Abstract
Recent studies have found that many proteins contain regions that do not form well defined three-dimensional structures in their native states. The study and detection of such disordered regions is very important both for facilitating structural analysis and to aid understanding of protein function. A newly developed pattern recognition algorithm termed a “Bio-basis Function Neural Network” has been applied to the detection of disordered regions in proteins. Different models were trained studying the effect of changing the size of the window used for residue classification. Ten-fold cross validation showed that the estimated prediction accuracy was 95.2% for a window size of 21 residues and an overlap threshold of 30%. Blind tests using the trained models on a data set unrelated to the training set gave a regional prediction accuracy of 81.4% (± 0.9%).
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References
Dunker, A.K., et al.: Intrinsic protein disorder in complete genomes. In: Genome Inform Ser Workshop Genome Inform, vol. 11, pp. 161–171 (2000)
Alber, T., et al.: The role of mobility in the substrate binding and catalytic machinery of enzymes. In: Ciba Found Symp., vol. 93, pp. 4–24 (1983)
Weinreb, P.H., et al.: NACP, a protein implicated in Alzheimer’s disease and learning, is natively unfolded. Biochemistry 35(43), 13709–13715 (1996)
Garner, E., et al.: Predicting Disordered Regions from Amino Acid Sequence: Common Themes Despite Differing Structural Characterization. Genome Inform Ser Workshop Genome Inform 9, 201–213 (1998)
Kissinger, C.R., et al.: Crystal structures of human calcineurin and the human FKBP12- FK506-calcineurin complex. Nature 378(6557), 641–644 (1995)
Romero, P., et al.: Identifying disordered regions in proteins from amino acid sequence. In: Proc. IEEE Int. Conf. On Neural Networks, TX, Huston (1997)
Xie, Q., et al.: The Sequence Attribute Method for Determining Relationships Between Sequence and Protein Disorder. Genome Inform Ser Workshop Genome Inform 9, 193–200 (1998)
Garner, E., et al.: Predicting Binding Regions within Disordered Proteins. In: Genome Inform Ser Workshop Genome Inform, vol. 10, pp. 41–50 (1999)
Li, X., et al.: Predicting Protein Disorder for N-, C-, and Internal Regions. In: Genome Inform Ser Workshop Genome Inform, vol. 10, pp. 30–40 (1999)
Li, X., et al.: Comparing predictors of disordered protein. In: Genome Inform Ser Workshop Genome Inform, vol. 11, pp. 172–184 (2000)
Romero, P., Obradovic, Z., Dunker, A.K.: Intelligent data analysis for protein disorder prediction. Artificial Intelligence Reviews 14, 447–484 (2000)
Radivojac, P., et al.: Protein flexibility and intrinsic disorder. Protein Sci. In Press (2004)
Thomson, R., et al.: Characterizing proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics 19(14), 1741–1747 (2003)
Yang, Z.R., Thomson, R.: novel neural network method in mining molecular sequence data. IEEE Trans. on Neural Networks (2005) (in Press)
Uversky, V.N., Gillespie, J.R., Fink, A.L.: Why are "natively unfolded" proteins unstructured under physiologic conditions? Proteins 41(3), 415-27 (2000)
Dunker, A.K., et al.: Intrinsic disorder and protein function. Biochemistry 41(21), 6573–6582 (2002)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis. Wiley, New York (2002)
Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta. 405(2), 442–451 (1975)
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Thomson, R., Esnouf, R. (2004). Prediction of Natively Disordered Regions in Proteins Using a Bio-basis Function Neural Network. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_16
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DOI: https://doi.org/10.1007/978-3-540-28651-6_16
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