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  • Evaluation of sequence features from intrinsically disordered regions for the estimation of protein function

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    Author(s)
    Sharma, Alok
    Dehzangi, Abdollah
    Lyons, James
    Imoto, Seiya
    Miyano, Satoru
    Nakai, Kenta
    Patil, Ashwini
    Griffith University Author(s)
    Sharma, Alok
    Lyons, James
    Dehzangi, Iman
    Year published
    2014
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    Abstract
    With the exponential increase in the number of sequenced organisms, automated annotation of proteins is becoming increasingly important. Intrinsically disordered regions are known to play a significant role in protein function. Despite their abundance, especially in eukaryotes, they are rarely used to inform function prediction systems. In this study, we extracted seven sequence features in intrinsically disordered regions and developed a scheme to use them to predict Gene Ontology Slim terms associated with proteins. We evaluated the function prediction performance of each feature. Our results indicate that the residue ...
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    With the exponential increase in the number of sequenced organisms, automated annotation of proteins is becoming increasingly important. Intrinsically disordered regions are known to play a significant role in protein function. Despite their abundance, especially in eukaryotes, they are rarely used to inform function prediction systems. In this study, we extracted seven sequence features in intrinsically disordered regions and developed a scheme to use them to predict Gene Ontology Slim terms associated with proteins. We evaluated the function prediction performance of each feature. Our results indicate that the residue composition based features have the highest precision while bigram probabilities, based on sequence profiles of intrinsically disordered regions obtained from PSIBlast, have the highest recall. Amino acid bigrams and features based on secondary structure show an intermediate level of precision and recall. Almost all features showed a high prediction performance for GO Slim terms related to extracellular matrix, nucleus, RNA and DNA binding. However, feature performance varied significantly for different GO Slim terms emphasizing the need for a unique classifier optimized for the prediction of each functional term. These findings provide a first comprehensive and quantitative evaluation of sequence features in intrinsically disordered regions and will help in the development of a more informative protein function predictor.
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    Journal Title
    PloS one
    Volume
    9
    Issue
    2
    DOI
    https://doi.org/10.1371/journal.pone.0089890
    Copyright Statement
    © 2014 Sharma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/67530
    Collection
    • Journal articles

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