Algorithm and computational complexity of Insulin

Transcription

1 Algorithm and computational complexity Insulin Lutvo Kurić Bosnia and Herzegovina, Novi Travnik, Kalinska 7 Abstract:This paper discusses cyberinformation studies the amino acid composition insulin, in particular the identification scientific terminology that could describe this phenomenon, ie, the study genetic information, as well as the relationship between the genetic language proteins and theoretical aspect this system and cybernetics. The result this research show that there is a matrix code for insulin. It also shows that the coding system within the amino acidic language gives detailed information, not only on the amino acid record, but also on its structure, configuration and its various shapes. The issue the existence an insulin code and coding the individual structural elements this protein are discussed. Answers to the following questions are sought. Does the matrix mechanism for biosynthesis this protein function within the law the general theory information systems, and what is the significance this for understanding the genetic language insulin? What is the essence existence and functioning this language? Is the genetic information characterized only by biochemical, or also by cyberinformation principles? The potential effects physical and chemical, as well as cybernetic and information ptinciples, on the biochemical basis insulin are also investigated.this aper discusses new methods for developing genetic technologies, in particular more advanced digital technology based on programming, cybernetics, and informational laws and systems, and how this new technology could be useful in medicine, bioinformatics, genetics, biochemistry, and other natural sciences. Keywords biocoding, atomic progression, human insulin, insulin code, genetics code, amino acids Introduction The biologic role any given protein in essential life processes, eg, insulin, depends on the positioning its component amino acids, and is understood by the positioning letters forming words. Each these words has its biochemical base. If this base is expressed by corresponding discrete numbers, it can be seen that any given base has its own program, along with its own unique cybernetics and information characteristics. Indeed, the sequencing the molecule is determined not only by distin biochemical features, but also by cybernetic and information principles. For this reason, research in this field deals more with the quantitative rather than qualitative characteristcs genetic information and its biochemical basis. For the purposes this paper, specific physical and chemical factors have been selected in order to express the genetic information for insulin.numerical values are them assigned to these factors, enabling them to be measured. In this way it is possible to determine oif a connection really exists between the quantitative ratios in the process transfer genetic information and the qualitative appearance the insulin molecule. To select these factors, preference is given to classical physical and chemical parameters, including the number atoms in the relevant amino acids, their analog values, the position in these amino acids in the peptide chain, and their frenquencies.there is a arge numbers these parameters, and each their gives important genetic information. Going through this process, it becomes clear that there is a mathematical relationship between quantitative ratios and the qualitative appearance the biochemical genetic processes and that there is a measurement method that can be used to describe the biochemistry insulin. 216

2 Methods Insulin can be represented by two different forms, ie, a discrete form and a sequential form. In the discrete form, a molecule insulin is represented by a set discrete codes or a multiple dimension vector. In the sequential form, an insulin molecule is represent by a series amino acids according to the order their position in the chains 1AI0. Therefore, the sequential form can naturally reflect all the information about the sequence order and lenght an insulin molecule. The key issue is whether we can develop a different discrete method representing an insulin molecule that will allow accomodation partial, if not all sequence order information? Because a protein sequence is usually represented by a series amino acids should be assigned to these codes in order to optimally convert the sequence order information into a series numbers for the discrete form representation? Expression Insulin Code Matrix- 1AI0 The matrix mechanism Insulin, the evolution biomacromolecules and, especially, the biochemical evolution Insulin language, have been analyzed by the application cybernetic methods, information theory and system theory, respectively. The primary structure a molecule Insulin is the exact specification its atomic composition and the chemical bonds connecting those atoms. has in total 12 chains: A,B,C,D,E,F,G,H,I,J,K,L. 1AI0:A G I V E Q C C T S I C S L Y Q L E N Y C N AI0:B F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T etc. Figure 1. Group chains A,B,C,D,E,F,G,H,I,J,K,L. Notes: Aforementioned aminoacids are positioned from number 1 to 306. s 1, 2, 3, n... present the position a certain aminoacid. This positioning is the key importance for understanding programmatic, cybernetic and information principles in this protein. The scientific key for interpretation bio chemical processes is the same for insulin and as well as for the other proteins and other sequences in biochemistry. 217

3 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), The first aminoacid in this example has 10 atoms, the second one 22, the third one 19, etc. They have exactly these numbers atoms because there are many codes in the insulin molecule, analog codes, and other voded features. In fact, there is a cybernetic algorithm which it is recorded that the firs amino acid has to have 10 atoms, the second one 22, the third one 19, etc. The first amino acid has its own biochemistry, as does the second and the third, etc. The obvious conclusion is that there is a concrete relationship between quantitative ratios in the process transfer genetic information and qualitative appearance, ie, the characteristcs the organism. ALGORITHM We shall now give some mathematical evidences that will prove that in the biochemistry hemoglob in there really is programmatic and cybernetic algorithm in which it is recorded, in the language mathematics, how the molecule will be built and what will be the quantitative characteristics the given genetic information. Atomic progression Step 1 (Amino acids from 1 to 306) AC1 = 10 atoms; AC2 = 22 atoms; AC3 = 19 atoms;... AC306 = 17 atoms; [AC1 + (AC1+ AC2) + (AC1+ AC2+ AC3)..., + (AC1+ AC2+ AC3..., + AC147)] = S1; AC1 = APa1 = 10; (AC1+ AC2) = (10+22) = APa2 = 32; (AC1+ AC2+ AC3) = ( ) = APa3 = 51; (AC1+ AC2+ AC3..., + AC306) = APa306 = 5640 atoms; APa1,2,3,n = Atomic progression amino acids 1,2,3,n [APa1+APa2+APa3)..., + APa306)] = ( , ) = S1; S1 = ; Example : Atomic progression 1 (APa) G I V E Q C.. P K T Sum G I V E Q C.. P K T Sum (0+10) = 10; (10+22)=32; ( ) = 51; etc. 218

4 Figure 2. Atomic progression 1 (APa) amino acids from 1 to 306. Notes: By using chemical-information procedures, we calculated the arithmetic progression for the information content aforementioned aminoacids. Step 2 (Amino acids from 306 to 1) AC306 = 17 atoms; AC305 = 24 atoms; AC304 = 17 atoms;... AC1 = 10 atoms; [AC306 + (AC306+ AC305) + (AC306+ AC305+ AC304)..., + (AC306+AC305+AC304..., +AC1)] = S2; AC306 = APb306 = 17; (AC306+ AC305) = (17+24) = APb306 = 41; (AC306+ AC305+ AC304) = ( ) = APb304 = 58; (AC306+ AC305+ AC304..., + AC1) = APb1 = 5640 atoms; APb306,305,304,,1 = Atomic progression amino acids 306,305,304, 1; [APb306+APb305+APb1304)..., + APb1)] = ( , ) = ; S2 = ; Example: Atomic progression 2 (APb) G I V.. I Y T P K T Sum Sum G I V.. I Y T P K T (0+17) = 17; (17+24)=41; ( )=58; etc. Figure 3. Schematic representation the atomic progression 2 from 306 to 1. Within the digital pictures in biochemistry, the physical and chemical parameters are in a strict compliance with programmatic, cybernetic and information principles. Each bar in the protein chain attracts only the corresponding aminoacid, and only the relevant aminoacid can be positioned at certain place in the chain. Each peptide chain can have the exact number aminoacids necessary to meet the strictly determined mathematical conditioning. It can have as many atoms as necessary to meet the mathematical balance the biochemical phenomenon at certain mathematical level, etc. The digital language biochemistry has a countless number codes and analogue codes, as well as other information content. These pictures enable us to realize the very essence functioning biochemical processes. There are some examples. 219

5 Table 1. Bio intervals APa and APb (sequence from 1 to 306 AA) AA S N C L E G T S N C L E G T atoms Rank APa > APb > AA G L Y N V A E G L Y N V A E atoms Rank ( ) = -1681; ( ) = -1358; ( ) ) =-1196; etc. Table 1. Schematic representation the bio intervals APa and APb (sequence from 1 to 306 AA). Notes: Namely, having mathematically analyzed the Bio intervals model (Table 1) we have found out that the protein code is based on a periodic law. This being the only to read the picture, the solution the main problem (concering an arrangement where each amino acid takes only one, precisely determined position in the code), is quite manifest: Bio intervals model insulin should, in fact, be remodelled into a periodic system. Examples: Table 2 AA S N C L E G T S N C L E G T atoms Rank APa >

6 APb > AA G L Y N V A E G L Y N V A E atoms Rank Table 2. Schematic representation the bio intervals APa and APb (sequence from 52 to 255 AA). Table 3 AA S N C L E G T S N C L E G T atoms Rank APa > APb > AA G L Y N V A E G L Y N V A E atoms Rank Table 3. Schematic representation the bio intervals APa and APb (sequence from 1 to 306 AA). The research we carried out have shown that atomic progression are one quantitative characteristics in biochemistry. Atomic progression is, actually, a discrete code that protects and guards genetic information coded in bio-chemical processes. This a recently discovered code, and more detailed knowledge on it is yet to be discovered. In a similar way we shall calculate bio codes other unions amino acids. Once we do this, we will find out that all these unions amino acids are connected by various bio codes, analogue codes as well as other quantitative features. Examples: 221

7 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), Table 4. AA S N C L E G T atoms Rank APa > APb > 3760 AA G L Y N V A E atoms Rank Table 4. Schematic representation the bio intervals APa and APb (sequence from 12 to 297. AA). ( ) = -741; ( ) = -418; ( ) = -256; Table 5. AA S N C L E G T atoms Rank APa >

8 APb > AA G L Y N V A E atoms Rank Table 5. Schematic representation the bio intervals APa and APb (sequence from 63 to 246. AA). Table 6. AA S N C L E G T atoms Rank APa > APb > AA G L Y N V A E atoms Rank Table 6. Schematic representation the bio intervals APa and APb (sequence from 114 to 195. AA). Table 7. AA S N C L E G T atoms Rank APa >

9 APb > AA G L Y N V A E atoms Rank Table 7. Schematic representation the bio intervals APa and APb (sequence from 103 to 165. AA). Table 8. AA S N C L E G T atoms Rank APa > APb > AA G L Y N V A E atoms Rank Table 8. Schematic representation the bio intervals APa and APb (sequence from 52 to 255. AA). Table 9. AA S N C L E G T atoms

10 Rank APa > APb > AA G L Y N V A E atoms Rank Table 9. Schematic representation the bio intervals APa and APb (sequence from 1 to 306. AA). Those tables (4-9) contains an overview all positive and negative values bio codes. The values show some the quantitative characteristics the molecule insulin. Actually, they show that there is an exact mathematical balance between positive and negative values. Therefore, there is a mathematical balance between the union aminoacids with positive progression and those negative progression. Aminoacids with a positive progression have a primary role in the mathematical picture that protein, and the negative progression have a secondary role in it. We assume that aminoacids with a positive progression have a primary role in the biochemical picture that protein, and the negative progression have a secondary role in it. If this really is the case and research on an experimental level proves it, a radically new way learning about biochemical processes will be opened. The molecule insulin we can understand as words built from letters, i.e. aminoacids. The meaning words is determined by positioning letters. Each these words has its biochemical base. If this base is expressed by corresponding discrete numbers, we find out that the base has its own program, cybernetic and information characteristics. In fact, we will find out that the sequencing the molecule is conditioned and determined not only by biochemical, but also by cybernetic and information principles. For this reason, in this research we will deal more with quantitative, and less with qualitative characteristics the genetic information and its biochemical foundation. Here are some examples Table 10. AA G G G G G G atoms Rank

11 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), APb APa AA S S S S S S atoms Rank Table 10. Schematic representation the bio intervals APa and APb (G and S. AA). ( )=741; ( ) = 741: ( ) = 741; etc. Table 11. AA L L L L L L atoms Rank APa APb AA N N N N N N atoms Rank Table 11. Schematic representation the bio intervals APa and APb (L and N. AA).. 226

12 Table 12. AA L L L L L L atoms Rank APa APb AA N N N N N N atoms Rank Table 12. Schematic representation the bio intervals APa and APb (L and N. AA). Table 13. AA G G G G G G atoms Rank APa APb AA A A A A A A 227

13 atoms Rank Table 13. Schematic representation the bio intervals APa and APb (G and A. AA). ( ) = 5764; ( ) = 5764; ( ) = 5764; etc. Table 14. AA C C C C C C atoms Rank APa APb AA Y Y Y Y Y Y atoms Rank Table 14. Schematic representation the bio intervals APa and APb (C and Y. AA). ( ) = 5678; ( ) = 5678; ( ) = 5678; etc. Table 15. AA E E E E E E atoms

14 Rank APa APb AA V V V V V V atoms Rank Table 15. Schematic representation the bio intervals APa and APb (E and V. AA). ( ) = 5678; ( ) = 5678; ( ) = 5678; ets. Table 16. AA T T T T T T atoms Rank APa APb AA E E E E E E atoms Rank Table 16. Schematic representation the bio intervals APa and APb (T and E. AA). ( ) = 5839; ( ) = 5839; ( ) = 5839; ets. 229

15 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), Table 17. AA E E E E E E atoms Rank APa APb AA Y Y Y Y Y Y atoms Rank Table 17. Schematic representation the bio intervals APa and APb (E and Y. AA). Table 18. AA C C C C C C atoms Rank APa APb AA V V V V V V 230

16 atoms Rank Table 18. Schematic representation the bio intervals APa and APb (C and V. AA). Table 19. AA T T T T T T atoms Rank APa APb AA E E E E E E atoms Rank ( ) = 741; ( ) = 741; ( ) = 741; etc. Table 19. Schematic representation the bio intervals APa and APb (T and E. AA). Atomic progression presented in Tables 1-19 are calculated using the relationship between corresponding groups amino acids. These are groups with different numbers amino acids. There are different ways and methods selecting these groups amino acids, which method is most efficient some We hope that science will determine which method is most efficient for this selection. Biological particularity proteins depends on the order amino acids in their molecules. Change that order will lead to the change their biological particularity. The base parameters that determine the change status biosynthesis matrix macromolecules are the system entropy, volume information transferred and the level probability that the genetic information will be transferred as a whole. 231

17 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), System is any group objects (elements), their relations (number atoms, atomic number, atomic weight, co-valent radius, molar rotation, electrical negativity, etc...), as well as relations among its attributes. Such is the example insulin, but also all other proteins. An identical scenario we have in the example all natural sequences. Therefore, if G (10 atoms) is replaced by an amino acid with the same polarity characteristics, but different number atoms, the biological particularity insulin will change, reflected in genetic changes and mutagenesis. Table 20. AA G G G G G G atoms Rank Rank AA S S S S S S atoms (256+12) = 268; (205+63) = 268; ( ) = 268; etc. Table 20. Schematic representation the ranks APa and APb (G and S. AA). Table 21. AA L L L L L L atoms Rank

18 Lutvo Kurić, Int. J. Comp. Tech. Appl., Vol 2 (2), Rank AA N N N N N N atoms (273+13) = 286; (222+64) = 286; ( ) = 286; etc. Table 21. Schematic representation the ranks APa and APb (L and N. AA). Table 22. AA L L L L L L atoms Rank Rank AA N N N N N N atoms (32+276) = 308; (83+225) = 308; ( ) = 308; etc. Table 22. Schematic representation the ranks APa and APb (L and N. AA). Table 23. AA G G G G G G atoms Rank

19 Rank AA A A A A A A atoms (296+35) = 331; (245+86) = 331; ( ) = 331; etc. Table 23. Schematic representation the ranks APa and APb (G and A. AA). Table 24. AA C C C C C C atoms Rank Rank AA Y Y Y Y Y Y atoms (20+274) = 294; (71+223) = 294; ( ) = 294; etc. Table 24. Schematic representation the ranks APa and APb (G and A. AA). Table 25. AA E E E E E E 234

20 atoms Rank Rank AA V V V V V V atoms (34+288) = 322; (85+237) = 322; ( ) = 322; etc. Table 25. Schematic representation the ranks APa and APb (E and V. AA). Table 26. AA T T T T T T atoms Rank Rank AA E E E E E E atoms (51+297) = 348; ( ) = 348; ( ) = 348; etc. Table 26. Schematic representation the ranks APa and APb (T and E. AA). As it cam be observed, quantitative characteristics the biochemistry insulin apply to the position number. It can be concluded that there is a connection between quantitative 235

21 characteristics in the process transfer genetic information and the qualitative appearance given genetic processes. We would particularly like to stress here that the genetic, as well as biochemical information in a broader sense the word, is determined and characterized by very complex cybernetic and information principles. The constantans in those principles are: the number atoms and molecules, atomic numbers, atomic weight, physical and chemical parameters, even and odd values, codes and analogue codes, standard deviations, frequencies, primary and secondary values, and many other things. There are some examples. Amino acids with atomic progression, chains: A,B,C,D,E,F,G,H,I,J,K,L. Table 27. G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T Table 27. Schematic representation the amino acids with atomic progression. 236

22 Amino acids without atomic progression Table 28. G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T G I V E Q C C T S I C S L Y Q L E N Y C N F V N Q H I C G S H L V E A L Y L V C G E R G F I Y T P K T Table 28. Schematic representation the amino acids without atomic progression, chains: A,B,C,D,E,F,G,H,I,J,K,L. Prima sequences Table

23 Table 29. Schematic representation the amino acids - prima sequences Prima numbers 19, 19, 17,, 17 = Prima sequences V,E,T, T In the previous examples we translated the physical and chemical parameters from the language biochemistry into the digital language programmatic, cybernetic and information principles. This we did by using the adequate mathematical algorithms. By using chemical-information procedures, we calculated the numerical value for the information content molecules. What we got this way is the digital picture the phenomenon biochemistry. These digital pictures reveal to us a whole new dimension this science. They reveal to us that the biochemical process is strictly conditioned and determined by programmatic, cybernetic and information principles. From the previous examples we can see that this protein really has its quantitative characteristics. It can be concluded that there is a connection between quantitative characteristics in the process transfer genetic information and the qualitative appearance given genetic processes. DISCUSSION The results our research show that the processes sequencing the molecules are conditioned and arranged not only with chemical and biochemical lawfulness, but also with program, cybernetic and informational lawfulness too. At the first stage our research we replaced nucleotides from the Amino Acid Code Matrix with numbers the atoms and atomic numbers in those nucleotides. Translation the biochemical language these amino acids into a digital language may be very useful for developing new methods predicting protein sub-cellular localization, membrane protein type, protein structure secondary prediction or any other protein attributes. Since the concept Chou's pseudo amino acid composition was proposed [1-2], there have been many efforts to try to use various digital numbers to represent the 20 native amino acids in order to better reflect the sequence-order effects through the vehicle pseudo amino acid composition. Some investigators used complexity measure factor [3], some used the values derived from the cellular automata [4-7], some used hydrophobic and/or hydrophilic values [8-16], some were through Fourier transform [17-18], and some used the physicochemical distance [19]. The author [34-44] is devoted to provide a digital code for each 20 native amino acids. These digital codes should more complete and better reflect the essence each the 20 amino acids. Therefore, it might stimulate a series future work by using the author s digital codes to formulate the pseudo amino acid composition for predicting protein structure class [20-22], subcellular location [23,24], membrane protein type [9,25], enzyme family class [26,27], GPCR type [28, 29], protease type [30], protein-protein interaction [31], metabolic pathways [32], protein quaternary structure [33], and other protein attributes. It is going to be possible to use a completely new strategy research in genetics in the future. However, close observation 238

24 all these relationships, which are the outcomes periodic laws (more specifically the law binary coding), stereo-chemical and digital structure proteins. Conclusions The process sequencing in bio-macromolecules is conditioned and determined not only through biochemical, but also through cybernetic and information principles. The digital pictures biochemistry provide us with cybernetic and information interpretation the scientific facts. Now we have the exact scientific pros that there is a genetic language that can be described by the theory systems and cybernetics, and which functions in accordance with certain principles. DISCLOSURE The author reports no conflict interest in this research. REFERENCES [1] K.C. Chou, Gene Cloning & Expression Technologies, Chapter 4 (Weinrer, P.W., and Lu, Q., Eds.), Eaton Publishing, Westborough, MA (2002), pp [2] K.C. Chou, Prediction protein cellular attributes using pseudo amino acid composition PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44,60) 43 (2001) [3] X. Xiao, S. Shao, Y. Ding, Z. Huang, Y. Huang, K. C. Chou, Using complexity measure factor to predict protein subcellular location, Amino Acids 28 (2005) [4] X. Xiao, S. Shao, Y. Ding, Z. Huang, X. Chen, K. C. Chou, Using cellular automata to generate Image representation for biological sequences, Amino Acids 28 (2005) [5] X. Xiao, S. Shao, Y. Ding, Z. Huang, X. Chen, K. C. Chou, An Application Gene Comparative Image for Predicting the Effect on Replication Ratio by HBV Virus Gene Missense Mutation, Journal Theoretical Biology 235 (2005) [6] X. Xiao, S. H. Shao, Z. D. Huang, K. C. Chou, Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor, Journal Computational Chemistry 27 (2006) [7] X. Xiao, S. H. Shao, Y. S. Ding, Z. D. Huang, K. C. Chou, Using cellular automata images and pseudo amino acid composition to predict protein sub-cellular location, Amino Acids 30 (2006) [8] K. C. Chou, Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics 21 (2005) [9] K. C. Chou, Y. D. Cai, Prediction membrane protein types by incorporating amphipathic effects, Journal Chemical Information and Modeling 45 (2005) [10] Z. P. Feng, Prediction the subcellular location prokaryotic proteins based on a new representation the amino acid composition, Biopolymers 58 (2001) [11] Z. P. Feng, An overview on predicting the subcellular location a protein, In Silico Biol 2 (2002) [12] M. Wang, J. Yang, Z. J. Xu, K. C. Chou, SLLE for predicting membrane protein types, Journal Theoretical Biology 232 (2005) [13] S. Q. Wang, J. Yang, K. C. Chou, Using stacked generalization to predict membrane protein types based on pseudo amino acid composition, Journal Theoretical Biology, 239

25 in press (2006) doi: /j.jtbi [14] M. Wang, J. Yang, G. P. Liu, Z. J. Xu, K. C. Chou, Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition, Protein Engineering, Design, and Selection 17 (2004) [15] S. W. Zhang, Q. Pan, H. C. Zhang, Z. C. Shao, J. Y. Shi, Prediction protein homooligomer types by pseudo amino acid composition: Approached with an improved feature extraction and naive Bayes feature fusion, Amino Acids 30 (2006) [16] Y. Gao, S. H. Shao, X. Xiao, Y. S. Ding, Y. S. Huang, Z. D. Huang, K. C. Chou, Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter, Amino Acids 28 (2005) [17] Y. Z. Guo, M. Li, M. Lu, Z. Wen, K. Wang, G. Li, J. Wu, Classifying G proteincoupled receptors and nuclear receptors based on protein power spectrum from fast Fourier transform, Amino Acids 30 (2006) [18] H. Liu, M. Wang, K. C. Chou, Low-frequency Fourier spectrum for predicting membrane protein types, Biochem Biophys Res Commun 336 (2005) [19] K. C. Chou, Prediction protein subcellular locations by incorporating quasisequence-order effect, Biochemical & Biophysical Research Communications 278 (2000) [20] K. C. Chou, A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space, Proteins: Structure, Function & Genetics 21 (1995) [21] K. C. Chou, C. T. Zhang, Predicting protein folding types by distance functions that make allowances for amino acid interactions, Journal Biological Chemistry 269 (1994) [22] K. C. Chou, C. T. Zhang, Review: Prediction protein structural classes, Critical Reviews in Biochemistry and Molecular Biology 30 (1995) [23] K. C. Chou, D. W. Elrod, Protein subcellular location prediction, Protein Engineering 12 (1999) [24] K. C. Chou, Review: Prediction protein structural classes and subcellular locations, Current Protein and Peptide Science 1 (2000) [25] K. C. Chou, D. W. Elrod, Prediction membrane protein types and subcellular locations, PROTEINS: Structure, Function, and Genetics 34 (1999) [26] K. C. Chou, D. W. Elrod, Prediction enzyme family classes, Journal Proteome Research 2 (2003) [27] K. C. Chou, Y. D. Cai, Predicting enzyme family class in a hybridization space, Protein Science 13 (2004) [28] K. C. Chou, D. W. Elrod, Bioinformatical analysis G-protein-coupled receptors, Journal Proteome Research 1 (2002) [29] K. C. Chou, Prediction G-protein-coupled receptor classes, Journal Proteome Research 4 (2005) [30] K. C. Chou, Y. D. Cai, Prediction protease types in a hybridization space, Biochem. Biophys. Res. Comm. 339 (2006) [31] K. C. Chou, Y. D. Cai, Predicting protein-protein interactions from sequences in a hybridization space, Journal Proteome Research 5 (2006) [32] K. C. Chou, Y. D. Cai, W. Z. Zhong, Predicting networking couples for metabolic pathways Arabidopsis, EXCLI Journal 5 (2006) [33] K. C. Chou, Y. D. Cai, Predicting protein quaternary structure by pseudo amino acid composition, PROTEINS: Structure, Function, and Genetics 53 (2003) [34] L.Kurić, The digital language amino acids. Amino Acids (2007)

26 [35] L.Kurić, The Atomic Genetic Code. J. Comput Sci Biol 2 (2009) [36] L.Kurić, Mesure complexe des caracteristiques dynamiques de series temporelles Journal de la Societe de statistique de Paris - tome 127, No [37] L.Kurić, The Insulin Bio Code - Zero Frenquencies, GJMR Vol. 10 Issue 1: 15 May [38] L.Kurić, Molecular biocoding insulin, Advances and Applications in Bioinformatics and Chemistry, Jul p [39] L.Kurić, The Insulin Bio Code Prima sequences, GJMR Vol. 1 Issue 1: 15 June [40] L.Kurić, ATOMIC HEMOGLOBIN CODE, GJMR Volume 10 Issue 2, October [41] L.Kurić, Language Insulin Decoded:Discret code 1128, IJPBS JOURNAL, October [42] L.Kurić, Measures Bio Insulin Frequencies, IJCSET (Volume 1. Issue 4. December, 2010) [43] Kurić L. The Insulin Bio Code - Standard Deviation, International Journal Scientific and Engineering Research (IJSER) Nov 25, 2010 under ISSN [44] Kurić L. Molecular biocoding insulin amino acid Gly, International Journal Scientific and Engineering Research (IJSER) - Feb 2011 issue. 241