Before revision:  In this paper, we analyze the outputs of existing structural prediction models when they receive auxiliary data derived from a mixture of structures. Under a binary model of auxiliary data, we find that current structural prediction methods typically favor distributions with one dominant structure , and hence cannot guarantee accurate reconstruction of multimodal distributions. Additionally, we analyze empirical distributions of auxiliary data used in current prediction models. We show that even when the structures in a distribution are known in advance, it is difficult to determine the weightings of the structures using auxiliary data.
After revision:  In this paper, we quantify this issue using a total variation distance. Then, we prove under a Nussinov-Jacobson model that a large proportion of RNA structure pairs cannot be jointly reconstructed with low total variation distance. Finally, we characterize the uncertainty in predicting conformational ratios by analyzing the amount of information in the auxiliary data.