The structure of an RNA sequence encodes information about its biological function. A sequence is typically predicted to fold to a single minimum free energy conformation. But, an increasing number of RNA molecules are now known to fold into multiple stable structures. Discrete optimization methods are commonly used to predict foldings, and adding experimental data as auxiliary information improves prediction accuracy when there is a single dominant conformation . 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.
A growing number of RNA sequences are now known to have distributions of multiple stable sequences. Recent algorithms use the list of nucleotides in a sequence and auxiliary experimental data to predict such distributions. Although the algorithms are largely successful in identifying a distribution's constituent structures, it remains challenging to recover their relative weightings . 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.
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The structure of an RNA sequence encodes information about its biological function. A sequence is typically predicted to fold to a single minimum free energy conformation. But, an increasing
A growing
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molecules
sequences
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fold into multiple stable structures. Discrete optimization methods are commonly used to predict foldings, and adding experimental data as auxiliary information improves prediction accuracy when there is a single dominant conformation
have distributions of multiple stable sequences. Recent algorithms use the list of nucleotides in a sequence and auxiliary experimental data to predict such distributions. Although the algorithms are largely successful in identifying a distribution's constituent structures, it remains challenging to recover their relative weightings
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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
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