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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 8, August 2011
Algorithm of Insulin Human P01308
Full Text(PDF, 3000)  PP.  
Lutvo Kuric
Discrete Code, Human Insulin, Insulin Model, Insulin Code.
The modern science mainly treats the biochemical basis of sequencing in bio-macromolecules and processes in medicine and biochemistry. One can ask weather the language of biochemistry is the adequate scientific language to explain the phenomenon in that science. Is there maybe some other language, out of biochemistry, that determines how the biochemical processes will function and what the structure and organization of life systems will be? The research results provide some answers to these questions. They reveal to us that the process of sequencing in bio-macromolecules is conditioned and determined not only through biochemical, but also through cybernetic and information principles. Many studies have indicated that analysis of protein sequence codes and various sequence-based prediction approaches, such as predicting drug-target interaction networks (He et al., 2010), predicting functions of proteins (Hu et al., 2011; Kannan et al., 2008), analysis and prediction of the metabolic stability of proteins (Huang et al., 2010), predicting the network of substrate-enzyme-product triads (Chen et al., 2010), membrane protein type prediction (Cai and Chou, 2006; Cai et al., 2003; Cai et al., 2004), protein structural class prediction (Cai et al., 2006; Ding et al., 2007), protein secondary structure prediction (Chen et al., 2009; Ding et al., 2009b), enzyme family class prediction (Cai et al., 2005; Ding et al., 2009a; Wang et al., 2010), identifying cyclin proteins (Mohabatkar, 2010), protein subcellular location prediction (Chou and Shen, 2010a; Chou and Shen, 2010b; Kandaswamy et al., 2010; Liu et al., 2010), among many others as summarized in a recent review (Chou, 2011), can timely provide very useful information and insights for both basic research and drug design and hence are widely welcome by science community. The present study is attempted to develop a novel sequence-based method for studying insulin in hopes that it may become a useful tool in the relevant areas.
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[3] Cai, Y.D., Zhou, G.P., and Chou, K.C., 2005. Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. J Theor Biol 234, 145-149.

[4] Cai, Y.D., Feng, K.Y., Lu, W.C., and Chou, K.C., 2006. Using LogitBoost classifier to predict protein structural classes. J Theor Biol 238, 172-176.

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[7] Chen, L., Feng, K.Y., Cai, Y.D., Chou, K.C., and Li, H.P., 2010. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics 11, 293.

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[11] Ding, H., Luo, L., and Lin, H., 2009a. Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein & Peptide Letters 16, 351-355.

[12] Ding, Y.S., Zhang, T.L., and Chou, K.C., 2007. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein & Peptide Letters 14, 811-815.

[13] Ding, Y.S., Zhang, T.L., Gu, Q., Zhao, P.Y., and Chou, K.C., 2009b. Using maximum entropy model to predict protein secondary structure with single sequence. Protein & Peptide Letters 16, 552-560.

[14] He, Z.S., Zhang, J., Shi, X.H., Hu, L.L., Kong, X.G., Cai, Y.D., and Chou, K.C., 2010. Predicting drug-target interaction networks based on functional groups and biological features. PLoS ONE 5, e9603.

[15] Hu, L., Huang, T., Shi, X., Lu, W.C., Cai, Y.D., and Chou, K.C., 2011. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties PLoS ONE 6, e14556.

[16] Huang, T., Shi, X.H., Wang, P., He, Z., Feng, K.Y., Hu, L., Kong, X., Li, Y.X., Cai, Y.D., and Chou, K.C., 2010. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks PLoS ONE 5, e10972.

[17] Kandaswamy, K.K., Pugalenthi, G., Moller, S., Hartmann, E., Kalies, K.U., Suganthan, P.N., and Martinetz, T., 2010. Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition. Protein and Peptide Letters 17, 1473-1479.

[18] Kannan, S., Hauth, A.M., and Burger, G., 2008. Function prediction of hypothetical proteins without sequence similarity to proteins of known function. Protein & Peptide Letters 15, 1107-1116.

[19] Liu, T., Zheng, X., Wang, C., and Wang, J., 2010. Prediction of Subcellular Location of Apoptosis Proteins using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation. Protein & Peptide Letters 17, 1263-9.

[20] Mohabatkar, H., 2010. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein & Peptide Letters 17, 1207-1214.

[21] Wang, Y.C., Wang, X.B., Yang, Z.X., and Deng, N.Y., 2010. Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature. Protein & Peptide Letters 17, 1441-1449.

[22] Xiao, X., and Chou, K.C., 2007. Digital coding of amino acids based on hydrophobic index. Protein & Peptide Letters 14, 871-875.

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