• Fisheries and Aquatic Sciences
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• 제27권3호
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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.

• 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.

• Journal of Microbiology and Biotechnology
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• 제27권5호
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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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• 제19권1호
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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.

• 대한기계학회논문집A
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• 제30권7호
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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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• 제49권11호
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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.

• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권4호
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• pp.163-168
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• 2014

단백질 분자 내에서 ${\alpha}$-헬릭스는 단백질의 구조나 기능, 그리고 다른 단백질과의 결합, 활성 조절 등에 있어 중요한 역할을 하며, 이에 따라 헬릭스에 대한 구조적인 분석이 연구되어 왔다. ${\alpha}$-헬릭스는 그 중심축을 기준으로 다른 ${\alpha}$-헬릭스와의 상호위치를 평가하기 때문에 길게 휘어지거나 꺾인 ${\alpha}$-헬릭스들을 한 개의 헬릭스로 해석할 경우에는 단백질의 구조 분석에 있어서 상당한 오차가 발생할 수 있다. 본 논문에서는 PDB 파일 내에 표시된 단백질 분자의 ${\alpha}$-헬릭스를 주어진 오차 범위 내에서 여러 개의 곧은 형태의 헬릭스로 재구성하는 알고리즘을 제안한다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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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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• 제21권8호
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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.