MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks.

The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.

Thermal tuning of protein hydration in a hyperthermophilic enzyme.

Water at the protein surface is an active biological molecule that plays a critical role in many functional processes. Using NMR-restrained MD simulations, we here addressed how protein hydration is tuned at high biological temperatures by analysing homologous acylphosphatase enzymes (AcP) possessing similar structure and dynamics under very different thermal conditions. We found that the hyperthermophilic at 80°C interacts with a lower number of structured waters in the first hydration shell than its human homologous at 37°C.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure.

The docking of synaptic vesicles on the presynaptic membrane induced by α-synuclein is modulated by lipid composition.

α-Synuclein (αS) is a presynaptic disordered protein whose aberrant aggregation is associated with Parkinson's disease. The functional role of αS is still debated, although it has been involved in the regulation of neurotransmitter release via the interaction with synaptic vesicles (SVs). We report here a detailed characterisation of the conformational properties of αS bound to the inner and outer leaflets of the presynaptic plasma membrane (PM), using small unilamellar vesicles.

Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations.

This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models.