Can biopolymer structures be sampled enumeratively? Atomic-accuracy RNA loop modeling by a stepwise ansatz

TL;DR

This paper introduces a novel enumerative stepwise assembly method in Rosetta for atomic-accuracy RNA loop modeling, overcoming sampling limitations and outperforming existing methods, with successful blind prediction of a previously unknown RNA motif.

Contribution

The paper presents the first enumerative, ab initio build-up approach for RNA structure prediction that systematically outperforms prior Monte Carlo and knowledge-based methods.

Findings

SWA achieves atomic accuracy on challenging RNA loops.

SWA exposes flaws in Rosetta's energy function.

Successful blind prediction of a novel RNA motif.

Abstract

Atomic-accuracy structure prediction of macromolecules is a long-sought goal of computational biophysics. Accurate modeling should be achievable by optimizing a physically realistic energy function but is presently precluded by incomplete sampling of a biopolymer's many degrees of freedom. We present herein a working hypothesis, called the "stepwise ansatz", for recursively constructing well-packed atomic-detail models in small steps, enumerating several million conformations for each monomer and covering all build-up paths. By implementing the strategy in Rosetta and making use of high-performance computing, we provide first tests of this hypothesis on a benchmark of fifteen RNA loop modeling problems drawn from riboswitches, ribozymes, and the ribosome, including ten cases that were not solvable by prior knowledge based modeling approaches. For each loop problem, this deterministic stepwise assembly (SWA) method either reaches atomic accuracy or exposes flaws in Rosetta's all-atom energy function, indicating the resolution of the conformational sampling bottleneck. To our knowledge, SWA is the first enumerative, ab initio build-up method to systematically outperform existing Monte Carlo and knowledge-based methods for 3D structure prediction. As a rigorous experimental test, we have applied SWA to a small RNA motif of previously unknown structure, the C7.2 tetraloop/tetraloop-receptor, and stringently tested this blind prediction with nucleotide-resolution structure mapping data.