Natively unstructured regions are a common feature of eukaryotic proteomes. Between 30% and 60% of proteins are predicted to contain long stretches of disordered residues, and not only have many of these regions been confirmed experimentally, but they have also been found to be essential for protein function. In this study, we directly address the potential contribution of protein disorder in predicting protein function using standard Gene Ontology (GO) categories. Initially we analyse the occurrence of protein disorder in the human proteome and report ontology categories that are enriched in disordered proteins. Pattern analysis of the distributions of disordered regions in human sequences demonstrated that the functions of intrinsically disordered proteins are both length- and position-dependent. These dependencies were then encoded in feature vectors to quantify the contribution of disorder in human protein function prediction using Support Vector Machine classifiers. The prediction accuracies of 26 GO categories relating to signalling and molecular recognition are improved using the disorder features. The most significant improvements were observed for kinase, phosphorylation, growth factor, and helicase categories. Furthermore, we provide predicted GO term assignments using these classifiers for a set of unannotated and orphan human proteins. In this study, the importance of capturing protein disorder information and its value in function prediction is demonstrated. The GO category classifiers generated can be used to provide more reliable predictions and further insights into the behaviour of orphan and unannotated proteins.

Abstract Summary: GENIUS II is an automated database system in which open reading frames (ORFs) in complete genomes are assigned to known protein three-dimensional (3D) structures. The system uses the multiple intermediate sequence search method in which query and target sequences are linked by intermediate sequences gathered by PSI-BLAST search. By applying the system to 129 complete genomes, 43.8% on average of the ORFs in the genomes were assigned to known 3D structures and the results are available for free at GENIUS II web site. Availability: http://genius.cbrc.jp Supplementary information: http://genius.cbrc.jp/summary_linking.html

MOTIVATION: In cDNA sequencing projects, it is vital to know whether the protein coding region of a sequence is complete, or whether errors have occurred during library construction. Here we present a linear discriminant approach that predicts this completeness by estimating the probability of each ATG being the initiation codon. RESULTS: Because of the current shortage of full-length cDNA data on which to base this work, tests were performed on a non-redundant set of 660 initiation codon-containing DNA sequences that had been conceptually spliced into mRNA/cDNA. We also used an edited set of the same sequences that only contained the region following the initiation codon as a negative control. Using the criterion that only a single prediction is allowed for each sequence, a cut-off was selected at which discrimination of both positive and negative sets was equal. At this cut-off, 67% of each set could be correctly distinguished, with the correct ATG codon also being identified in the positive set. Reliability could be increased further by raising the cut-off or including homologues, the relative merits of which are discussed. AVAILABILITY: The prediction program, called ATGpr, and other data are available at http://www.hri.co.jp/atgpr CONTACT: [email protected]