Prediction of Dynamic RBP-RNA Interactions Using PrismNet.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Understanding RNA-binding protein (RBP) functions in posttranscriptional regulation requires knowledge of their RNA target binding profiles under diverse cellular conditions.
• Current methods for predicting RBP binding sites in vivo are limited, as sequence or predicted structure alone does not fully represent the in vivo RNA state.
• Accurate prediction of RBP-RNA interactions is crucial for elucidating gene regulation.

Purpose of the Study:

• To present a detailed protocol for training and applying PrismNet, a deep learning model for predicting RBP-RNA interactions.
• To enable accurate prediction of RBP binding under varying cellular conditions using both RNA sequence and in vivo structural information.
• To facilitate functional studies of RBPs by providing a robust prediction tool.

Main Methods:

• Utilizing deep learning with PrismNet, incorporating both RNA sequence and in vivo RNA structure data obtained from probing experiments.
• Training a PrismNet model specific to a particular RNA-binding protein (RBP).
• Applying the trained PrismNet model to predict RBP binding sites under different cellular conditions.

Main Results:

• PrismNet accurately predicts RBP binding profiles under different cellular conditions by integrating sequence and in vivo structure data.
• The model demonstrates the capability to reflect the dynamic state of RNA in vivo, overcoming limitations of existing prediction tools.
• Successful training and application of PrismNet for specific RBP-RNA interaction predictions.

Conclusions:

• PrismNet offers a powerful and accurate method for predicting RBP-RNA interactions in vivo.
• The protocol enables researchers to train and utilize PrismNet for studying RBP functions in posttranscriptional regulation.
• This approach enhances our understanding of how RBPs bind to RNA targets under dynamic cellular environments.