Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement

• Published in: Nature communications Vol. 12; no. 1; pp. 2777 - 11
• Main Authors: Xiong, Peng, Wu, Ruibo, Zhan, Jian, Zhou, Yaoqi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.05.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/114/2411; 631/45/535; Accuracy; Algorithms; Energy; Humanities and Social Sciences; Knowledge; Methods; Model accuracy; Molecular biology; multidisciplinary; Nucleotide sequence; Oxygen; Protein structure; Proteins; Ribonucleic acid; RNA; Sampling; Science; Science (multidisciplinary); Statistical analysis; Statistics; Three dimensional models
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-23100-4

Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.