Developing machine-learning-based amyloidogenicity predictors with Cross-Beta DB.

Introduction: The importance of protein amyloidogenesis, associated with various diseases and functional roles, has driven the creation of computational predictors of amyloidogenicity. The accuracy of these predictors, particularly those utilizing artificial intelligence technologies, heavily depends on the quality of the data.

Methods: We built Cross-Beta DB, a database containing high-quality data on known cross-β amyloids formed under natural conditions.

The automated optimisation of a coarse-grained force field using free energy data.

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability.

Variations in periplasmic loop interactions determine the pH-dependent activity of the hexameric urea transporter UreI from Helicobacter pylori: a molecular dynamics study.

Background: Helicobacter pylori is an important factor in the development of diseases such as ulcer and gastric cancer. This bacterium uses a periplasmic transporter, UreI, to deliver urea to the intracelullar space, where later it is transformed into ammonia by the cytoplasmic enzyme urease to survive the acidic condition of the human stomach. The UreI transporter presents a pH-dependent activity, where this pH-dependence remains unknown at a structural level.