Abstract
This paper reports the use of lacunarity analysis of protein sequences as a new method to analyze the distribution of amino acids in a protein sequence. One of the key results is that distribution of hydrophobic amino acids in a protein sequence exhibit fractal like behavior. It is found that lacunarity plots of distribution of hydrophobic amino acids follow similar patterns for a given protein sequence as well as for amino acid sequences that are extracted from the given protein sequence as prefixes with length reduced by half from the original sequence length. Another interesting result is that using the lacunarity values of chaos game representations of amino acid sequences, we can prove the non-random nature of protein sequences. Lacunarity values also help us to classify a set of true and random protein sequences. These two findings affirm lacunarity analysis as a novel and promising bio-sequence analysis method.
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References
Mandelbrot, B.: The Fractal Geometry of Nature. Freeman, New York (1983)
Li-Qian, Z., Zu-Guo, Y., Ji-Qing, D., Vo, A., Sgun-Chao, L.: A Fractal Method to Distinguish Coding and Non-coding Sequences in a Complete Genome Based on a Number Sequence Representation. J. Theor. Biol. 232, 559–567 (2005)
Peng, C.K., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Sciortino, F., Simons, M., Stanley, H.E.: Long-Range Correlations in Nucleotide Sequences. Nature 356, 168–170 (1992)
Garte, S.: Fractal Properties of Human Genome. J. Theor. Biol. 230, 251–260 (2004)
Zn-Guo, Y., Anh, V., Zhi-Min, G., Shun-Chao, L.: Fractals in DNA Sequence Analysis. Chinese Phys. 11, 1313 (2002)
Plotnick, R.E., Gardner, R.H., Hargrove, W.W., Prestegaard, K., Perlmutter, M.: Lacunarity Analysis: A General Technique for the Analysis of Spatial Patterns. Phys. Rev. E. 53, 5461–5468 (1996)
Allain, C., Cloitre, M.: Characterizing the Lacunarity of Random and Deterministic Fractal Sets. Phys. Rev. A. 44, 3552–3558 (1991)
Plotnick, R.E., Gardner, R.H., O’Neill, R.V.: Lacunarity Indices as Measures of Landscape Texture. Landscape Eco. 8, 201–211 (1993)
McIntyre, N.E., Wiens, J.A.: A Novel Use of the Lacunarity Index to Discern Landscape Function. Landscape Eco. 15, 313–321 (2000)
Gopakumar, G., Achuthsankar, N.S.: Fractality of Numeric and Symbolic Sequences. IEEE Potentials 29, 36–39 (2010)
Roya, A., Perfecta, E., Dunnea, W.M., Odlingb, N., Kim, J.: Lacunarity Analysis of Fracture Networks: Evidence for Scale-Dependent Clustering. J. Struct. Geol. 32, 1444–1449 (2010)
Gilmore, S., Hofmann-Wellenhof, R., Muir, J., Soyer, H.P.: Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma. PLoS One 4, e7449 (2009)
Katti, M.V., Sami-Subbu, R., Ranjekar, P.K., Gupta, V.S.: Amino Acid Repeat Patterns in Protein Sequences: Their Diversity and Structural-Functional Implications. Prot. Science. 9, 1203–1209 (2000)
Roy, S., Martinez, D., Platero, H., Lane, T., Werner-Washburne, M.: Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions. PLoS ONEÂ 4, e7813 (2009)
Jeffrey, H.J.: Chaos Game Representation of Gene Structure. Nucleic Acids Res. 18, 2163–2170 (1990)
Gilmore, S., Hofmann-Wellenhof, R., Muir, J., Soyer, H.P.: Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma. PLoS One 4, e7449 (2009)
Benson, D.A., Boguski, M.S., Lipman, D.J., Ostell, J., Francis Ouellette, B.F.: Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma. PLoS One 4, e7449 (2009)
The Universal Protein Resource, http://www.uniprot.org/
Nair, S.A., Nair, V.V.: K, S.A., Kant, K., Dey, A.: Bio-sequence Signatures using Chaos Game Representation. In: Bioinformatics: Applications in Life and Environmental Sciences, Capital Publishing Company, New Delhi (2008)
Otaki, J.M., Tsutsumi, M., Gotoh, T., Yamamoto, H.: Secondary Structure Characterization Based on Amino Acid Composition and Availability in Proteins. J. Chem. Inf. Model. 50, 690–700 (2010)
ExPASy Proteomics Server, http://expasy.org/tools/randseq.html
Ding, Y.S., Zhang, T.L., Chou, K.C.: Prediction of Protein Structure Classes with Pseudo Amino Acid Composition and Fuzzy Support Vector Machine Network. Protein Pept. Lett. 14, 811–815 (2007)
Lin, H., Wang, H., Ding, H., Chen, Y., Li, Q.: Prediction of Subcellular Localization of Apoptosis Protein Using Chous Pseudo Amino Acid Composition. Acta Biotheoritica 57, 321–330 (2009)
Wang, W., Geng, X.B., Dou, Y., Liu, T., Zheng, X.: Predicting Protein Subcellular Localization by Pseudo Amino Acid Composition with a Segment-Weighted and Features-Combined Approach. Protein Pept. Lett. (to be appeared, 2011)
Argos, P., Palau, J.: Amino Acid Distribution in Protein Secondary Structures. Int. Jour. Peptide and Prot. Research. 19, 380–393 (1982)
Yu, Z.G., Anh, V., Lau, K.S.: Chaos Game Representation of Protein Sequences Vased on the Detailed HP Model and Their Multifractal and Correlation Analyses. J. Theor. Biol. 226, 341–348 (2004)
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Gopakumar, G., Nair, A.S. (2011). Lacunarity Analysis of Protein Sequences Reveal Fractal Like Behavior of Amino Acid Distributions. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22709-7_33
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DOI: https://doi.org/10.1007/978-3-642-22709-7_33
Publisher Name: Springer, Berlin, Heidelberg
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