• ETRI Journal
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• v.37 no.4
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• pp.813-823
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• 2015

Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features - protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F-measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated - information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.

• 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.

• Experimental and Molecular Medicine
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• v.50 no.11
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• pp.12.1-12.12
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• 2018

Drugs targeting the epidermal growth factor receptor (EGFR), such as cetuximab and panitumumab, have been prescribed for metastatic colorectal cancer (CRC), but patients harboring KRAS mutations are insensitive to them and do not have an alternative drug to overcome the problem. The levels of ${\beta}$-catenin, EGFR, and RAS, especially mutant KRAS, are increased in CRC patient tissues due to mutations of adenomatous polyposis coli (APC), which occur in 90% of human CRCs. The increases in these proteins by APC loss synergistically promote tumorigenesis. Therefore, we tested KYA1797K, a recently identified small molecule that degrades both ${\beta}$-catenin and Ras via $GSK3{\beta}$ activation, and its capability to suppress the cetuximab resistance of KRAS-mutated CRC cells. KYA1797K suppressed the growth of tumor xenografts induced by CRC cells as well as tumor organoids derived from CRC patients having both APC and KRAS mutations. Lowering the levels of both ${\beta}$-catenin and RAS as well as EGFR via targeting the $Wnt/{\beta}$-catenin pathway is a therapeutic strategy for controlling CRC and other types of cancer with aberrantly activated the $Wnt/{\beta}$-catenin and EGFR-RAS pathways, including those with resistance to EGFR-targeting drugs attributed to KRAS mutations.

• 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.

• Interdisciplinary Bio Central
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• v.1 no.2
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• pp.8.1-8.6
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• 2009

Introduction: It is a challenge to design a protein score function which stabilizes the native structures of many proteins simultaneously. The coarse-grained description of proteins to construct the pairwise-contact score function usually ignores the backbone directionality of protein structures. We propose a new two-body score function which stabilizes all native states of 1,006 proteins simultaneously. This two-body score function differs from the usual pairwise-contact functions in that it considers two adjacent amino acids at two ends of each peptide bond with the backbone directionality from the N-terminal to the C-terminal. The score is a corresponding propensity for a directional alignment of two adjacent amino acids with their local environments. Results and Discussion: We show that the construction of a directional adjacency-score function was achieved using 1,006 training proteins with the sequence homology less than 30%, which include all representatives of different protein classes. After parameterizing the local environments of amino acids into 9 categories depending on three secondary structures and three kinds of hydrophobicity of amino acids, the 32,400 adjacency-scores of amino acids could be determined by the perceptron learning and the protein threading. These could stabilize simultaneously all native folds of 1,006 training proteins. When these parameters are tested on the new distinct 382 proteins with the sequence homology less than 90%, 371 (97.1%) proteins could recognize their native folds. We also showed using these parameters that the retro sequence of the SH3 domain, the B domain of Staphylococcal protein A, and the B1 domain of Streptococcal protein G could not be stabilized to fold, which agrees with the experimental evidence.

• BMB Reports
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• v.42 no.11
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• pp.697-704
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• 2009

Membrane proteins play important roles in the biology of the cell, including intercellular communication and molecular transport. Their well-established importance notwithstanding, the high-resolution structures of membrane proteins remain elusive due to difficulties in protein expression, purification and crystallization. Thus, accurate prediction of membrane protein topology can increase the understanding of membrane protein function. Here, we provide a brief review of the diverse computational methods for predicting membrane protein structure and function, including recent progress and essential bioinformatics tools. Our hope is that this review will be instructive to users studying membrane protein biology in their choice of appropriate bioinformatics methods.

• Proceedings of the Zoological Society Korea Conference
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• 1998.10b
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• pp.277.1-277
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• 1998

No Abstract, See Full Text

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.431-436
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• 2005

Living cells are sustained not by individual activities but rather by coordinated summative efforts of different biological functional modules. While recent research works have focused largely on finding individual functional modules, this paper attempts to explore the connections or relationships between different cellular functions through cross-function domain interaction maps. Exploring such a domain interaction map can help understand the underlying inter-function communication mechanisms. To construct a cross-function domain interaction map from existing genome-wide protein-protein interaction datasets, we propose a two-step procedure. First, we infer conserved domain-domain interactions from genome-wide protein-protein interactions of yeast, worm and fly. We then build a cross-function domain interaction map that shows the connections of different functions through various conserved domain interactions. The domain interaction maps reveal that conserved domain-domain interactions can be found in most detected cross-functional relationships and a f9w domains play pivotal roles in these relationships. Another important discovery in the paper is that conserved domains correspond to highly connected protein hubs that connect different functional modules together.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.1-6
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• 2006

The prediction of protein function basically make use of a protein-protein interaction map based on the concept of guilt-by-association. The method however cannot determine the functions of proteins in case that the target protein does not interact with proteins with known functions directly. This paper studies protein function prediction considering the given problem as a K-class classification problem and proposes a predictive approach utilizing a modular neural network. The proposed method uses interaction data and protein related attributes as well. The experimental results demonstrate that the proposed approach can predict the functional roles of Yeast proteins whose interaction knowledge is not known and shows better performance than the graph-based models that use protein interaction data.

• Journal of Nutrition and Health
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• v.34 no.8
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• pp.843-849
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• 2001

This study was performed to investigate the effect of n-3 fatty acids and dietary protein levels on renal function. Fifteen-month old male Sprague-Dawley rats were divided into 4 diet groups. Two-month old rats were used as a control group. The experimental diets contained either a% or 25% casein and lipid levels of the diets were 20% by weight. For the control group, the lipid was composed of beef tallow and corn oil on a 1:1 basis, and fish oil was comprised 75% of the fat mixture for the fish oil group. Rats were fed the diets ad libitum for 8 weeks. GFR and urinary protein excretion were higher in high protein groups, while fish oil exhibited no effects. Renal medulla TXB$_2$and PGE$_2$ concentrations tended to be higher in high protein groups and lower in fish oil groups. Light microscopic examinations showed that glomerulosclerosis, tubular atrophy, tubular cast, interstitial inflammation and interstitial fibrosis fended to be higher in aged rats and in high protein groups and lower in fish oil groups. Serum levels of total lipid, triglyceride and total cholesterol were higher in aged rats and lower in fish oil groups while serum HDL-cholesterol level was higher in young rats and in fish oil groups. However, dietary protein level had no effect on serum lipid levels. Serum TBARS concentration was higher in aged rats and in fish oil groups. In conclusion, fish oil caused changes in serum lipid concentrations and eicosanoids metabolism. The effect of fish oil on renal function was less obvious than dietary protein. However, fish oil seemed to be effective in lessening deterioration of renal function due to aging and/or high protein diets through changes in lipid and eicosanoids metabolism.