Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.

Abstract

Accurately predicting the structures of RNA-protein complexes remains a major challenge. Recently, machine learning-based methods such as AlphaFold3 and RosettaFoldNA have been proposed. However, most conventional approaches rely on docking simulations to generate candidate structures, which are then identified as accurate using various methods. This study presents a method that integrates specialized molecular dynamics simulations and machine learning (ML) techniques to identify the correct structure among many docking poses. First, steered molecular dynamics simulations are performed to estimate the stability of the candidate structures. The simulation data then serve as the training data for a ML model, which classifies the results as either correct or incorrect. Next, the candidates predicted as correct are narrowed down using thermodynamic simulations and ML methods. Findings indicated that candidate structures could be classified as correct or incorrect with an accuracy of 0.934 in the RNA-protein docking simulation results. Additionally, we used AlphaFold3 to predict 15 RNA-protein complexes that Zou's group categorized as difficult, medium or easy category. Subsequently, our method classified these binding structures as correct or incorrect, with accuracies of 0.80, 0.92 and 0.96, respectively. Thus, our method is powerful for accurately predicting the structures of RNA-protein complexes.