Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism - Liu, Zhendong; Yang, Yurong; Li, Dongyan; Lv, Xinrong; Chen, Xi; Dai, Qionghai (Frontiers in Genetics, 2022)
Background: Macromolecules structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model effectively RNA tertiary structures when the de novo modeling of RNA involves solving a well-defined small puzzle. Methods: In this paper, we introduce a Stepwise Monte Carlo Parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using Monte Carlo algorithm and stepwise ansatz hypothesis. And SMCP using 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 9 single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including 6 motifs that can not be solved by knowledge mining-based modeling algorithms. Experimental results show that the modeling accuracy of SMCP algorithm is up to 0.14Å, and the modeling integrity on this benchmark is extremely high. Conclusions: SMCP is an ab initio modeling algorithm, substantially outperforms previous algorithms in 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 biomedical field.