• Journal of Microbiology and Biotechnology
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• v.17 no.2
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• pp.359-363
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• 2007

Because of the importance of the type III protein-secretion system in bacteria-plant interaction, its function in bacterial pathogenesis of plants has been intensively studied. To identity bacterial proteins interacting with Xanthomonas hrp gene products that are involved in pathogenicity, we performed the glutathione-bead binding analysis of Xanthomonas lysates containing GST-tagged Hrp proteins. Analysis of glutathione-bead bound proteins by 1-DE and MALDI-TOF has demonstrated that Avr proteins, RecA, and several components of the type III secretion system interact with HrpB protein. This proteomic approach could provide a powerful tool in finding interaction partners of Hrp proteins whose roles in host-pathogen interaction need further studies.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.125-132
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• 2002

In the past, bioinformatics was often regarded as a difficult and rather remote field, practiced only by computer scientists and not a practical tool available to biologists. However, the various on-going genome projects have had a serious impact on biological sciences in various ways and now there is little doubt that bioinformatics is an essential part of the research environment, with a wealth of biological information to analyze and predict. Fully sequenced genomes made us to have additional insights into the functional properties of the encoded proteins and made it possible to develop new tools and schemes for functional biology on a proteomic scale. Among those are the yeast two-hybrid system, mass spectrometry and microarray: the technology of choice to detect protein-protein interactions. These functional insights emerge as networks of interacting proteins, also known as "pathway informatics" or "interactomics". Without exception it is no longer possible to make advances in the signaling/regulatory pathway studies without integrating information technologies with experimental technologies. In this paper, we will introduce the databases of protein interaction worldwide and discuss several challenging issues regarding the actual implementation of databases.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.234-243
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• 2004

단백질-단백질 상호작용(PPI :Protein-Protein Interaction) 데이터는 생물체가 어떠한 메커니즘으로 생명을 유지하는지에 대한 정보를 담고 있다. 최근에는 생물학자들의 실험에 의해 많은 데이터가 축적되어 있으며, 데이터베이스로 구축되어 인터넷에 공개되어 있다. PPI 데이터는 단백질를 노드(node)로, 상호작용은 에지(edge)로 갖는 그래프(Graph) 구조로 표현 가능하다. 본 논문에서는 사용자가 PPI 데이터를 쉽게 가공하고 분석할 수 있도록 그래프 이론 기반에 기반하여 구현한 Proteinca(PROTEin INteraction CAbaret) 시스템에 대해 소개한다. Proteinca에 대한 자세한 정보는 http://jade.cs.pusan.ac.kr/${\sim}$proten에서 볼 수 있다.

• 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.

• 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 Applied Biological Chemistry
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• v.42 no.3
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• pp.125-129
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• 1999

The changes in gel characteristics of soy protein and succinylated soy protein due to various specific interaction-altering reagents which affect the formation and textural properties of gels, were studied. The reagents were added to 15% soy protein solutions prior to heat treatment. Succinylated soy protein formed harder gel without the addition of reagents. Hardly no gels were formed with urea, indicating that hydrogen bonds significantly contributed to the formation and hardness of the gel and the effects of urea on the hardness of succinylated soy protein gel were more significant. Disulfide bonds were important in the formation of hard gels whether they were succinylated or not, but the contributions of hydrophobic interactions to gel hardness were relatively insignificant. The hardness reducing effects of NaCl and NaSCN were more significant in succinylated soy protein gel. As such, electrostatic interactions were important for succinylated soy protein to form hard gel but not for unmodified soy protein.

• Archives of Pharmacal Research
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• v.4 no.2
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• pp.99-107
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• 1981

To investigate the protein binding characteristics of ibuprofenlysine, the effects of drub conentration, pH, ionic strength and protein concentration on the binding of drug to protein concentration on the binding of drug to protein were studied by fluorescence probe method. The conformational change of protein was investigated by circular dichroism (CD) measurement. As the concentration of drug increases, the association constant decreases. These may be due to complex formation of the probe and drug, or the interaction of the protein-probe complex and drug. The association constant for ibuprofenlysine increased with increasing protein concentration. These finding suggest a sharing of one ibuprofenlysine molecule by more than one protein molecule in the binding. The binding between ibuprofenlysine and protein was dependent on pH and ionic strength. It seems that both hydrophobic binding and some electrostatic forces are involved in the binding of ibuprofenlysing to protein.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.194-198
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• 2017

In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.193-207
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• 2009

Intracellular signal transduction is achieved by protein-protein interaction. In this paper, we suggest high performance algorithm based on Yeast protein-protein interaction and protein location information. We compare if pathways predicted with high valued weights indicate similar tendency with pathways provided in KEGG. Furthermore, we suggest extracted results, which can imply a discovery of new signaling pathways that is yet proven through experiments. This will be a good basis for research to discover new protein signaling pathways and unknown functions of established proteins.

• Journal of Microbiology
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• v.44 no.5
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• pp.577-580
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• 2006

GST pull-down assays were used to characterize the SARS-CoV membrane (M) and nucleocapsid (N) interaction, and it was found that the amino acids 211-254 of N protein were essential for this interaction. When tetrad glutamines (Q) were replaced with glutamic acids (E) at positions of 240-243 of the N protein, the interaction was disrupted.