Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution.

Area of Science:

• Computational Biology
• Bioinformatics
• Molecular Biology

Background:

• RNA-binding proteins (RBPs) regulate gene expression post-transcriptionally.
• Experimental determination of RBP binding preferences is crucial but data is lacking for many RBPs.
• Existing computational methods require experimental binding data, limiting their application to well-studied RBPs.

Purpose of the Study:

• To develop a novel computational method for predicting RBP sequence preferences.
• To overcome the limitation of requiring experimental binding data for poorly studied RBPs.
• To infer RBP binding preferences using co-evolutionary information.

Main Methods:

• Demonstrated co-evolutionary relationships between RBPs and their RNA targets.
• Developed a K-nearest neighbors (KNN) based algorithm to infer sequence preferences from homologous RBPs.
• Benchmarked the KNN method against existing approaches using in vitro and in vivo datasets.

Main Results:

• The proposed KNN method successfully inferred RBP sequence preferences without experimental data.
• The method outperformed existing alternatives that use only the closest neighbor's preference.
• Performance was comparable to state-of-the-art methods that require experimental binding data.
• Successfully applied the method to infer preferences for novel proteins lacking binding data.

Conclusions:

• This study presents an economical and practical solution for determining RBP sequence preferences, especially for understudied proteins.
• The co-evolution-based method eliminates the need for expensive and time-consuming experimental data.
• The developed source code and datasets are publicly available for broader application.