Disease-associated missense mutations in GluN2B subunit alter NMDA receptor ligand binding and ion channel properties.

Genetic and bioinformatic analyses have identified missense mutations in GRIN2B encoding the NMDA receptor GluN2B subunit in autism, intellectual disability, Lennox Gastaut and West Syndromes. Here, we investigated several such mutations using a near-complete, hybrid 3D model of the human NMDAR and studied their consequences with kinetic modelling and electrophysiology. The mutants revealed reductions in glutamate potency; increased receptor desensitisation; and ablation of voltage-dependent Mg block. In addition, we provide new views on Mg and NMDA channel blocker binding sites. We demonstrate that these mutants have significant impact on excitatory transmission in developing neurons, revealing profound changes that could underlie their associated neurological disorders. Of note, the NMDAR channel mutant GluN2B unusually allowed Mg permeation, whereas nearby N615I reduced Ca permeability. By identifying the binding site for an NMDAR antagonist that is used in the clinic to rescue gain-of-function phenotypes, we show that drug binding may be modified by some GluN2B disease-causing mutations

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community