The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

All-ceramic restoration has become a popular technology for dental restoration; however, the relative bond strength between the ceramic and resin limits its further application. Long-term high bond strength, especially after thermal cycling, is of great importance for effective restoration. The effect of physical and/or chemical surface treatments on bonding durability is seldom reported. To overcome this problem, we investigate the bond strength between lithium disilicate ceramics (LDC) and two kinds of resin cements before and after thermal cycling for a variety of surface treatments including hydrofluoric acid, two kinds of silane and a combined effect. The shear bond strength in every group is characterized by universal mechanical testing machine averaged by sixteen-time measurements. The results show that when treated with HF and a mixed silane, the LDC surface shows maximum bonding strengths of 27.1 MPa and 23.3 MPa with two different resin cements after 5000 thermal cycling, respectively, indicating an excellent ability to resist the damage induced by cyclic expansion and contraction. This long-term high bond strength is attributed to the combined effect of micromechanical interlocking (physical bonding) and the formation of Si-O-Si and -C-C- at the interface (chemical bonding). This result offers great potential for enhancing bond strength for all-ceramic restoration by optimizing the surface treatment.

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.