• 한국정보통신학회논문지
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• 제20권9호
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• pp.1816-1821
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• 2016

단백질은 대부분의 생물학적 과정에서 중대한 역할을 수행하고 있으므로, 단백질 진화, 구조와 기능을 알아내기 위하여 많은 연구가 수행되고 있는데, 단백질의 이차 구조는 이러한 연구의 중요한 기본적 정보이다. 본 연구는 대규모 단백질 구조 자료로부터 단백질 이차 구조 정보를 효과적으로 추출하여 미지의 단백질 서열이 가지는 이차 구조를 예측하려 한다. 질의 서열과 상동관계에 있는 단백질 구조자료내의 서열들을 광범위하게 찾아내기 위하여, 탐색에 사용하는 프로파일의 구성에 질의 서열과 유사한 서열들을 사용하고 갭을 허용하여 반복적인 탐색이 가능한 PSI-BLAST를 사용하였다. 상동 단백질들의 이차구조는 질의 서열과의 상동 관계의 강도에 따라 가중되어 이차 구조 예측에 기여되었다. 이차 구조를 각각 세 개와 여덟 개로 분류하는 예측 실험에서 상동 서열들과 신경망을 동시에 사용하여 93.28%와 88.79%의 정확도를 얻어서 기존 방법보다 성능이 향상되었다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권4호
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• pp.365-377
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• 2006

단백질들은 서로 다른 단백질들과 상호작용 하거나 복합물을 형성함으로써 생물학적으로 중요한 기능을 한다고 알려져 있다. 때문에 대부분의 세포작용에 있어 중요한 역할을 하는 단백질 상호작용의 분석 및 예측에 대한 연구는 여러 연구그룹으로부터 풍부한 데이타가 산출되고 있는 현(現) 게놈시대에서 또 하나의 중요한 이슈가 되고 있다. 본 논문에서는 효모(Saccharomyces cerevisiae)에 대해 공개되어있는 단백질 상호작용 데이타들에서 속성들 간의 연관을 통해 유추 가능한 잠재적 단백질 상호작용들을 예측하기 위한 연관속성 마이닝 방법을 제시한다. 단백질의 속성들 중 연속값을 가지는 속성값들은 최대상호 의존성에 기반을 두어 이산화 하였으며, 정보이론기반 속성선택 알고리즘을 사용하여 단백질들 간의 상호작용 예측을 위해 고려되는 단백질의 속성(attribute) 수 증가에 따른 속성차원문제를 극복하도록 하였다. 속성들 간의 연관성 발견은 데이타마이닝 분야에서 사용되는 연관규칙 발견(association rule discovery) 방법을 사용하였다 논문에서 제안한 방법은 발견된 연관규칙을 통한 단백질 상호작용 예측문제에 있어 최대 약 96.5%의 예측 정확도를 보였으며 속성필터링을 통하여 속성필터링을 하지 않는 기존의 방법에 비해 최대 약 29.4% 연관규칙 발견속도 향상을 보였다.

• 한국자기공명학회논문지
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• 제17권1호
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• pp.59-66
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• 2013

BldD, a developmental transcription factor from Streptomyces coelicolor, is a homodimeric, DNA-binding protein with 167 amino acids in each subunit. Each monomer consists of two structurally distinct domains, the N-terminal domain (BldD-NTD) responsible for DNA-binding and dimerization and the C-terminal domain (BldD-CTD). In contrast to the BldD-NTD, of which crystal structure has been solved, the BldD-CTD has been characterized neither in structure nor in function. Thus, in terms of structural genomics, structural study of the BldD-CTD has been conducted in solution, and in the present work, mainchain NMR assignments of the recombinant BldD-CTD (residues 80-167 of BldD) could be achieved by a series of heteronuclear multidimensional NMR experiments on a [$^{13}C/^{15}N$]-enriched protein sample. Finally, the secondary structure prediction by CSI and TALOS+ analysis using the assigned chemical shifts data identified a ${\beta}-{\alpha}-{\alpha}-{\beta}-{\alpha}-{\alpha}-{\alpha}$ topology of the domain. The results will provide the most fundamental data for more detailed approach to the atomic structure of the BldD-CTD, which would be essential for entire understanding of the molecular function of BldD.

• Journal of Information Processing Systems
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• 제8권3호
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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.

• 한국미생물·생명공학회지
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• 제23권4호
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• pp.485-491
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• 1995

Protein ion exchange chromatography was studied experimentally in order to prove the theoretical prediction from the linear model of Yamamoto, S. et al (1). Adsorption isotherms were measured as a function of ionic strength in a batch experiment. The relationship between the characteristics of chromatogram and the operating conditions of ionic strength, flow rate, length of column, concentration and amount of protein sample were studied. At the higher ionic strength, the lower flow rate and the longer column conditions, the higher number of plate was obtained. Satisfactory agreement was observed between the experimental and the calculated chromatograms except for the case of high protein concentration.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 추계학술발표대회
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• pp.1115-1118
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• 2011

단백질 사이의 상호작용 네트워크(PPI network: Protein-Protein Interaction network)를 이용하여 단백질 기능을 예측 하는 것은 단백질 기능 예측 기법들 중에서 중요한 작용을 한다. 하지만 PPI를 이용한 단백질 기능 예측은 기능의 복잡도와 다양성으로 인해 제한적인 결과를 나타내 왔다. 따라서 본 논문에서는 기존의 연구들 보다 높은 정확도로 단백질 기능을 예측하기 위해 기능 예측을 하려는 단백질과 상호작용 하는 단백질들에 그래프 마이닝 기법을 적용하여 빈발 2-노드 상호작용 패턴을 찾고, 그 패턴을 이용하여 단백질 기능을 예측하는 접근법을 제안하였다. 실험데이터로 DIP(Database of Interacting Proteins)에서 제공하는 단백질 상호작용 데이터를 사용하였으며, 다른 기존의 단백질 기능 예측 기법들보다 높은 정확도를 보여주었다.

• EDISON SW 활용 경진대회 논문집
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• 제3회(2014년)
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• pp.63-74
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• 2014

Predicting protein loop structures is an important modeling problem since protein loops are often involved in diverse biological functions by participating in enzyme active sites, ligand binding sites, etc. However, loop structure prediction is difficult even when structures of homologous proteins are known due to large sequence and structure variability among loops of homologous proteins. Therefore, an ab initio approach is necessary to solve loop modeling problems. One of the difficulties in the development of ab initio loop modeling method is to derive an accurate scoring function that closely approximates the true free energy function. In particular, entropy as well as energy contribution have to be considered adequately for loops because loops tend to be flexible compared to other parts of protein. In this study, the contribution of conformational entropy is considered in scoring loop conformations by employing "colony energy" which was previously proposed to estimate the free energy for an ensemble of conformations. Loop conformations were generated by using two EDISON_Chem programs GalaxyFill and GalaxySC, and colony energy was designed for this sampling by tuning relevant parameters. On a test set of 40 loops, the accuracy of predicted loop structure improved on average by scoring with the colony energy compared to scoring by energy alone. In addition, high correlation between colony energy and deviation from the native structure suggested that more extensive sampling can further improve the prediction accuracy. In another test on 6 ligand-binding loops that show conformational changes by ligand binding, both ligand-free and ligand-bound states could be identified by using colony energy when no information on the ligand-bound conformation is used.

• Genomics & Informatics
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• 제8권2호
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• pp.90-93
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• 2010

A-kinase-anchoring proteins (AKAPs) are scaffold proteins which compartmentalize protein kinase A (PKA, cAMP-dependent protein kinase) and other enzymes to specific subcellular sites. The spatiotemporal control of these enzymes by AKAPs is important for cellular function like cell growth and development etc. Hence, it is important to understand the basic function of AKAPs and their functional domains. However, diverse names, function, cellular localizations and many members of AKAPs increase difficulties when researchers search appropriate AKAPs for their experimental purpose. Nevertheless, there was no previous AKAPs-related database regardless of their important cellular functions and difficulty of finding appropriate AKAPs. So, we developed AKAPs database (AKAPDB), which contains their sequence information, functions and other information derived from prediction programs and other databases. Therefore, we propose that AKAPDB can be an important tool to researchers in the related fields. AKAPDB is available via the internet at http://plaza3.snu.ac.kr/akapdb/.

• BMB Reports
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• 제40권5호
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• pp.839-843
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• 2007

HP0495 (Swiss-Prot ID; Y495_HELPY) is an 86-residue hypothetical protein from Helicobacter pylori strain 26695. The function of HP0495 cannot be identified based on sequence homology, and HP0495 is included in a fairly unique sequence family. Here, we report the sequencespecific backbone resonance assignments of HP0495. About 97% of all the $^1HN$, $^{15}N$, $^{13}C{\alpha}$, $^{13}C{\beta}$, and $^{13}CO$ resonances were assigned unambiguously. We could predict the secondary structure of HP0495, by analyzing the deviation of the $^{13}C{\alpha}$ and $^{13}C{\beta}$ shemical shifts from their respective random coil values. Secondary structure prediction shows that HP0495 consists of two $\alpha$-helices and four $\beta$-strands. This study is a prerequisite for determining the solution structure of HP0495 and investigating the protein-protein interaction between HP0495 and other Helicobacter pylori proteins.

• 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.