Abstract
Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.
Highlights
Disordered Proteins (IDPs) do not fold into stable conformations, because their free energy landscapes possess many shallow local minima
In this work we have shown how chemical shifts can be used to improve the configurations arising from molecular dynamics simulations of intrinsically disordered proteins
We have introduced a systematic method to assess the amount of reweighting (θ) needed to fit the experimental chemical shifts based on cross-validation
Summary
Disordered Proteins (IDPs) do not fold into stable conformations, because their free energy landscapes possess many shallow local minima. Some of these may correspond to conformations rich in secondary structure and α-helices appear to be the most common of these regular structures. Their ability to fold into different conformations allows IDPs to interact with different binding partners and tuning their populations by post-translational modifications can allow for the regulation of important cellular functions. Several methods exist to generate ensembles of configurations representing the conformational heterogeneity of IDPs, including all-atom molecular dynamics simulations [1], implicit-solvent. Because equilibrium sampling of the IDPs conformational space is challenging to achieve, mainly due to its enormous size
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