• Fisheries and Aquatic Sciences
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• v.27 no.3
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• pp.171-179
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• 2024

A study was conducted to evaluate dietary valine (Val) requirement for Pacific white shrimp (Penaeus vannamei). Five isonitrogenous (353 g/kg) and isocaloric (4.08 kcal/g) semi-purified diets containing graded levels of Val (2.7, 5.1, 8.7, 12.1 or 16.0 g/kg) were formulated. Quadruplicate groups of 12 shrimp (average body weight: 0.46 ± 0.00 g) were fed one of the experimental diets (2%-5% of total body weight) for 8 weeks. Maximum weight gain was observed in 8.7 g/kg Val group. However, the growth performance was reduced when Val concentration in diets were higher than 12.1 g/kg. Feed conversion ratio was significantly increased with 2.7 and 16.0 g/kg Val inclusion. Shrimp fed the diets containing 2.7 g/kg Val showed significantly lower protein efficiency ratio, whole-body crude protein and Val concentrations. Dietary inclusion of Val significantly improved the relative expression of insulin-like growth factor binding protein and immune-related genes (prophenoloxidase, lysozyme and crustin) in the hepatopancreas and 8.7 g/kg Val group showed highest expression among all the groups. The dietary requirement of Val for maximum growth of juvenile P. vannamei, estimated using polynomial regression analysis on growth, was 9.54 g/kg of Val (27.2 g/kg based on protein level) and maximum growth occurred at 9.27 g/kg of Val (26.2 g/kg based on protein level) based on broken-line regression analysis.

• 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 Microbiology and Biotechnology
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• v.27 no.5
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• pp.878-895
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• 2017

Phosphorylation, a critical mechanism in biological systems, is estimated to be indispensable for about 30% of key biological activities, such as cell cycle progression, migration, and division. It is synergistically balanced by kinases and phosphatases, and any deviation from this balance leads to disease conditions. Pathway or biological activity-based abnormalities in phosphorylation and the type of involved phosphatase influence the outcome, and cause diverse diseases ranging from diabetes, rheumatoid arthritis, and numerous cancers. Protein tyrosine phosphatases (PTPs) are of prime importance in the process of dephosphorylation and catalyze several biological functions. Abnormal PTP activities are reported to result in several human diseases. Consequently, there is an increased demand for potential PTP inhibitory small molecules. Several strategies in structure-based drug designing techniques for potential inhibitory small molecules of PTPs have been explored along with traditional drug designing methods in order to overcome the hurdles in PTP inhibitor discovery. In this review, we discuss druggable PTPs and structure-based virtual screening efforts for successful PTP inhibitor design.

• Biomolecules & Therapeutics
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• v.19 no.1
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• pp.1-8
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• 2011

G-protein coupled receptors (GPCRs) constitute an important class of drug targets and are involved in every aspect of human physiology including sleep regulation, blood pressure, mood, food intake, perception of pain, control of cancer growth, and immune response. Radiometric assays have been the classic method used during the search for potential therapeutics acting at various GPCRs for most GPCR-based drug discovery research programs. An increasing number of diverse small molecules, together with novel GPCR targets identified from genomics efforts, necessitates the use of high-throughput assays with a good sensitivity and specificity. Currently, a wide array of high-throughput tools for research on GPCRs is available and can be used to study receptor-ligand interaction, receptor driven functional response, receptor-receptor interaction,and receptor internalization. Many of the assay technologies are based on luminescence or fluorescence and can be easily applied in cell based models to reduce gaps between in vitro and in vivo studies for drug discovery processes. Especially, cell based models for GPCR can be efficiently employed to deconvolute the integrated information concerning the ligand-receptor-function axis obtained from label-free detection technology. This review covers various platform technologies used for the research of GPCRs, concentrating on the principal, non-radiometric homogeneous assay technologies. As current technology is rapidly advancing, the combination of probe chemistry, optical instruments, and GPCR biology will provide us with many new technologies to apply in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.7 s.250
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• pp.841-848
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• 2006

Many researchers are developing computational prediction methods for protein tertiary structures to get much more information of protein. These methods are very attractive on the aspects of breaking technologies of computer hardware and simulation software. One of the computational methods for the prediction is a fragment assembly method which shows good ab initio predictions at several cases. There are many barriers, however, in conventional fragment assembly methods. Argues on protein energy functions and global optimization to predict the structures are in progress fer example. In this study, a new prediction method for protein structures is proposed. The proposed method mainly consists of two parts. The first one is a fragment assembly which uses very shot fragments of representative proteins and produces a prototype of a given sequence query of amino acids. The second one is a global optimization which folds the prototype and makes the only protein structure. The goodness of the proposed method is shown through numerical experiments.

• BMB Reports
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• v.49 no.11
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• pp.585-586
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• 2016

Extracellular vesicles (EVs) are natural carriers of biomolecules that play central roles in cell-to-cell communications. Based on this, there have been various attempts to use EVs as therapeutic drug carriers. From chemical reagents to nucleic acids, various macromolecules were successfully loaded into EVs; however, loading of proteins with high molecular weight has been huddled with several problems. Purification of recombinant proteins is expensive and time consuming, and easily results in modification of proteins due to physical or chemical forces. Also, the loading efficiency of conventional methods is too low for most proteins. We have recently proposed a new method, the so-called exosomes for protein loading via optically reversible protein-protein interaction (EXPLORs), to overcome the limitations. Since EXPLORs are produced by actively loading of intracellular proteins into EVs using blue light without protein purification steps, we demonstrated that the EXPLOR technique significantly improves the loading and delivery efficiency of therapeutic proteins. In further in vitro and in vivo experiments, we demonstrate the potential of EXPLOR technology as a novel platform for biopharmaceuticals, by successful delivery of several functional proteins such as Cre recombinase, into the target cells.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.728-733
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• 2018

Deep learning has been actively studied for predicting protein secondary structure based only on the sequence information of the amino acids constituting the protein. In this paper, we compared the performances of the convolutional neural networks of various structures to predict the protein secondary structure. To investigate the optimal depth of the layer of neural network for the prediction of protein secondary structure, the performance according to the number of layers was investigated. We also applied the structure of GoogLeNet and ResNet which constitute building blocks of many image classification methods. These methods extract various features from input data, and smooth the gradient transmission in the learning process even using the deep layer. These architectures of convolutional neural networks were modified to suit the characteristics of protein data to improve performance.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.163-168
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• 2014

In a protein molecule, ${\alpha}$-helices are important for protein structure, function, and binding to other proteins, so the analysis on the structure of helices has been researched. Since an interaction between two helices is evaluated based on their axes, massive errors in protein structure analysis would be caused if a curved or kinked long ${\alpha}$-helix is considered as a linear one. In this paper, we present an algorithm to reconstruct ${\alpha}$-helices in a protein molecule as a sequence of straight helices under given threshold.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• Journal of Microbiology and Biotechnology
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• v.21 no.8
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• pp.869-873
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• 2011

Most medium formulations for improving culture of entomopathogenic nematodes (EPN) based on protein sources have used enriched media like animal feed such as dried egg yolk, lactalbumin, and liver extract, among other ingredients. Most results, however, showed unstable yields and longer production time. Many of the results do not show the detailed parameters of fermentation. Soy flour, cotton seed flour, corn gluten meal, casein powder, soytone, peptone, casein hydrolysates, and lactalbumin hydrolysate as protein sources were tested to determine the source to support optimal symbiotic bacteria and nematode growth. The protein hydrolysates selected did not improve bacterial cell mass compared with the yeast extract control, but soy flour was the best, showing 75.1% recovery and producing more bacterial cell number ($1.4{\times}10^9$/ml) than all other sources. The highest yield ($1.85{\times}10^5$ IJs/ml), yield coefficient ($1.67{\times}10^6$ IJs/g medium), and productivity ($1.32{\times}10^7$ IJs/l/day) were also achieved at enriched medium with soybean protein.