The utility of different representations of protein sequence for predicting functional class

Ross D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Motivation: Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model. Results: Using the different representations DMP learnt prediction rules that were more accurate than default at every level of function using every type of representation. The most effective way to represent sequence was using phylogeny (75% accuracy and 13% coverage of unassigned ORFs at the most general level of function: 69% accuracy and 7% coverage at the most detailed). We tested different methods for combining predictions from the different types of representation. These improved both the accuracy and coverage of predictions, e.g. 40% of all unassigned ORFs could be predicted at an estimated accuracy of 60% and 5% of unassigned ORFs could be predicted at an estimated accuracy of 86%.
    Original languageEnglish
    Pages (from-to)445-454
    Number of pages9
    JournalBioinformatics
    Volume17
    Issue number5
    DOIs
    Publication statusPublished - May 2001

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