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Improving Function Prediction Using Patterns of Native Disorder in Proteins
Anna Lobley
Instrinsically unstructured (disordered) proteins adopt little or no stable secondary
structure in their native state. Proteins containing long disordered regions are
abundant within eukaryotic genomes and can be predicted successfully from amino
sequence. Disordered regions have been shown to be important for functional
specificity and frequently contain binding motifs or are located at sites of covalent
modification often inferring a regulatory role for the protein. Computational methods
that predict protein function from sequence rely upon the use of homology
information to transfer annotations between proteins. These methods are not
applicable to orphan proteins or cases where whole families of protein sequences are
not annotated. To address the requirement for protein function prediction methods that
are independent of sequence homology and explore the use of information describing
protein disorder, we have implemented a machine learning method for predicting
protein function from sequence. A set of features for encoding disorder information
was designed and their importance in predicting Gene Ontology (GO) categories
demonstrated. The addition of disorder features significantly improved prediction of
many GO categories. The method has
been benchmarked against a competing
method and the practical use of the classifiers demonstrated through the annotation of
a set of orphan and unknown human proteins.