TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep Neural Networks.

Knowledge of protein structures is essential to understand proteins' functions, evolution, dynamics, stabilities, and interactions and for data-driven protein- or drug design. Yet, experimental structure determination rates are far exceeded by that of next-generation sequencing, resulting in less than 1/1000th of proteins having an experimentally known 3D structure. Computational structure prediction seeks to alleviate this problem, and the Critical Assessment of Protein Structure Prediction (CASP) has shown the value of consensus and meta-methods that utilize complementary algorithms. However, traditionally, such methods employ majority voting during template selection and model averaging during refinement, which can drive the model away from the native fold if it is underrepresented in the ensemble. Here, we present TopModel, a fully automated meta-method for protein structure prediction. In contrast to traditional consensus and meta-methods, TopModel uses top-down consensus and deep neural networks to select templates and identify and correct wrongly modeled regions. TopModel combines a broad range of state-of-the-art methods for threading, alignment, and model quality estimation and provides a versatile workflow and toolbox for template-based structure prediction. TopModel shows a superior template selection, alignment accuracy, and model quality for template-based structure prediction on the CASP10-12 datasets compared to 12 state-of-the-art stand-alone primary predictors. TopModel was validated by prospective predictions of the nisin resistance protein (NSR) protein from Streptococcus agalactiae and LipoP from Clostridium difficile, showing far better agreement with experimental data than any of its constituent primary predictors. These results, in general, demonstrate the utility of TopModel for protein structure prediction and, in particular, show how combining computational structure prediction with sparse or low-resolution experimental data can improve the final model.

Solubilization and stabilization of macular carotenoids by water soluble oligosaccharides and polysaccharides.

Xanthophyll carotenoids zeaxanthin and lutein play a special role in the prevention and treatment of visual diseases. These carotenoids are not produced by the human body and must be consumed in the diet. On the other hand, extremely low water solubility of these carotenoids and their instability restrict their practical application as components of food or medicinal formulations. Preparation of supramolecular complexes of zeaxanthin and lutein with glycyrrhizic acid, its disodium salt and the natural polysaccharide arabinogalactan allows one to minimize the aforementioned disadvantages when carotenoids are used in food processing as well as for production of therapeutic formulations with enhanced solubility and stability. In the present study, the formation of supramolecular complexes was investigated by NMR relaxation, surface plasmon resonance (SPR) and optical absorption techniques. The complexes increase carotenoid solubility more than 1000-fold. The kinetics of carotenoid decay in reactions with ozone molecules, hydroperoxyl radicals and metal ions were measured in water and organic solutions, and significant increases in oxidation stability of lutein and zeaxanthin in arabinogalactan and glycyrrhizin complexes were detected.