• IMMUNE NETWORK
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• 제4권3호
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• pp.184-189
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• 2004

Background: Hu syndrome, a neurological disorder, is characterized by the remote effect of small cell lung cancer on the neural degeneration. The suspicious effectors for this disease are anti-Hu autoantibodies or Hu-related CD8+ T lymphocytes. Interestingly, the same effectors have been suggested to act against tumor growth and this phenomenon may represent natural tumor immunity. For these diagnostic and therapeutic reasons, the demand for antibodies against Hu protein is rapidly growing. Methods: Polyclonal and monoclonal antibodies were generated using recombinant HuR protein. Western blot analyses were performed to check the specificity of generated antibodies using various recombinant proteins and cell lysates. Extracellular stimuli for HuR expression had been searched and HuR-associated proteins were isolated from polysome lysates and then separated in a 2-dimensional gel. Results: Polyclonal and monoclonal antibodies against HuR protein were generated and these antibodies showed HuR specificity. Antibodies were also useful to detect and immunoprecipitate endogenous HuR protein in Jurkat and BJAB. This report also revealed that TNF-${\alpha}$ treatment in BJAB up-regulated HuR expression. Lastly, protein profile in HuR-associated mRNAprotein complexes was mapped by 2-dimensional gel electrophoresis. Conclusion: This study reported that new antibodies against HuR protein were successfully generated. Currently, project to develop a diagnostic kit is in process. Also, this report showed that TNF-${\alpha}$ up-regulated HuR expression in BJAB and protein profile associated with HuR protein was mapped.

• Bioinformatics and Biosystems
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• 제2권2호
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• pp.46-51
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• 2007

In the proteomics research using mass spectrometry, the protein database search gives the protein information from the peptide sequences that show the best match with the tandem mass spectra. The protein sequence database has been a powerful knowledgebase for this protein identification. However, as we accumulate the protein sequence information in the database, the database size gets to be huge. Now it becomes hard to consider all the protein sequences in the database search because it consumes much computing time. For the high-throughput analysis of the proteome, usually we have used the non-redundant refined database such as IPI human database of European Bioinformatics Institute. While the non-redundant database can supply the search result in high speed, it misses the variation of the protein sequences. In this study, we have concerned the proteomics data in the point of protein similarities and used the network analysis tool to build a new analysis method. This method will be able to save the computing time for the database search and keep the sequence variation to catch the modified peptides.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Bulletin of the Korean Chemical Society
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• 제24권4호
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• pp.411-412
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• 2003

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.29-29
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• 2003

Incompatible plant-pathogen interactions result in the rapid cell death response known as hypersensitive response (HR) and activation of host defense-related genes. To understand the molecular and cellular mechanism controlling defense response better, several approaches including isolation and characterization of novel genes, promoter analysis of those genes, protein-protein interaction analysis and reverse genetic approach etc. By using the yeast two-hybrid system a clone named Tsipl, Tsil -interacting protein 1, was isolated whose translation product apparently interacted with Tsil, an EREBP/AP2 type DNA binding protein. RNA gel blot analysis showed that the expression of Tsipl was increased by treatment with NaCl, ethylene, salicylic acid, or gibberellic acid. Transient expression analysis using a Tsipl::smGFP fusion gene in Arabidopsis protoplasts indicated that the Tsipl protein was targeted to the outer surface of chloroplasts. The targeted Tsipl::smGFP proteins were diffused to the cytoplasm of protoplasts in the presence of salicylic acid (SA) The PEG-mediated co-transfection analysis showed that Tsipl could interact with Tsil in the nucleus. These results suggest that Tsipl-Tsil interaction might serve to regulate defense-related gene expression. Basically the useful promoters are valuable tools for effective control of gene expression related to various developmental and environmental condition.(중략)

• Molecules and Cells
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• 제27권6호
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• pp.629-634
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• 2009

G protein-coupled receptors (GPCRs) are part of multi-protein networks called 'receptosomes'. These GPCR interacting proteins (GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed the identification of novel GPCR-PDZ domain protein interactions.

• 한국정보통신학회논문지
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• 제22권5호
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• pp.728-733
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• 2018

단백질을 구성하는 아미노산의 서열 정보만으로 단백질 이차 구조를 예측하기 위하여 심층 학습이 활발히 연구되고 있다. 본 논문에서는 단백질 이차 구조를 예측하기 위하여 다양한 구조의 합성곱 신경망의 성능을 비교하였다. 단백질 이차 구조의 예측에 적합한 신경망의 층의 깊이를 알아내기 위하여 층의 개수에 따른 성능을 조사하였다. 또한 이미지 분류 분야의 많은 방법들이 기반 하는 GoogLeNet과 ResNet의 구조를 적용하였는데, 이러한 방법은 입력 자료에서 다양한 특성을 추출하거나, 깊은 층을 사용하여도 학습과정에서 그래디언트 전달을 원활하게 한다. 합성곱 신경망의 여러 구조를 단백질 자료의 특성에 적합하게 변경하여 성능을 향상시켰다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.476-479
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• 2008

Understanding the dynamics of proteins is essential to gain insight into biological functions of proteins. The protein dynamics is delineated by conformational fluctuation (i.e. thermal vibration), and thus, thermal vibration of proteins has to be understood. In this paper, a simple mechanical model was considered for understanding protein's dynamics. Specifically, a mechanical vibration model was developed for understanding the large protein dynamics related to biological functions. The mechanical model for large proteins was constructed based on simple elastic model (i.e. Tirion's elastic model) and model reduction methods (dynamic model condensation). The large protein structure was described by minimal degrees of freedom on the basis of model reduction method that allows one to transform the refined structure into the coarse-grained structure. In this model, it is shown that a simple reduced model is able to reproduce the thermal fluctuation behavior of proteins qualitatively comparable to original molecular model. Moreover, the protein's dynamic behavior such as collective dynamics is well depicted by a simple reduced mechanical model. This sheds light on that the model reduction may provide the information about large protein dynamics, and consequently, the biological functions of large proteins.

• Macromolecular Research
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• 제17권6호
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• pp.424-429
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• 2009

In this study, we demonstrate that the biological unnatural amino acid incorporation method can be utilized in vivo to synthesize an alkyne-terminated telechelic protein, Synthesis of terminally-functionalized polymers such as telechelic polymers is recognized to be important, since they can be employed usefully in many areas of biology and material science, such as drug delivery, colloidal dispersion, surface modification, and formation of polymer network. The introduction of alkyne groups into polymeric material is particularly interesting since the alkyne group can be a linker to combine other materials using click chemistry. To synthesize the telechelic recombinant protein, we attempted to incorporate the L-homopropargylglycine into the recombinant GroES fragment by expressing the recombinant gene encoding Met at the codons for both N- and C-terminals of the protein in the Met auxotrophic E. coli via Hpg supplementation. The Hpg incorporation rate was investigated and the incorporation was confirmed by MALDI-TOF analysis of the telcchelic recombinant protein.

• 한국비블리아학회지
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• 제26권1호
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• pp.217-231
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• 2015

본 연구에서는 췌장암의 유전자-단백질 상호작용 네트워크를 구성하고, 관련 연구에서 주요하게 언급되는 유전자-단백질의 유발관계 사슬을 파악함으로써, 췌장암의 원인을 규명하는 실증적인 연구로 이어질 수 있는 미발견 공공 지식을 제공하려 하였다. 이를 위하여 텍스트마이닝과 주경로 분석을 Swanson의 ABC 모델에 적용해 중간 개념인 B를 방향성을 가진 다단계 모델로 확장하고 가장 의미 있는 경로를 도출하였다. 본 연구의 주제가 된 췌장암의 사례처럼 시작점과 끝점조차 한정할 수 없는 미발견 공공 지식 추론에서 주경로 분석은 유용한 도구가 될 수 있을 것이다.