Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Zhendong Liu,Qionghai Dai,Dongyan Li,Xi Chen,Yurong Yang,Xinrong Lv

doi:10.3389/fgene.2021.813604

Zhendong Liu, Qionghai Dai + Show 4 more

Open Access

https://doi.org/10.3389/fgene.2021.813604

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Abstract

Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle. Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results. Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high. Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.

Highlights

Biological macromolecules such as protein, ribose, and polysaccharides are indispensable substances in living systems, and the structural prediction of biological macromolecules is a grand challenge of bioinformatics
We developed stepwise Monte Carlo parallelization (SMCP) based on the Rosetta software suite framework, which is a Monte Carlo optimization algorithm whose primary moves remain the stepwise addition or deletion moves
The parameter n indicates the number of parallel threads, and the parameter m indicates the number of Monte Carlo samples

Summary

Introduction

Biological macromolecules such as protein, ribose, and polysaccharides are indispensable substances in living systems, and the structural prediction of biological macromolecules is a grand challenge of bioinformatics. RNA Tertiary Structure Prediction Motif PDB id. As a blind experiment to evaluate the RNA tertiary structure modeling, the purpose of RNA puzzle is to find out the capacity and bottleneck in RNA prediction. On the other hand, understanding the RNA structures can provide a basis for medical progress, for example, providing the theoretical basis for designing targeted ribosome drugs (Shi et al, 2014), measuring the epigenomic features of each NC RNA type to provide a theoretical basis for human disease research, especially cancer (Boukelia et al, 2020), and providing a new perspective for disease diagnosis and prognosis (Lu et al, 2021). It is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle

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Conclusion