Abstract
An important step to understand the main functions of a specific family of proteins is the detection of protein features that could reveal how protein chains are constituted. To achieve this aim we treated amino acid sequences of proteins as a formal language, building a Context-Free Grammar annotated using an n-gram Bayesian classifier. This formalism is able to analyze the connection between protein chains and protein functions. In order to design new protein chains with the properties of the considered family we performed a rule clustering of the grammar to build an Annotated Stochastic Context Free Grammar.
Our methodology was applied to a class of Antimicrobial Peptides (AmPs): the Frog antimicrobial peptides family. Through this case study, our approach pointed out some important aspects regarding the relationship between sequences and functional domains of proteins and how protein domain motifs are preserved by natural evolution in to the amino acid sequences. Moreover our results suggest that the synthesis of new proteins with a given domain architecture can be one of the fields where application of Annotated Stochastic Context Free Grammars can be useful.
This research is founded by the BioBITs Project (Converging Technologies 2007, area: Biotechnology-ICT).
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References
Abe, N., Mamitsuka, H.: Predicting protein secondary structure using stochastic tree grammars. Machine Learning 29(2), 275–301 (1997)
Breyer, L.: The DBACL text classifier (2005), http://www.lbreyer.com/preprints/dbacl.ps.gz
Dyrka, W., Nebel, J.: A stochastic context free grammar based framework for analysis of protein sequences. BMC Bioinformatics 10(1), 323 (2009)
Finn, R., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J., Gavin, O., Gunasekaran, P., Ceric, G., Forslund, K., et al.: The Pfam protein families database. Nucleic Acids Research (2009)
Geman, S., Johnson, M.: Probabilistic grammars and their applications. In: International Encyclopedia of the Social & Behavioral Sciences, pp. 12075–12082 (2002)
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classification. Journal of the American Statistical Association 49, 732–764 (1954)
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences of the United States of America 89(22), 10915 (1992)
Heyer, L., Kruglyak, S., Yooseph, S.: Exploring expression data: identification and analysis of coexpressed genes. Genome Research 9(11), 1106 (1999)
Ienco, D., Pensa, R.G., Meo, R.: Parameter-free hierarchical co-clustering by n-ary splits. In: ECML/PKDD (1), pp. 580–595 (2009)
Kriegel, H.P., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. TKDD 3(1) (2009)
Kwon, S., Carlson, B., Park, J., Lee, B.: Structural organization and expression of the gaegurin 4 gene of Rana rugosa. Biochimica et Biophysica Acta 1492(1) (2000)
Mor, A., Pierre, N.: Isolation and structure of novel defensive peptides from frog skin. European Journal of Biochemistry 219(1-2), 145–154 (2005)
Muggleton, S., Bryant, C., Srinivasan, A., Whittaker, A., Topp, S., Rawlings, C.: Are grammatical representations useful for learning from biological sequence data?-a case study. Journal of Computational Biology 8(5), 493–521 (2001)
Otaki, J., Ienaka, S., Gotoh, T., Yamamoto, H.: Availability of short amino acid sequences in proteins. Protein Science: A Publication of the Protein Society 14(3), 617 (2005)
Peng, F., Schuurmans, D.: Combining naive Bayes and n-gram language models for text classification. In: Peng, F., Schuurmans, D. (eds.) Advances in Information Retrieval, pp. 547–547 (2003)
Peris, P., López, D., Campos, M., Sempere, J.: Protein Motif Prediction by Grammatical Inference. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 175–187. Springer, Heidelberg (2006)
Rigoutsos, I., Floratos, A.: Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm. Bioinformatics-Oxford 14(1), 55–67 (1998)
Rinaldi, A.: Antimicrobial peptides from amphibian skin: an expanding scenario: Commentary. Current Opinion in Chemical Biology 6(6), 799–804 (2002)
Schuster-Böckler, B., Schultz, J., Rahmann, S.: HMM Logos for visualization of protein families. BMC Bioinformatics 5(1), 7 (2004)
Searls, D.B.: The computational linguistics of biological sequences. Artificial Intelligence and Molecular Biology, 47–120 (1993)
Searls, D.B.: The language of genes. Nature 420(6912), 211–217 (2002)
Sigrist, C., Cerutti, L., De Castro, E., Langendijk-Genevaux, P., Bulliard, V., Bairoch, A., Hulo, N.: PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Research 38(Database issue), D161 (2010)
Vignal, E., Chavanieu, A., Roch, P., Chiche, L., Grassy, G., Calas, B., Aumelas, A.: Solution structure of the antimicrobial peptide ranalexin and a study of its interaction with perdeuterated dodecylphosphocholine micelles. European Journal of Biochemistry 253(1), 221–228 (2001)
Waldispühl, J., Steyaert, J.: Modeling and predicting all-α transmembrane proteins including helix-helix pairing. Theoretical Computer Science 335(1), 67–92 (2005)
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 23(3), 337–343 (1977)
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Sciacca, E., Spinella, S., Ienco, D., Giannini, P. (2011). Annotated Stochastic Context Free Grammars for Analysis and Synthesis of Proteins. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_8
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DOI: https://doi.org/10.1007/978-3-642-20389-3_8
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