• Journal of Integrative Natural Science
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• v.2 no.2
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• pp.55-58
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• 2009

A ligand-receptor docking program is an indispensible tool in modern pharmaceutical design. An accurate prediction of small molecular docking pose to a receptor is essential in drug design as well as molecular recognition. An effective docking program requires the ability to locate a correct binding pose in a surprisingly complex conformational space. However, there is an inherent difficulty to predict correct binding pose. The odds are more demanding than finding a needle in a haystack. This mainly comes from the flexibility of both ligand and receptor. Because the searching space to consider is so vast, receptor rigidity has been often applied in docking programs. Even nowadays the receptor may not be considered to be fully flexible although there have been some progress in search algorithm. Improving the efficiency of searching algorithm is still in great demand to explore other applications areas with inherently flexible ligand and/or receptor. In addition to classical search algorithms such as molecular dynamics, Monte Carlo, genetic algorithm and simulated annealing, rather recent algorithms such as tabu search, stochastic tunneling, particle swarm optimizations were also found to be effective. A good search algorithm would require a good balance between exploration and exploitation. It would be a good strategy to combine algorithms already developed. This composite algorithms can be more effective than an individual search algorithms.

• BMB Reports
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• v.52 no.12
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• pp.718-727
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• 2019

Severe combined immunodeficiency (SCID) is a group of inherited disorders characterized by compromised T lymphocyte differentiation related to abnormal development of other lymphocytes [i.e., B and/or natural killer (NK) cells], leading to death early in life unless treated immediately with hematopoietic stem cell transplant. Functional NK cells may impact engraftment success of life-saving procedures such as bone marrow transplantation in human SCID patients. Therefore, in animal models, a T cell-/B cell-/NK cell+ environment provides a valuable tool for understanding the function of the innate immune system and for developing targeted NK therapies against human immune diseases. In this review, we focus on underlying mechanisms of human SCID, recent progress in the development of SCID animal models, and utilization of SCID pig model in biomedical sciences. Numerous physiologies in pig are comparable to those in human such as immune system, X-linked heritability, typical T-B+NK- cellular phenotype, and anatomy. Due to analogous features of pig to those of human, studies have found that immunodeficient pig is the most appropriate model for human SCID.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.178-184
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• 2020

The traditional methods for radiation shield design always only focus on either the structure or the components of the shields rather than both of them at the same time, which largely affects the shielding performance of the facilities, so in this paper, a novel method for designing the structure and components of shields simultaneously is put forward to enhance the shielding ability. The method is developed by using the genetic algorithm (GA) and the MCNP software. In the research, six types of shielding materials with different combinations of elements such as polyethylene (PE), lead (Pb) and Boron compounds are applied to the radiation shield design, and the performance of each material is analyzed and compared. Then two typical materials are selected based on the experiment result of the six samples, which are later verified by the Compact Accelerator Neutron Source (CANS) facility. By using this method, the optimal result can be reached rapidly, and since the design progress is semi-automatic for most procedures are completed by computer, the method saves time and improves accuracy.

• Journal of Plant Biotechnology
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• v.37 no.4
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• pp.400-407
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• 2010

Zoysiagrass (Zoysia japonica Steud.), also called Korean or Japanese lawngrass, is the most popular warm-season turfgrass in Korea and is widely used for home lawns, parks, roadsides, golf courses and athletic fields. Its use is rapidly expanding in Korea and the other countries, due to its excellent characteristics which include tolerance to heat, drought and salinity. As the utilization area of this turfgrass increases, there is an increase in the demand for improved cultivars with disease and insect tolerance or with herbicide-tolerance or with extended greening periods. Conventional breeding methods have been used to improve the traits described above with limited success. However, with the advances in biotechnology, genetic transformation can be utilized for turfgrass improvement. In this paper, we review recent progress in biotechnological improvement of zoysiagrass and discuss future molecular breeding of this species.

• Journal of mucopolysaccharidosis and rare diseases
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• v.3 no.2
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• pp.41-43
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• 2017

Prader-Willi syndrome (PWS) is a genetic disorder that is considered, especially on child, to cause poor feeding, hypotonia, failure to thrive, developmental delay and hypogonadism which is known to affect between 1 in 10,000 and 30,000 people. The children with PWS are viewed as affected by growth hormone (GH) insufficiency, although the exact mechanisms of GH deficiency are not fully understood. However, the benefits of GH treatment in children with PWS are well established. Myers, et al. (2006), Grugni, et al. (2016) indicated its positive effects on linear growth, body composition, motor function, respiratory function and psychomotor development. Despite of its effectiveness and advantages had been well known and proven in many other studies, there is only one recombinant GH product that is approved for PWS in Korea, $Genotropin^{(R)}$, till now. A phase III clinical study of GH treatment with $Eutropin^{TM}$, in 34 Korean PWS children is in progress, which is expected to have comparable effects and safety profile with the active control by assessing auxological changes such as height standard deviation score, body composition changes such as lean body mass and percent body fat, motor and cognitive development using Bayley scale, and safety profiles.

• Experimental and Molecular Medicine
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• v.50 no.12
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• pp.10.1-10.11
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• 2018

Cancer growth and progression are associated with immune suppression. Cancer cells have the ability to activate different immune checkpoint pathways that harbor immunosuppressive functions. Monoclonal antibodies that target immune checkpoints provided an immense breakthrough in cancer therapeutics. Among the immune checkpoint inhibitors, PD-1/PD-L1 and CTLA-4 inhibitors showed promising therapeutic outcomes, and some have been approved for certain cancer treatments, while others are under clinical trials. Recent reports have shown that patients with various malignancies benefit from immune checkpoint inhibitor treatment. However, mainstream initiation of immune checkpoint therapy to treat cancers is obstructed by the low response rate and immune-related adverse events in some cancer patients. This has given rise to the need for developing sets of biomarkers that predict the response to immune checkpoint blockade and immune-related adverse events. In this review, we discuss different predictive biomarkers for anti-PD-1/PD-L1 and anti-CTLA-4 inhibitors, including immune cells, PD-L1 overexpression, neoantigens, and genetic and epigenetic signatures. Potential approaches for further developing highly reliable predictive biomarkers should facilitate patient selection for and decision-making related to immune checkpoint inhibitor-based therapies.

• Journal of Integrative Natural Science
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• v.2 no.4
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• pp.243-251
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• 2009

This paper surveys some researches to accomplish on bioinformatics. These researches wish to propose a database architecture combining a general view of bioinformatics data as a graph of data objects and data relationships, with the efficiency and robustness of data management and query provided by indexing and generic programming techniques. Here, these invert the role of the index, and make it a first-class citizen in the query language. It is possible to do this in a structured way, allowing users to mention indexes explicitly without yielding to a procedural query model, by converting functional relations into explicit functions. In the limit, the database becomes a graph, in which the edges are these indexes. Function composition can be specified either explicitly or implicitly as path queries. The net effect of the inversion is to convert the database into a hyperdatabase: a database of databases, connected by indexes or functions. The inversion approach was motivated by their work in biological databases, for which hyperdatabases are a good model. The need for a good model has slowed progress in bioinformatics.

• BMB Reports
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• v.52 no.11
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• pp.625-634
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• 2019

Severe combined immunodeficiency (SCID) is a group of inherited disorders characterized by compromised T lymphocyte differentiation related to abnormal development of other lymphocytes [i.e., B and/or natural killer (NK) cells], leading to death early in life unless treated immediately with hematopoietic stem cell transplant. Functional NK cells may impact engraftment success of life-saving procedures such as bone marrow transplantation in human SCID patients. Therefore, in animal models, a T cell-/B cell-/NK cell＋ environment provides a valuable tool for understanding the function of the innate immune system and for developing targeted NK therapies against human immune diseases. In this review, we focus on underlying mechanisms of human SCID, recent progress in the development of SCID animal models, and utilization of SCID pig model in biomedical sciences. Numerous physiologies in pig are comparable to those in human such as immune system, X-linked heritability, typical T-B＋NK- cellular phenotype, and anatomy. Due to analogous features of pig to those of human, studies have found that immunodeficient pig is the most appropriate model for human SCID.

• Journal of Microbiology and Biotechnology
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• v.29 no.5
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• pp.667-686
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• 2019

Streptomyces are attractive microbial cell factories that have industrial capability to produce a wide array of bioactive secondary metabolites. However, the genetic potential of the Streptomyces species has not been fully utilized because most of their secondary metabolite biosynthetic gene clusters (SM-BGCs) are silent under laboratory culture conditions. In an effort to activate SM-BGCs encoded in Streptomyces genomes, synthetic biology has emerged as a robust strategy to understand, design, and engineer the biosynthetic capability of Streptomyces secondary metabolites. In this regard, diverse synthetic biology tools have been developed for Streptomyces species with technical advances in DNA synthesis, sequencing, and editing. Here, we review recent progress in the development of synthetic biology tools for the production of novel secondary metabolites in Streptomyces, including genomic elements and genome engineering tools for Streptomyces, the heterologous gene expression strategy of designed biosynthetic gene clusters in the Streptomyces chassis strain, and future directions to expand diversity of novel secondary metabolites.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.