• Bioinformatics and Biosystems
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• 제1권2호
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• pp.128-133
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• 2006

This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

• 한국식품저장유통학회지
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• 제31권1호
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• pp.64-79
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• 2024

The rise in popularity of vegetarian and plant-based diets has led to extensive research into plant-based whipped creams. Whipped cream is an oil-in-water emulsion that creates foam through whipping, stabilizing the foam with proteins and fats. Pea protein is an excellent emulsifier and foaming agent among plant-based proteins, but its application in whipped cream is currently limited. The objective of this study was to investigate the quality characteristics of plant-based whipped cream made with ultrasonicated pea protein. The whipped creams were evaluated based on their quality characteristics. A commercially available dairy whipped cream (CON) was used as a control. Plant-based creams were evaluated using pea protein solution, cocoa butter, and canola oil to produce un-ultrasonicated pea protein whipped cream (PP) and ultrasonicated pea protein whipped cream (UPP) at 360 W for 6 min. UPP significantly reduced whipping time and foam drainage compared with CON and PP, resulting in significantly increased overrun, fat destabilization, and hardness. Optical microscopy showed that UPP had smaller fat globules and bubble size than PP. The fat globules of UPP and CON were mostly below 5 ㎛, whereas those of PP were distributed at 5-20 ㎛. Finally, ultrasonication significantly improved the overrun, foam drainage, fat destabilization, and hardness of UPP, which are significant quality characteristics of whipped creams. Therefore, ultrasonicated plant-based pea protein whipped cream is believed to be a viable alternative to dairy whipped cream.

• 한국재료학회지
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• 제28권3호
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• pp.182-188
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• 2018

Nitrogen (N)-doped protein-based carbon as platinum (Pt) catalyst supports from tofu for oxygen reduction reactions are synthesized using a carbonization and reduction method. We successfully prepare 5 wt% Pt@N-doped protein-based carbon, 10 wt% Pt@N-doped protein-based carbon, and 20 wt% Pt@N-doped protein-based carbon. The morphology and structure of the samples are characterized by field emission scanning electron microscopy and transmission electron micro scopy, and crystllinities and chemical bonding are identified using X-ray diffraction and X-ray photoelectron spectroscopy. The oxygen reduction reaction are measured using a linear sweep voltammogram and cyclic voltammetry. Among the samples, 10 wt% Pt@N-doped protein-based carbon exhibits exellent electrochemical performance with a high onset potential of 0.62 V, a high $E_{1/2}$ of 0.55 V, and a low ${\Delta}E_{1/2}=0.32mV$. Specifically, as compared to the commercial Pt/C, the 10 wt% Pt@N-doped protein-based carbon had a similar oxygen reduction reaction perfomance and improved electrochemical stability.

• 약학회지
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• 제35권6호
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• pp.461-465
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• 1991

Silica-based gel filtration chromatography has been used to characterize molecular weight of proteins. However, the molecular weight measured by this method was distorted by protein-silica interactions like hydrophobic and electrostatic forces. Therefore, we characterized protein-silica interaction using two forms of phytochrome (124 kDa) having different hydrophobicity and surface charge. PH and ionic strength affected the retention time of phytochrome suggesting that electrostatic force is the major interaction between protein and silica surface.

• 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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(B)
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• pp.426-428
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• 2012

In order to identify proteins that are present in biological samples, these samples are separated and analyzed under the sequential procedure as follows: protein purification and digestion, peptide fragmentation by tandem mass spectrometry (MS/MS) which breaks peptides into fragments, peptide identification, and protein identification. One of the widely used methods for protein identification is based on probabilistic approaches such as ProteinProphet and BaysPro. However, they do not consider the difference in peptide identification probabilities according to their length. Here, we propose a probabilistic graphical model-based approach to protein identification from MS/MS data considering peptide identification probabilities, number of sibling peptides, and peptide length. We compared our approach with ProteinProphet using a yeast MS/MS dataset. As a result, our model identified 27 more proteins than ProteinProphet at 1% of FDR (false discovery rate), confirming the importance of peptide length information in protein identification.

• Asian-Australasian Journal of Animal Sciences
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• 제11권2호
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• pp.107-116
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• 1998

Measurement of DCP is considered inadequate and unsatisfactory means of assessing the protein value of the diet as no distinction is made between the digestion in ferestomach and in the small intestine. Protein meals should be classified on the basis of rumen degradable protein (RDP) and rumen undegradable protein (UDP). Usually, protein meals naturally available with high level of UDP or bypass protein value should be preferred for incorporation in the diet of lactating and growing animals. However, if such resources are non-available or are expensive, protein meals having high degradability can be carefully subjected to heat or formaldehyde treatment to achieve desired level of rumen bypassability. Various studies conducted the world over have revealed that bypass protein feeding to ruminants, especially when animals are fed on crop residue based basal diet, help increasing feed conversion efficiency in growing and lactating ruminants.

• 정보처리학회논문지B
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• 제12B권2호
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• pp.209-214
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• 2005

단백질 상호작용 데이터는 현 생물정보학에서 기능이 알려져 있지 않은 단백질의 기능 예측에 높은 신뢰성이 있는 프로티오믹스의 계산 모델에 이용되고 있다. 단백질 기능 예측 관련 연구로는 guilt-by-association 개념을 바탕으로 대규모의 단순 2차원 단백질-단백질 상호작용 맵을 이용하고 있다. 본 논문에서는 단백질-단백질 상호작용 데이터를 이용한 그래프 기반 기능 예측 방법인 neighbor-counting, $\chi^2$-통계치 예측 모델을 살펴보고 대량의 상호작용 데이터로부터 빠른 기능예측에 효과적인 알고리즘을 제안한다. 제안하는 알고리즘은 단백질 상호작용 맵, 서열 유사성 및 경험적 전문가 지식을 이용하는 그래프 기반 모델이다. 제안된 알고리즘은 Yeast 단백질의 기능 예측을 수행하였으며, neighbor-counting, $\chi^2$-통계치 모델의 실험 결과와 비교되었다.

• Macromolecular Research
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• 제11권1호
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• pp.53-61
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• 2003

A molecular-thermodynamic model is developed for the salt-induced protein precipitation. The protein molecules interact through four intermolecular potentials. An equation of state is derived based on the statistical mechanical perturbation theory with the modified Chiew's equation for the fluid phase, Young's equation for the solid phase as the reference system and a perturbation based on the protein-protein effective two body potential. The equation of state provides an expression for the chemical potential of the protein. In a single protein system, the phase separation is represented by fluid-fluid equilibria. The precipitation behaviors are simulated with the partition coefficient at various salt concentrations and degree of pre-aggregation effect for the protein particles. In a binary protein system, we regard the system as a fluid-solid phase equilibrium. At equilibrium, we compute the reduced osmotic pressure-composition diagram in the diverse protein size difference and salt concentrations.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.