Predicting Protein Secondary Structure with Markov Models

Paul Fischer, Simon Larsen, Claus Thomsen

AbstractThe 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.
TypeConference paper [With referee]
ConferenceProceedings of 29th Annual Conference of the German Classification Society (GfKl 2005)
Year2004    Month March
BibTeX data [bibtex]
IMM Group(s)Computer Science & Engineering


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