Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at .

N-methyl-D-aspartate receptors (NMDARs) are critically involved in basic brain functions and neurodegeneration as well as tumor invasiveness. Targeting specific subtypes of NMDARs with distinct activities has been considered an effective therapeutic strategy for neurological disorders and diseases. However, complete elimination of off-target effects of small chemical compounds has been challenging and thus, there is a need to explore alternative strategies for targeting NMDAR subtypes. Here we report identification of a functional antibody that specifically targets the GluN1-GluN2B NMDAR subtype and allosterically down-regulates ion channel activity as assessed by electrophysiology. Through biochemical analysis, x-ray crystallography, single-particle electron cryomicroscopy, and molecular dynamics simulations, we show that this inhibitory antibody recognizes the amino terminal domain of the GluN2B subunit and increases the population of the non-active conformational state. The current study demonstrates that antibodies may serve as specific reagents to regulate NMDAR functions for basic research and therapeutic objectives.

Despite their classification as ionotropic glutamate receptors, GluD receptors are not functional ligand-gated ion channels and do not bind glutamate. GluD2 receptors bind D-serine and coordinate trans-synaptic complexes that regulate synaptic plasticity. Instead of opening the ion channel pore, mechanical tension produced from closure of GluD2 ligand-binding domains (LBDs) drives conformational rearrangements for non-ionotropic signaling. We report computed conformational free energy landscapes for the GluD2 LBD in apo and D-serine-bound states. Unexpectedly, the conformational free energy associated with GluD2 LBD closure upon D-serine binding is greater than that for AMPA, NMDA, and kainate receptor LBDs upon agonist binding. This excludes insufficient force generation as an explanation for lack of ion channel activity in GluD2 receptors and suggests non-ionotropic conformational rearrangements do more work than pore opening. We also report free energy landscapes for GluD2 LBD harboring a neurodegenerative mutation and demonstrate selective stabilization of closed conformations in the apo state.