Abstract
One of the major challenges in the post-genomic era is to accurately model the interactions taking place in most cellular processes. Detailed characterization of such interactions is critical for understanding the principles of living cell molecular machinery on the system biology level. This book chapter contains a review of the multiscale protein biological function prediction algorithms that are founded on protein sequence analysis, three-dimensional structure comparison, biological function annotation, and finally molecular interactions. We include diverse computational methods used to predict the biological function for a given biomolecule using multiscale features, and more generally to model a meta-learning prediction system to analyze the impact of micro-dynamics on global behavior for selected biological systems, with important roles in chemistry, biology, and medicine.
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References
Watson, J.D.: The human genome project: past, present, and future. Science 248, 44–49 (1990)
Adams, M.D., Kelley, J.M., Gocayne, J.D., Dubnick, M., Polymeropoulos, M.H., Xiao, H., Merril, C.R., Wu, A., Olde, B., Moreno, R.F.: Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252, 1651–1656 (1991)
Moult, J., Fidelis, K., Kryshtafovych, A., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP)—round IX. Proteins 79, 1–5 (2011)
Basu, S., Plewczynski, D.: AMS 3.0: prediction of post-translational modifications. BMC Bioinformatics 11, 210 (2010)
Plewczynski, D., Basu, S., Saha, I.: AMS 4.0: consensus prediction of post-translational modifications in protein sequences. Amino Acids 43(2), 573–582 (2012)
Plewczynski, D.: Mean-field theory of meta-learning. J. Stat. Mech. 11, P11003 (2009)
Plewczynski, D.: Landau theory of meta-learning. In: Security and Intelligent Information Systems, vol. 7053, pp. 142–153. Springer, Heidelberg (2012)
Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 43, 583–594 (2012)
von Grotthuss, M., Plewczynski, D., Ginalski, K., Rychlewski, L., Shakhnovich, E.: PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics. BMC Bioinformatics 7, 53 (2006)
von Grotthuss, M., Plewczynski, D., Vriend, G., Rychlewski, L.: 3D-Fun: predicting enzyme function from structure. Nucleic Acids Res. 36, W303–W307 (2008)
Plewczyński, D., Paś, J., von Grotthuss, M., Rychlewski, L.: 3D-Hit: fast structural comparison of proteins. Appl. Bioinformatics 1, 223 (2002)
Plewczynski, D., Rychlewski, L.: Meta-basic estimates the size of druggable human genome. J. Mol. Model. 15, 695–699 (2009)
Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M., Plewczynski, D.: PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines. J. Mol. Model. 17, 2191–2201 (2011)
Kawashima, S., Kanehisa, M.: AAindex: amino acid index database. Nucleic Acids Res. 28(374) (2000)
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997)
Frishman, D., Argos, P.: Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27, 329–335 (1997)
King, R.D., Sternberg, M.J.E.: Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci. 5, 2298–2310 (1996)
Levin, J.M.: Exploring the limits of nearest neighbour secondary structure prediction. Protein Eng. 10, 771–776 (1997)
Plewczynski, D., Tkacz, A., Wyrwicz, L.S., Rychlewski, L.: AutoMotif server: prediction of single residue post-translational modifications in proteins. Bioinformatics 21, 2525–2527 (2005)
Plewczynski, D., Tkacz, A., Wyrwicz, L.S., Rychlewski, L., Ginalski, K.: AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update. J. Mol. Model. 14, 69–76 (2008)
Plewczynski, D., Rychlewski, L., Ye, Y., Jaroszewski, L., Godzik, A.: Integrated web service for improving alignment quality based on segments comparison. BMC Bioinformatics 5, 98 (2004)
Chatterjee, P., Basu, S., Kundu, M.M., Nasipuri, M., Plewczynski, D.: PPI_SVM: prediction of protein-protein interactions using machine learning, do-main-domain affinities and frequency tables. Cell. Mol. Biol. Lett. 16, 264–278 (2011)
Xenarios, I., Salwinski, L., Duan, X.J., Higney, P., Kim, S.-M., Eisenberg, D.: DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30, 303–305 (2002)
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Plewczynski, D., Basu, S. (2014). A Meta-learning Approach for Protein Function Prediction. In: Saha, P., Maulik, U., Basu, S. (eds) Advanced Computational Approaches to Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41539-5_5
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