• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1816-1821
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• 2016

Protein secondary structure is important for the study of protein evolution, structure and function of proteins which play crucial roles in most of biological processes. This paper try to effectively extract protein secondary structure information from the large protein structure database in order to predict the protein secondary structure of a query protein sequence. To find more remote homologous sequences of a query sequence in the protein database, we used PSI-BLAST which can perform gapped iterative searches and use profiles consisting of homologous protein sequences of a query protein. The secondary structures of the homologous sequences are weighed combined to the secondary structure prediction according to their relative degree of similarity to the query sequence. When homologous sequences with a neural network predictor were used, the accuracies were higher than those of current state-of-art techniques, achieving a Q3 accuracy of 92.28% and a Q8 accuracy of 88.79%.

• 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.

• Journal of the Korean Magnetic Resonance Society
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• v.17 no.1
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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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• 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.

• Microbiology and Biotechnology Letters
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• v.23 no.4
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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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.

• Proceeding of EDISON Challenge
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• 2014.03a
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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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• v.8 no.2
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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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• v.40 no.5
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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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• v.27 no.6
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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.