has been studied for the past 20 years, the past decade has seen remarkable progress in our understanding of the enzyme and its activation at the molecular level. This article describes the current state of knowledge, and proposes a model for the mechanism by which protein-protein interactions regulate enzyme activity in this system.

• Journal of the Korean Magnetic Resonance Society
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• v.22 no.4
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• pp.76-81
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• 2018

Investigation of the protein-protein interaction mode at atomic resolution is essential for understanding on the underlying functional mechanisms of proteins as well as for discovering druggable compounds blocking deleteriou interprotein interactions. Solution NMR spectroscopy provides accurate and precise information on intermolecular interactions even for weak and transient interactions, and it is also markedly useful for examining the change in the conformation and dynamics of target proteins upon binding events. In this mini-review, we comprehensively describe three unique and powerful methods of solution NMR spectroscopy, paramagnetic relaxation enhancement (PRE), pseudo-contact shift (PCS), and residual dipolar coupling (RDC), for the study on protein-protein interactions.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.17-25
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• 2003

Determining the binding sites in protein-nucleic acid complexes is essential to the complete understanding of protein-nucleic acid interactions and to the development of new drugs. We have developed a set of algorithms for analyzing protein-nucleic acid interactions and for predicting potential binding sites in protein-nucleic acid complexes. The algorithms were used to analyze the hydrogen-bonding interactions in protein-RNA and protein-DNA complexes. The analysis was done both at the atomic and residue level, and discovered several interesting interaction patterns and differences between the two types of nucleic acids. The interaction patterns were used for predicting potential binding sites in new protein-RNA complexes.

• 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.

• Rapid Communication in Photoscience
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• v.4 no.1
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• pp.1-8
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• 2015

For understanding molecular mechanisms of photochemical reactions, in particular reactions of proteins with biological functions, it is important to elucidate both the initial reactions from the photoexcited states and the series of subsequent chemical reactions, e.g., conformation, intermolecular interactions (hydrogen bonding, hydrophobic interactions), and inter-protein interactions (oligomer formation, dissociation reactions). Although time-resolved detection of such dynamics is essential, these dynamics have been very difficult to track by traditional spectroscopic techniques. Here, relatively new approaches for probing the dynamics of protein photochemical reactions using time-resolved transient grating (TG) are reviewed. By using this method, a variety of spectrally silent dynamics can be detected and such data provide a valuable description about the reaction scheme. Herein, a blue light sensor protein TePixD is the exemplar. The initial photochemistry for TePixD occurs around the chromophore and is detected readily by light absorption, but subsequent reactions are spectrally silent. The TG experiments revealed conformational changes and changes in inter-protein interactions, which are essential for TePixD function. The TG experiments also showed the importance of fluctuations of the intermediates as the driving force of the reaction. This technique is complementary to optical absorption detection methods. The TG signal contains a variety of unique information, which is difficult to obtain by other methods. The advantages and methods for signal analyses are described in detail in this review.

• Food Science and Preservation
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• v.5 no.1
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• pp.65-71
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• 1998

The changes in gel characteristics of soy protein as a result of various reagents that alter specific interactions which affect the formation and textural properties of gels, were studied. The reagents were added to 15% soy protein solutions prior to heat treatment. The gels were not formed with urea, indicating that hydrogen bonds significantly contributed to the formation and hardness of soy protein gel. Hydrophobic interactions and disulfide bonds compensated for hydrogen bonds and the contributions of electrostatic interactions to gel hardness are relatively insignificant. The farce primarily responsible for gel cohesiveness appeared to be disulfide bonds, because a significant decrease in cohesiveness was found only with the presence of N-ethylmaleimide. Adhesiveness decreased only with the addition of urea, and thus the contribution of hydrogen bonding to adhesiveness of gel could be concluded to be resent. However, adhesiveness was suggested to be interpreted not only wile molecular forces involved in gel formation but also with hydration properties of protein.

• 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.