Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface

• Developed chemical context profiling to score RNA-protein complex by machine learning
• Performed PDB-wide screening of 801 mammalian proteins to score for tRNA binding
• Predicted 37 tRNA-binding proteins; most are not known to interact with RNA
• Experimental validation of six positive and three negative predictions in vivo

SummaryRNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein’s binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs.

Graphical AbstractFigure optionsDownload as PowerPoint slide