An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling.

Area of Science:

• Computational Biology
• Structural Biology
• Biophysics

Background:

• Macromolecular structure prediction is crucial for understanding biological function.
• Current methods struggle with conformational sampling, limiting atomic-accuracy predictions.
• Physically realistic energy functions are key but require efficient sampling.

Purpose of the Study:

• To present and test the "stepwise ansatz" for ab initio RNA structure prediction.
• To address the conformational sampling bottleneck in biopolymer modeling.
• To develop a method that achieves atomic accuracy in macromolecular modeling.

Main Methods:

• The "stepwise ansatz" recursively builds atomic-detail models in small steps.
• Enumeration of millions of conformations per monomer and all build-up paths.
• Utilized high-performance computing and the Rosetta framework for modeling.

Main Results:

• Successfully modeled 15 RNA loop problems, including 10 previously unsolvable cases.
• The stepwise assembly method achieved atomic accuracy or identified energy function flaws.
• A blind prediction of a C7.2 tetraloop/receptor was validated experimentally.

Conclusions:

• The stepwise ansatz resolves the conformational sampling bottleneck for RNA structure prediction.
• This enumerative, ab initio method outperforms existing Monte Carlo and knowledge-based approaches.
• The approach provides a robust framework for accurate atomic-detail modeling of biopolymers.