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

By Peng Xiong, Ruibo Wu, Jian Zhan, Yaoqi Zhou. 2021

59 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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In Molecular Biology

1. Peng Xiong
2. Yaoqi Zhou
3. K. Chen
4. Sheng Wang

Citations in the corpus

• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, neutral): “As a preventive measure to resolve any remaining structural clashes and violations, we may relax our model predictions using a restrained energy minimization procedure, such as AMBER 53 and BRiQ 52 .”
• Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “For example, the fraction of the test targets for which DRfold achieved a lower RMSD than the control methods was 80.0% (to 3dRNA), 82.5% (FARFAR2), 72.5% (RNAComposer), 75.0% (RNA-BRiQ), and 80.0% (SimRNA), respectively, as shown in Fig.”
• Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, methodology): “While there exist methods to optimize 3D structure with the minimum energy [33] given 2D information, deep learning methods could be utilized to solve the downgraded 2D problem (secondary structure, distance structure).”
• RNA tertiary structure modeling with BRiQ potential in CASP15 (2023, background): “28 Unlike other mostly coarse-grained statistical potentials, the BRiQ potential is a full-atom potential that includes all bonded and non-bonded interactions.”