Discriminating between HuR and TTP binding sites using the k-spectrum kernel method.
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.


CONCLUSIONS
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.
Department of Computer Science, University of Colorado at Boulder, 1111 Engineering Dr, Boulder, CO, 80303 United States of America.
Shweta
http://orcid.org/0000-0003-4893-792X
S
Bhandare
Department of Computer Science, University of Colorado at Boulder, 1111 Engineering Dr, Boulder, CO, 80303 United States of America.|Computational Bioscience Program, University of Colorado, School of Medicine, 12801 E. 17th Ave., RC1N-6129, Aurora, CO, 80045, United States of America.
Debra S
DS
Goldberg
Department of Computer Science, University of Colorado at Boulder, 1111 Engineering Dr, Boulder, CO, 80303 United States of America.|Computational Bioscience Program, University of Colorado, School of Medicine, 12801 E. 17th Ave., RC1N-6129, Aurora, CO, 80045, United States of America.|Department of Molecular, Cellular and Developmental Biology and the BioFrontiers Institute, University of Colorado at Boulder, 596 UCB, Boulder, Boulder, CO, 80309, United States of America.
Robin
R
Dowell
D001665:Binding Sites; D020134:Catalytic Domain; D000067780:ELAV-Like Protein 1 / Q000378:metabolism*; D006801:Humans; D008962:Models, Theoretical; D060388:Support Vector Machine; D051816:Tristetraprolin / Q000378:metabolism*
D000067780:ELAV-Like Protein 1; C098770:ELAVL1 protein, human; D051816:Tristetraprolin; C497552:ZFP36 protein, human