Abstract
The primary structure of a protein is the sequence of its amino acids. The secondary structure describes structural properties of the molecule such as which parts of it form sheets, helices or coils. Spacial and other properties are described by the higher order structures. The classification task we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance.
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References
BRUNNERT, M., FISCHER, P. and URFER, W. (2002): Sequence-structure alignment using a statistical analysis of core models and dynamic programming. Technical report, SFB 475, Universität Dortmund.
GARNIER, J., GIBRAT, J.-F. and ROBSON, B. (1996): GOR method for predicting protein secondary structure from amino acid sequence. Methods in Enzymology, 266, 540–553.
KLOCZKOWSKI, A., TING, K.L., JERNIGAN, R.L. and GARNIER, J (2002): Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from aminoacid sequence. Proteins, 49, 154–166.
LARSEN, S. and THOMSEN, C. (2004): Classification of protein sequences using Markov models, Masters thesis, Informatics and Mathematical Modelling. Technical University of Denmark.
ROST, B. and SANDER, C. (1993): Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol., 232, 584–599.
ROST, B. and SANDER, C. (1994): Combining evolutionary information and neural networks to predict protein secondary structure. Proteins, 19, 55–72.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Fischer, P., Larsen, S., Thomsen, C. (2005). Predicting Protein Secondary Structure with Markov Models. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_3
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DOI: https://doi.org/10.1007/3-540-28084-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
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