RNA 3D Structure Prediction: Progress and Perspective

Abstract

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

Keywords: RNA 3D structure; ensemble prediction; structure evaluation; structure refinement.

Figure 1

The general workflow for modeling RNA 3D structures: structure generation, structure evaluation, and structure refinement. There are three types of predictive models for RNA 3D structures, namely physics-based, knowledge-based, and deep-learning-based models.

Figure 2

(A) Major coarse-grained (CG) representations for existing physics-based models. (B) A typical schematic diagram for building an RNA 3D structure through knowledge-based fragment assembly models.