• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.21-28
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• 2012

Predication of protein interaction sites for monomer structures can reduce the search space for protein docking and has been regarded as very significant for predicting unknown functions of proteins from their interacting proteins whose functions are known. In the other hand, the prediction of interaction sites has been limited in crystallizing weakly interacting complexes which are transient and do not form the complexes stable enough for obtaining experimental structures by crystallization or even NMR for the most important protein-protein interactions. This work reports the calculation of 3D surface patches of complex structures and their properties and a machine learning approach to build a predictive model for the 3D surface patches in interaction and non-interaction sites using support vector machine. To overcome classification problems for class imbalanced data, we employed an under-sampling technique. 9 properties of the patches were calculated from amino acid compositions and secondary structure elements. With 10 fold cross validation, the predictive model built from SVM achieved an accuracy of 92.7% for classification of 3D patches in interaction and non-interaction sites from 147 complexes.

• Animal cells and systems
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• v.6 no.3
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• pp.277-282
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• 2002

Topoisomearse II is an essential enzyme in all organisms with several independent roles in DNA metabolism. Recently, it has been demonstrated that the C-terminal region of topoisomerases II is associated with hetero-logous protein-protein interactions in human and yeast. In this study, we identified that RTP1, a rat homologue of EIA binding protein BS69, is another topoisomerae II interacting protein by yeast two-hybrid screening. RTP1 has an E1A-binding domain and a MYND motif, which are known to be required for transcriptional regulation by binding to other proteins and interaction with the leucine zipper motif of topoisomerase II. The physical interaction between RTP1 and topoisomerase ll$\alpha$ was examined by GST pull-down assay in vitro. The expression level of RTP1 peaks in S phase as that of topoisomerase ll$\alpha$. These results suggest that the interaction between topoisomerase ll$\alpha$ and RTP1 might play an important role in regulating the transcription of genes involved in DNA metabolism in higher eukaryotes.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4621-4625
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• 2013

The purpose of this study was to construct a protein-protein interaction (PPI) network related to oral squamous cell carcinoma (OSCC). Each protein was ranked and those most associated with OSCC were mined within the network. First, OSCC-related genes were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Then they were mapped to their protein identifiers and a seed set of proteins was built. The seed proteins were expanded using the nearest neighbor expansion method to construct a PPI network through the Online Predicated Human Interaction Database (OPHID). The network was verified to be statistically significant, the score of each protein was evaluated by algorithm, then the OSCC-related proteins were ranked. 38 OSCC related seed proteins were expanded to 750 protein pairs. A protein-protein interaction nerwork was then constructed and the 30 top-ranked proteins listed. The four highest-scoring seed proteins were SMAD4, CTNNB1, HRAS, NOTCH1, and four non-seed proteins P53, EP300, SMAD3, SRC were mined using the nearest neighbor expansion method. The methods shown here may facilitate the discovery of important OSCC proteins and guide medical researchers in further pertinent studies.

• Korean Journal of Veterinary Service
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• v.25 no.4
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• pp.333-346
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• 2002

We previously reported that the equine herpesvirus type 1(EHV-1) immediate-early protein(IE protein) physically interacts with the general transcription factor human TFIIB(Jang et al, J Virol 75：10219-10230, 2001). The interaction between the IE protein and TFIIB is necessary for the IE protein to efficiently transactivate the early TK and late IR5 EHV-1 promoters. A panel of deletion and truncation mutants of the TFIIB gene was constructed and employed in protein-binding assays to map the IE protein-binding domain within TFIIB. Evidence is presented that the first direct repeat of TFIIB interacts specifically with the EHV-1 IE protein.

• Genomics & Informatics
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• v.18 no.4
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.365-377
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• 2006

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.

• Food Science of Animal Resources
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• v.39 no.2
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• pp.229-239
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• 2019

The objectives of this study were to determine the interaction between porcine myofibrillar proteins and various gelatins (bovine hide, porcine skin, fish skin, and duck skin gelatins) and their impacts on gel properties of porcine myofibrillar proteins. Porcine myofibrillar protein was isolated from pork loin muscle (M. longissimus dorsi thoracis et lumborum). Control was prepared with only myofibrillar protein (60 mg/mL), and gelatin treatments were formulated with myofibrillar protein and each gelatin (9:1) at the same protein concentration. The myofibrillar protein-gelatin mixtures were heated from $10^{\circ}C$ to $75^{\circ}C$ ($2^{\circ}C/min$). Little to no impacts of gelatin addition on pH value and color characteristics of heat-induced myofibrillar protein gels were observed (p>0.05). The addition of gelatin slightly decreased cooking yield of heat-induced myofibrillar protein gels, but the gels showed lower centrifugal weight loss compared to control (p<0.05). The addition of gelatin significantly decreased hardness, cohesiveness, gumminess, and chewiness of heat-induced myofibrillar gels. Further, sodium dodecyl poly-acrylamide gel electrophoresis (SDS-PAGE) showed no interaction between myofibrillar proteins and gelatin under non-thermal conditions. Only a slight change in the endothermic peak (probably myosin) of myofibrillar protein-gelatin mixtures was found. The results of this study show that the addition of gelatin attenuated the water-holding capacity and textural properties of heat-induced myofibrillar protein gel. Thus, it could be suggested that well-known positive impacts of gelatin on quality characteristics of processed meat products may be largely affected by the functional properties of gelatin per se, rather than its interaction with myofibrillar proteins.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a Protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• 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.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.