Identification of metal ion-binding sites in RNA structures using deep learning method

Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA structures, it becomes possible to identify the metal ion-binding sites in RNA structures through in-silico methods. Here, we propose an approach called Metal3DRNA to identify the binding sites of the most common metal ions (Mg2+, Na+ and K+) in RNA structures by using a three-dimensional convolutional neural network model. The negative samples, screened out based on the analysis for binding surroundings of metal ions, are more like positive ones than the randomly selected ones, which are beneficial to a powerful predictor construction. The microenvironments of the spatial distributions of C, O, N and P atoms around a sample are extracted as features. Metal3DRNA shows a promising prediction power, generally surpassing the state-of-the-art methods FEATURE and MetalionRNA. Finally, utilizing the visualization method, we inspect the contributions of nucleotide atoms to the classification in several cases, which provides a visualization that helps to comprehend the model. The method will be helpful for RNA structure prediction and dynamics simulation study.

Automated summary

Metal ions are essential for the folding, stability and function of RNA molecules, but they are difficult to detect experimentally. A method called Metal3DRNA is proposed to identify metal ion-binding sites in RNA structures using a three-dimensional convolutional neural network model. The method shows promising prediction power and outperforms state-of-the-art methods. The visualization method used provides insights into the contributions of nucleotide atoms to classification. This method will aid in RNA structure prediction and dynamics simulation studies.