Atomic-accuracy structure prediction of macromolecules 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 making use of high-performance computing and the Rosetta framework, we provide first tests of this hypothesis on a benchmark of 15 RNA loop-modeling problems drawn from riboswitches, ribozymes, and the ribosome, including 10 cases that are not solvable by current knowledge-based modeling approaches. For each loop problem, this deterministic stepwise assembly method either reaches atomic accuracy or exposes flaws in Rosetta's all-atom energy function, indicating the resolution of the conformational sampling bottleneck. As a further rigorous test, we have carried out a blind all-atom prediction for a noncanonical RNA motif, the C7.2 tetraloop/receptor, and validated this model through nucleotide-resolution chemical mapping experiments. Stepwise assembly is an enumerative, ab initio build-up method that systematically outperforms existing Monte Carlo and knowledge-based methods for 3D structure prediction.de novo modeling | tertiary structure | dynamic programming | structure mapping | nucleic acid P redicting the 3D structures attained by functional macromolecules is a fundamental challenge in computational biophysics and, more generally, in understanding and engineering living systems. There have been numerous recent successes in the highresolution modeling of small proteins (1-3), protein/RNA complexes (4), and protein/DNA interfaces (5) by optimizing physically realistic energy functions. Nevertheless, rigorous blind trials demonstrate that the predictive power of computational algorithms remains limited, especially if atomic resolution is sought. For essentially all high-resolution modeling problems tackled to date, the shared critical bottleneck of these methods is inefficient sampling of a biopolymer's vast conformational space (1-7). In addition to hindering accurate modeling, poor sampling precludes rigorous tests of the assumed high-resolution energy functions.To gain insight into the conformational sampling bottleneck, we have been focusing on some of the smallest well-defined biomolecular folding problems: RNA motifs, as short as four nucleotides (nts) in length (8). In addition to offering "toy puzzles" for computational methods (9), these modular loops, junctions, and tertiary interactions are fundamental building blocks of structured noncoding RNAs; they attain well-defined noncanonical conformations that in turn define the positions of the canonical double helices in three dimensions. A previous study presented a fragment assembly of RNA with full-atom refinement (FARFAR) method (10), tested on a benchmark of 32 RNA motifs. Although FARFAR re...