• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

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 probability 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 for 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 teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• BMB Reports
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• v.33 no.2
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• pp.188-192
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• 2000

Recombinant protein G has been utilized in the purification of antibodies from various mammalian species based on the interaction of antibodies with protein G. The interaction between immunoglobulin and protein G may not be restricted to the Fc protion of antibodies, as many different $F(ab)_2$ or Fab fragments can also bind to protein G. I found both FAb $F(ab)_2$ of 145-2C11, a hamster anti-mouse $CD3{\varepsilon}$ antibody, bound to the protein G-sepharose. Interestingly, Fab and $F(ab)_2$ of 145-2C11 did not bind to the protein A-sepharose. The binding of Fab and $F(ab)_2$ of 145-2C11 to protein G provided a useful method to remove proteases, chopped fragments of the Fc region, and other contaminating proteins. The remaining intact antibody in the protease reaction mixture can be removed by using a protein A-sepharose, because the Fab and $F(ab)_2$ portions of 145-2C11 did not bind to protein A-sepharose. The specific binding of Fab and $F(ab)_2$ portions of 145-sC11 to a protein G-sepharose (though not to a protein A-sepharose) and binding of intact 145-2C11 to both protein A- and G-sepharose will be useful in developing an effective purification protocol for Fab and $F(ab)_2$ portions of 145-2C11.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4621-4625
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• 2013

The purpose of this study was to construct a protein-protein interaction (PPI) network related to oral squamous cell carcinoma (OSCC). Each protein was ranked and those most associated with OSCC were mined within the network. First, OSCC-related genes were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Then they were mapped to their protein identifiers and a seed set of proteins was built. The seed proteins were expanded using the nearest neighbor expansion method to construct a PPI network through the Online Predicated Human Interaction Database (OPHID). The network was verified to be statistically significant, the score of each protein was evaluated by algorithm, then the OSCC-related proteins were ranked. 38 OSCC related seed proteins were expanded to 750 protein pairs. A protein-protein interaction nerwork was then constructed and the 30 top-ranked proteins listed. The four highest-scoring seed proteins were SMAD4, CTNNB1, HRAS, NOTCH1, and four non-seed proteins P53, EP300, SMAD3, SRC were mined using the nearest neighbor expansion method. The methods shown here may facilitate the discovery of important OSCC proteins and guide medical researchers in further pertinent studies.

• Food Science of Animal Resources
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• v.39 no.2
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• pp.229-239
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• 2019

The objectives of this study were to determine the interaction between porcine myofibrillar proteins and various gelatins (bovine hide, porcine skin, fish skin, and duck skin gelatins) and their impacts on gel properties of porcine myofibrillar proteins. Porcine myofibrillar protein was isolated from pork loin muscle (M. longissimus dorsi thoracis et lumborum). Control was prepared with only myofibrillar protein (60 mg/mL), and gelatin treatments were formulated with myofibrillar protein and each gelatin (9:1) at the same protein concentration. The myofibrillar protein-gelatin mixtures were heated from $10^{\circ}C$ to $75^{\circ}C$ ($2^{\circ}C/min$). Little to no impacts of gelatin addition on pH value and color characteristics of heat-induced myofibrillar protein gels were observed (p>0.05). The addition of gelatin slightly decreased cooking yield of heat-induced myofibrillar protein gels, but the gels showed lower centrifugal weight loss compared to control (p<0.05). The addition of gelatin significantly decreased hardness, cohesiveness, gumminess, and chewiness of heat-induced myofibrillar gels. Further, sodium dodecyl poly-acrylamide gel electrophoresis (SDS-PAGE) showed no interaction between myofibrillar proteins and gelatin under non-thermal conditions. Only a slight change in the endothermic peak (probably myosin) of myofibrillar protein-gelatin mixtures was found. The results of this study show that the addition of gelatin attenuated the water-holding capacity and textural properties of heat-induced myofibrillar protein gel. Thus, it could be suggested that well-known positive impacts of gelatin on quality characteristics of processed meat products may be largely affected by the functional properties of gelatin per se, rather than its interaction with myofibrillar proteins.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.365-377
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• 2006

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.194-198
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• 2017

In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.193-207
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• 2009

Intracellular signal transduction is achieved by protein-protein interaction. In this paper, we suggest high performance algorithm based on Yeast protein-protein interaction and protein location information. We compare if pathways predicted with high valued weights indicate similar tendency with pathways provided in KEGG. Furthermore, we suggest extracted results, which can imply a discovery of new signaling pathways that is yet proven through experiments. This will be a good basis for research to discover new protein signaling pathways and unknown functions of established proteins.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.163-166
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• 2016

Hydrophobicity is the key concept to understand the water plays in protein folding, protein aggregation, and protein-protein interaction. Traditionally, the hydrophobicity of protein is defined based on the scales of the hydrophobicity of residue, assuming that the hydrophobicity of free amino acids is maintained. Here, we explore how the hydrophobicity of constituting amino acids in protein rely on the protein context, in particular, on the total charge and secondary structures of a protein. To this end, we calculate and investigate the hydration free energy of three short proteins based on the integral-equation theory of liquids. We find that the hydration free energy of charged amino acids is significantly affected by the protein total charge and exhibits contrasting behavior depending on the protein total charge being positive or negative. We also observe that amino acids in the ${\beta}-sheets$ display more enhanced the hydrophobicity than amino acids in the loop, whereas those in the ${\alpha}-helix$ do not clearly show such a tendency. And the salt-bridge forming amino acids also exhibit increase of the hydrophobicity than that with no salt bridge. Our results provide novel insights into the hydrophobicity of amino acids, and will be valuable for rationalizing and predicting the strength of water-mediated interaction involved in the biological activity of proteins.