Deducing conformational variability of intrinsically disordered proteins from infrared spectroscopy with Bayesian statistics

Highlights: • Deduce secondary structure content of intrinsically disordered proteins from IR spectra. • Bayesian analysis to infer conformations of disordered regions of proteins from IR. • Comparison of measured and calculated IR spectra to obtain thermodynamic weights. - Abstract: As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 μs to characterize the conformational ensemble of α-synuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts