• Journal of Life Science
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• v.14 no.6 s.67
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• pp.997-1002
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• 2004

This paper deals with the WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that can provide the comprehensive information on Protein-protein interaction and function concerned with function analysis. Different from existing systems for protein function and protein-protein interaction analysis, which provide fragmentary information restricted to specific field, our system furnishes end-user with comprehensive and synthetic information on the input sequence to be analyzed, including function and annotation information, domain information, and interaction relationship information. The synthetic information that our system contains as local databases has been extracted from many resources related to function, annotation, motif and domain by various pre-processing. Employing our system, end-users can evaluate and judge the synthetic results to do protein interaction and function analysis effectively. In addition, the WASPIFA system is equipped with automatic system management and data update function that facilitates system manager to maintain and manage it efficiently.

• Proceedings of the KSRS Conference
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• v.2
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• pp.812-815
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• 2006

Proteins are essential agents for controlling, effecting and modulating cellular functions, and proteins with similar sequences have diverged from a common ancestral gene, and have similar structures and functions. Function prediction of unknown proteins remains one of the most challenging problems in bioinformatics. Recently, various computational approaches have been developed for identification of short sequences that are conserved within a family of closely related protein sequence. Protein function is often correlated with highly conserved motifs. Motif is the smallest unit of protein structure and function, and intends to make core part among protein structural and functional components. Therefore, prediction methods using data mining or machine learning have been developed. In this paper, we describe an approach for protein function prediction of motif-based models using data mining. Our work consists of three phrases. We make training and test data set and construct classifier using a training set. Also, through experiments, we evaluate our classifier with other classifiers in point of the accuracy of resulting classification.

• Journal of Life Science
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• v.18 no.3
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• pp.318-322
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• 2008

This paper deals with an efficient management and automatic update sub-system for WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that had been developed in the past and now provides the comprehensive information on protein-protein interaction and protein function. Protein interacting data has increased exponentially, so that it costs enormous time and effort. In other words, it is actually impossible to manually update and manage an analysis system based on protein interacting data. Even though there exists a good analysis system, it could be useless if it was able to be updated timely and managed properly. Unfortunately, in most cases, biologists without professional knowledge on their analysis systems have to cope with a great difficulty in running them. In this respect, the efficient management and automatic update subsystem of protein interacting and its related data has been developed to facilitate experimental biologists as well as bioinformaticians to update and manage the WASPIFA system.

• Mass Spectrometry Letters
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• v.12 no.3
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• pp.60-65
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• 2021

As a part of the Chromosome-centric Human Proteome Project (C-HPP), we have developed a few algorithms for accurate identification of missing proteins, alternative splicing variants, single amino acid variants, and characterization of function unannotated proteins. We have found missing proteins, novel and known ASVs, and SAAVs using LC-MS/MS data from human brain and olfactory epithelial tissue, where we validated their existence using synthetic peptides. According to the neXtProt database, the number of missing proteins in chromosome 11 shows a decreasing pattern. The development of genomic and transcriptomic sequencing techniques make the number of protein variants in chromosome 11 tremendously increase. We developed a web solution named as SAAvpedia for identification and function annotation of SAAVs, and the SAAV information is automatically transformed into the neXtProt web page using REST API service. For the 73 uPE1 in chromosome 11, we have studied the function annotaion of CCDC90B (NX_Q9GZT6), SMAP (NX_O00193), and C11orf52 (NX_Q96A22).

• Korean journal of food and cookery science
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• v.6 no.3 s.12
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• pp.33-41
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• 1990

This study was conducted to compare the characteristics of the ordinary soybean curd and 3 protein-adding soybean curds (soy protein, casein, gelatin). The sensory evaluation, textural analysis by Instron Universal Testing Machine & the microstructure analysis by SEM for 4 soybean curds were carried out. The results were as follows: 1. In sensory evaluation. 1) The differentiation of soybean curds was greatly explained by `hardness in mouth' through ANOVA test. 2) Discriminant analysis showed that the properties of casein soybean curd were different from those of other three soybean curds by discriminant function I, and the properties of soy protein soybean curd were slightly different from those of ordinary and gelatin soybean curds by discriminant function II. 2. In textural analysis by Instron, protein-adding soybean curds showed significantly lower hardness than ordinary soybean curd. 3. In microstructure analysis by SEM, soy protein soybean curd showed regular, good honeycomb-like network structure and other soybean curds showed lumpy network. The structure of gelatin soybean curd was slightly similar to that of ordinary soybean curd.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.259-271
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• 2008

We propose a protein function finding algorithm that is able to predict specific molecular function for unannotated proteins through domain analysis from protein-protein network. To do this, we first construct protein-protein interaction(PPI) network in Saccharomyces cerevisiae from MIPS databases. The PPI network(proteins; 3,637, interactions; 10,391) shows the characteristics of a scale-free network and a hierarchical network that proteins with a number of interactions occur in small and the inherent modularity of protein clusters. Protein-protein interaction databases obtained from a Y2H(Yeast Two Hybrid) screen or a composite data set include random false positives. To filter the database, we reconstruct the PPI networks based on the cellular localization. And then we analyze Hub proteins and the network structure in the reconstructed network and define structural modules from the network. We analyze protein domains from the structural modules and derive functional modules from them. From the derived functional modules with high certainty, we find tentative functions for unannotated proteins.

• Journal of Microbiology and Biotechnology
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• v.6 no.6
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• pp.468-473
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• 1996

The genome of tomato spotted wilt virus (TSWV) is composed of three RNA segments, S, M, and L RNA and the 5.0 kb M RNA encodes two glycoproteins Gl, G2 and NSm protein of unknown function. In an effort to investigate the function of the NSm protein, antibody was raised against NSm fusion protein overexpressed in Escherichia coli. This antibody was used to detect the NSm protein by using western blot analysis and electron microscopic observation after immunogold labelling. For the cloning of the NSm gene, total RNA extracted from a TSWV infected plant was used for cDNA synthesis and polymerase chain reaction (PCR) instead of going through time-consuming virus purification. A protein band specifically reacting to the NSm antibody was detected from TSWV inoculated plants. The NSm protein was detected in the cell wall fraction and in pellet from low speed centrifugation when the infected plant tissue was fractionated into 4 fractions. In the immuno-electron microscopic observation, gold particles were found around the plasmodesmata of infected plant tissue. These results suggest that the NSm protein of TSWV plays some role in cell-to-cell movement of this virus.

• BMB Reports
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• v.44 no.5
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• pp.347-351
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• 2011

The testis is major male gonad responsible for spermatogenesis and steroidogenesis. Much knowledge is still remained to be learned about the control of these events. In this study, we performed a comprehensive bioinformatics analysis on 1,196 mouse testis proteins screened from public protein database. Integrated function and pathway analysis were performed through Database for Annotation, Visualization and Integrated Discovery (DAVID) and ingenuity Pathway Analysis (IPA), and significant features were clustered. Protein membrane organization and gene density on chromosomes were analyzed and discussed. The enriched bioinformatics analysis could provide clues and basis to the development of diagnostic markers and therapeutic targets for infertility and male contraception.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1602-1610
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• 2004

As various genome projects have produced enormous amount of biosequence data, functional sequence analysis in terms of tile nucleic acid and protein becomes very significant. In functional genomics and proteomics, the functional analysis of each individual gene and protein remains a big challenge. Contrary to traditional studies, which regard proteins as not components of a whole protein interaction network but individual entities, recent studies have focused on examining functions and roles of each individual gene and protein in view of a whole life system. In this regard, it has been recognized as an appropriate method to analyze protein function on the basis of synthetic information of its interaction and domain modularity. In this context, this paper introduces the PIVS (Protein-protein interaction Inference & Visualization System), which predicts the interaction relationship of input proteins by taking advantage of information on homology degree, domain modules which input sequences contain, and protein interaction relationship. The information on domain modules can increase the accuracy of the function and interaction relationship analysis in terms of the specificity and sensitivity.

• Genomics & Informatics
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• v.18 no.4
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.