Key result
A broad class of non-Markov models identified a folding intermediate for the P5ab RNA hairpin during zipping/unzipping that would not have been resolved using standard Markov models.
A novel non-Markov modeling approach for single molecule force spectroscopy data reveals a previously unresolved folding intermediate in the P5ab RNA hairpin.
Non-Markov models may detect hidden RNA intermediates; extends single-molecule analysis but leaves open generalizability to other systems.
Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are noisy and rich in structure. Standard modeling methods normally assume (i) a prespecified number of discrete states and (ii) that transitions between states are Markovian. The data set is then fit to this predetermined model to find a handful of rates describing the transitions between states. We show that it is unnecessary to assume either (i) or (ii) and focus our analysis on the zipping/unzipping transitions of an RNA hairpin. The key is in starting with a very broad class of non-Markov models in order to let the data guide us toward the best model from this very broad class. Our method suggests that there exists a folding intermediate for the P5ab RNA hairpin whose zipping/unzipping is monitored by force spectroscopy experiments. This intermediate would not have been resolved if a Markov model had been assumed from the onset. We compare the merits of our method with those of others.
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Pressé et al. (2014) studied this question. Non-Markov modeling method vs. Standard Markov modeling methods was evaluated on Identification of folding intermediates. A broad class of non-Markov models identified a folding intermediate for the P5ab RNA hairpin during zipping/unzipping that would not have been resolved using standard Markov models.
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