Abstract
BackgroundThe 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.ResultsApplying 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.ConclusionThe 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.
Highlights
RNA binding proteins (RBPs) are crucial regulators of numerous post-transcriptional processes [1, 2]
Our model is consistent with competitive binding of the two proteins, 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
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 when examining RBPs with similar binding preferences
Summary
RNA binding proteins (RBPs) are crucial regulators of numerous post-transcriptional processes [1, 2]. Many RNA binding proteins recognize AU-rich sequence elements, including human antigen R (HuR) and tristetraprolin (TTP). Recent high throughput photoactivatableribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) [3] data is available for these two key regulatory RNA binding proteins This data suggests that both HuR and TTP bind to similar AU-rich elements which are typically 50–150 nucleotides long and generally located within the 30 UTR. 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. This method has yet to be applied to studies of RNA binding protein motifs
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.