Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction
This work addresses the challenge of RNA 3D structure prediction, which is important for understanding RNA functions and drug discovery, but it appears incremental as it builds on existing GNN methods with physics-inspired modifications.
The authors tackled the problem of predicting RNA 3D structures by developing a Graph Neural Network-based scoring function, PaxNet, which significantly outperformed state-of-the-art baselines on two benchmarks.
Biological functions of RNAs are determined by their three-dimensional (3D) structures. Thus, given the limited number of experimentally determined RNA structures, the prediction of RNA structures will facilitate elucidating RNA functions and RNA-targeted drug discovery, but remains a challenging task. In this work, we propose a Graph Neural Network (GNN)-based scoring function trained only with the atomic types and coordinates on limited solved RNA 3D structures for distinguishing accurate structural models. The proposed Physics-aware Multiplex Graph Neural Network (PaxNet) separately models the local and non-local interactions inspired by molecular mechanics. Furthermore, PaxNet contains an attention-based fusion module that learns the individual contribution of each interaction type for the final prediction. We rigorously evaluate the performance of PaxNet on two benchmarks and compare it with several state-of-the-art baselines. The results show that PaxNet significantly outperforms all the baselines overall, and demonstrate the potential of PaxNet for improving the 3D structure modeling of RNA and other macromolecules. Our code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN.