Mitochondrial calcium uniporter (MCU) channel is responsible for Ruthenium Red-sensitive mitochondrial calcium uptake. Here, we demonstrate MCU oligomerization by immunoprecipitation and Förster resonance energy transfer (FRET) and characterize a novel protein (MCUb) with two predicted transmembrane domains, 50% sequence similarity and a different expression profile from MCU. Based on computational modelling, MCUb includes critical amino-acid substitutions in the pore region and indeed MCUb does not form a calcium-permeable channel in planar lipid bilayers. In HeLa cells, MCUb is inserted into the oligomer and exerts a dominant-negative effect, reducing the [Ca 2 þ ] mt increases evoked by agonist stimulation. Accordingly, in vitro co-expression of MCUb with MCU drastically reduces the probability of observing channel activity in planar lipid bilayer experiments. These data unveil the structural complexity of MCU and demonstrate a novel regulatory mechanism, based on the inclusion of dominant-negative subunits in a multimeric channel, that underlies the fine control of the physiologically and pathologically relevant process of mitochondrial calcium homeostasis.

Supervised MD (SuMD) is a computational method that allows the exploration of ligand-receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand-receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR-ligand complex embedded into explicit lipid-water environment.

In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound, followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. The generative design of novel scaffolds and functional groups can cover unexplored regions of chemical space that still possess lead-like properties.

Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand-protein recognition pathways of six different case studies that we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand-protein recognition pathways.