• Genomics & Informatics
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• v.15 no.4
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• pp.162-169
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• 2017

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.207-213
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• 2009

The bundling process is the final step in vegetable manufacturing, however, the process is a little difficult to be automatized, because vegetable has the physical properties of roughness, softness, and fragility etc. In this paper, we proposed an automatic bundling mechanism for vegetable based on the heat melt sticking. The proposed mechanism consists of three modules, one module is the moving part for aligning of the vegetable shape and adjusting of the vegetable tension, second module is the arm driving part for the vegetable binding and the band roll releasing, and third module is band joining, band cutting, and band feeding part for the vegetable binding continuously. Through this research, Using the SMO(SimDesigner Motion) module, we optimize condition of mechanical movement of the bundling mechanism. This bundling system designed in order to binding 288 bundle/hour.

• Molecules and Cells
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• v.27 no.6
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• pp.629-634
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• 2009

G protein-coupled receptors (GPCRs) are part of multi-protein networks called 'receptosomes'. These GPCR interacting proteins (GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed the identification of novel GPCR-PDZ domain protein interactions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.668-673
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• 1999

• BMB Reports
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• v.28 no.2
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• pp.94-99
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• 1995

The properties of binding sites in the active site of $Zn^{2+}$-glycerophosphocholine cholinephosphodiesterase were examined using substrates and inhibitors of the enzyme. Phosphodiesterase hydrolyzed p-nitrophenylphosphocholine, p-aminophenylphosphocholine, and glycerophosphocholine, but did not hydrolyze either acylated glycerophosphocholine or bis (p-nitrophenyl)phosphate, suggesting a size limitation for interaction with a glyceryl moiety-binding subsite. The hydrolysis of p-nitrophenylphosphocholine was competitively inhibited by glycerophosphocholine and p-aminophenylphosphocholine, while glycerophosphoethanolamine was a weak inhibitor. The enzyme was also inhibited by choline, but not by ethanolamine. Thiocholine, a much more potent inhibitor than choline, was more inhibitory than cysteamine, suggesting a strict specificity of an anionic subsite adjacent to a $Zn^{2+}$ subsite. Of all oxyanions tested, the tellurite ion was found to strongly inhibit the enzyme by binding to a $Zn^{2+}$ subsite. The inhibitory role of tellurite was synergistically enhanced by tetraalkylammonium salts, but not by glycerol. Deactivation of the enzyme by diethylpyrocarbonate was partially protected by choline, but not by glycerophosphate. It is suggested that the active site of phosphodiesterase contains three binding subsites.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.56-62
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• 2008

An Allium-odorum is difficult material to handle because it is soft and weak mechanically. For bundling work of allium-odorum, we must grip in the bundle shape of unit weight. However it is difficult to grip allium-odorum by hand because the bundle bulk is very big. Especially, in packing work, the bundle shape of allium-odorum package is more important than other vegetables because the sale price depend on the bundle shape. In this paper, we propose bundling mechanism for a rectangular shape, and semi-auto bundling mechanism by a eccentric roller and a triangular link. We carry out mechanical model and analysis respectively using the CATIA V5 and SimDesigner. We have manufactured the prototype of the semi-automatic bundling machine, and got satisfied result through field test. This machine is in the process of commercialization.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.07a
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• pp.245-250
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• 2000

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.18-21
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• 2002

We present a phase covariance model that can well represent stimulus intensity as well af feature binding (i.e., covariance). The model is represented by complex neural equations, which is a mean field model of stochastic neural model such as Boltzman machine and sigmoid belief networks.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.45-52
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• 2012

T cells induce immune responses and thereby eliminate infected micro-organisms when peptides from the microbial proteins are bound to HLAs in the host cell surfaces, It is known that the more stable the binding of peptide to HLA is, the stronger the T cell response gets to remove more effectively the source of infection. Accordingly, if peptides (HLA binder) which can be bound stably to a certain HLA are found, those peptieds are utilized to the development of peptide vaccine to prevent infectious diseases or even to cancer. However, HLA is highly polymorphic so that HLA has a large number of alleles with some frequencies even in one population. Therefore, it is very inefficient to find the peptides stably bound to a number of HLAs by testing random possible peptides for all the various alleles frequent in the population. In order to solve this problem, computational methods have recently been developed to predict peptides which are stably bound to a certain HLA. These methods could markedly decrease the number of candidate peptides to be examined by biological experiments. Accordingly, this paper not only introduces a method of machine learning to predict peptides binding to an HLA, but also suggests a new prediction model so called 'knowledge-based genetic algorithm' that has never been tried for HLA binding peptide prediction. Although based on genetic algorithm (GA). it showed more enhanced performance than GA by incorporating expert knowledge in the process of the algorithm. Furthermore, it could extract rules predicting the binding peptide of the HLA alleles common in Koreans.

• Journal of KIISE:Software and Applications
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• v.28 no.12
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• pp.955-965
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• 2001

We propose a systematic method of compiling Haskell based on the spineless Tagless G-machine (STGM) for the Java, Virtual Machine (JVM) We introduce an intermediate language called L-code to identify each micro-operation of the machine by its instruction, Each macro operation of the machine is identified by a binding Each instruction of the L-code can be easily translated into Java statements. After our determination on representation and L-code program from a STG program is translated into Java program according to out compilation rules. Our experiment shows that the execution times of translated benchmarks are competitive compared with those in Haskell interpreter Hugs, particularly when Glasgow Haskell compiler's STG -level optimizations are applied.