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Research Article Open access CC BY 3.0

Algorithm of Insulin Human P01308 - Position 102 - Determinante 2029

Lutvo Kurić

International Journal of Biochemistry Research & Review · pp. 82–107 · Published 28 Aug 2011

10.9734/IJBCRR/2011/503

Abstract

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.

Human insulin algorithm insulin model insulin code determinante of insulin

References (24)

  1. 1 Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization [DOI]
  2. 2 Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms [DOI]
  3. 3 Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features [DOI]
  4. 4 Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition [DOI]
  5. 5 Prediction of Cyclin Proteins Using Chous Pseudo Amino Acid Composition [DOI]
  6. 6 Using LogitBoost classifier to predict protein structural classes [DOI]
  7. 7 Prediction of Protein Secondary Structure Content by Using the Concept of Chous Pseudo Amino Acid Composition and Support Vector Machine [DOI]
  8. 8 Prediction of Protein Structure Classes with Pseudo Amino Acid Composition and Fuzzy Support Vector Machine Network [DOI]
  9. 9 Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties [DOI]
  10. 10 Prediction of Cell Wall Lytic Enzymes Using Chous Amphiphilic Pseudo Amino Acid Composition [DOI]
  11. 11 Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks [DOI]
  12. 12 Application of SVM to predict membrane protein types [DOI]
  13. 13 Predicting membrane protein type by functional domain composition and pseudo-amino acid composition [DOI]
  14. 14 Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition [DOI]
  15. 15 Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition [DOI]
  16. 16 Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature [DOI]
  17. 17 Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition [DOI]
  18. 18 Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation [DOI]
  19. 19 Digital Coding of Amino Acids Based on Hydrophobic Index [DOI]
  20. 20 Using Maximum Entropy Model to Predict Protein Secondary Structure with Single Sequence [DOI]
  21. 21 Molecular biocoding of insulin – amino acid Ser [DOI]
  22. 22 The digital language of amino acids [DOI]
  23. 23 Molecular biocoding of insulin [DOI]
  24. 24 Algorithm and computational complexity of biochemistry [DOI]

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