Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing the structural ensembles of intrinsically disordered proteins (IDPs) is essential for studying structure-function relationships as conformational dynamics govern proteins’ biological functions. Due to the notable difference between the neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, the experimental timescale typically results in measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a protein complex formed by two IDP domains, the NCBD/ACTR complex, using data from selective labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. By incorporating structural metrics derived from the SANS experiments as constraints in MD structure classification, we describe a structural ensemble that captures the experimental SANS and, in addition, NMR data better than MD ensembles generated by one single force field. Based on structural similarity, DL reveals three clusters of distinct conformations in the ensemble. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.