Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.

For intrinsically disordered proteins (IDPs), a pressing question is how sequence codes for function. Dynamics serves as a crucial link, reminiscent of the role of structure in sequence–function relations of structured proteins. To define general rules governing sequence-dependent backbone dynamics, we carried out long molecular dynamics simulations of eight IDPs. Blocks of residues exhibiting large amplitudes in slow dynamics are rigidified by local inter-residue interactions or secondary structures. A long region or an entire IDP can be slowed down by long-range contacts or secondary-structure packing. On the other hand, glycines promote fast dynamics and either demarcate rigid blocks or facilitate multiple modes of local and long-range inter-residue interactions. The sequence-dependent backbone dynamics endows IDPs with versatile response to binding partners, with some blocks recalcitrant while others readily adapting to intermolecular interactions.

The folding/unfolding equilibrium of proteins in aqueous medium can be altered by adding small organic molecules generally termed as co-solvents. Denaturants such as urea are instrumental in the unfolding of proteins while protecting osmolytes favour the folded ensemble. Recently, room temperature ionic liquids (ILs) have been shown to counteract the deleterious effect of urea on proteins. In this paper, using atomistic molecular dynamics we show that a ternary mixture containing a particular ammonium-based IL, triethylammonium acetate (TEAA), and urea (in 1 : 5 molar ratio) helps a small 15-residue S-peptide analogue regain most of its native structure, whereas a binary aqueous mixture containing a large amount of urea alone completely distorts it. Our simulations show that the denaturant urea directly interacts with the peptide backbone in the binary mixture while for the ternary mixture both urea as well as the IL are preferentially excluded from the peptide surface.

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