Structure Based Protein Fold Recognition Using Dynamic Bayesian Networks Today it remains impossible to predict the three-dimensional structure of a protein based on its amino acid sequence alone. The ability to predict protein structure from sequence would have great impact on crucial areas ranging from understanding protein function over protein design to drug development. To simplify the problem, fold recognition methods try to evaluate the possibility that a query sequence adopts any of the already known folds (the template folds). This is already extremely useful, since knowing that a sequence likely corresponds to a certain fold can e.g. lead to the elucidation of the protein's function. This can e.g. be used to perform gene annotation on a genomic scale. We propose to construct a novel structure based protein fold recognition method that uses a probabilistic description of protein structure using Dynamic Bayesian Networks (Dens), a machine learning method for which efficient methods exist to perform inference and parameter learning from data. This will allow us to construct probabilistic models that are in many ways more advanced than currently used techniques. In particular, we will develop Dens that represents local amino acid preference, local backbone structure, residue exposure and no local residue-residue contacts of a 'general' protein structure. These probabilistic models can then be used to evaluate the fit between a given sequence family superimposed on a specific structure, le. As a scoring function in a fold recognition method.
Field of science
- /natural sciences/chemical sciences/organic chemistry/amines
- /natural sciences/biological sciences/biochemistry/biomolecules/proteins
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