Article

Abstract

• BACKGROUND: The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs. RESULTS: Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP. Additional feature engineering highlighted the U-rich binding preference of HuR and AU-rich binding preference for TTP. Domain adaptation along with multi-task learning was used to predict the common binding sites. CONCLUSION: The distinction between HuR and TTP binding appears to be subtle content features. HuR prefers strongly U-rich sequences whereas TTP prefers AU-rich as with increasing A content, the sequences are more likely to be bound only by TTP. Our model is consistent with competitive binding of the two proteins, particularly at intermediate AU-balanced sequences. This suggests that fine changes in the A/U balance within a untranslated region (UTR) can alter the binding and subsequent stability of the message. Both feature engineering and domain adaptation emphasized the extent to which these proteins recognize similar general sequence features. This work suggests that the k-spectrum kernel method could be useful when studying RNA binding proteins and domain adaptation techniques such as feature augmentation could be employed particularly when examining RBPs with similar binding preferences.

Creator

• Dowell, Robin
• Goldberg, Debra S
• Bhandare, Shweta

Date Issued

• 2017-01-01

Academic Affiliation

• Molecular, Cellular & Developmental Biology (MCDB)

Journal Title

• PLoS One

Journal Issue/Number

• 3

Journal Volume

• 12

File Extent

• 0174052-0174052

Subject

• Support Vector Machine
• Catalytic Domain
• Humans
• Tristetraprolin
• Theoretical
• ELAV-Like Protein 1
• Binding Sites
• Models

Last Modified

• 2020-03-09

Identifier

• PubMed ID: 28333956

Resource Type

• Article

Rights Statement

• In Copyright

DOI

•  https://doi.org/10.1371/journal.pone.0174052

ISSN

• 1932-6203

Language

• English [eng]

License

• Creative Commons BY Attribution 4.0 International

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