Protein ligand binding site prediction using graph transformer neural network.

Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to be still insufficient. In this study, we introduce an approach that leverages a graph transformer neural network to rank the results of a geometry-based pocket detection method. We also created a larger training dataset compared to the conventionally used sc-PDB and investigated the correlation between the dataset size and prediction performance. Our findings indicate that utilizing a graph transformer-based method alongside a larger training dataset could enhance the performance of ligand binding site prediction.

Neuronal and non-neuronal functions of the synaptic cell adhesion molecule neurexin in Nematostella vectensis.

The evolutionary transition from diffusion-mediated cell-cell communication to faster, targeted synaptic signaling in animal nervous systems is still unclear. Genome sequencing analyses have revealed a widespread distribution of synapse-related genes among early-diverging metazoans, but how synaptic machinery evolved remains largely unknown. Here, we examine the function of neurexins (Nrxns), a family of presynaptic cell adhesion molecules with critical roles in bilaterian chemical synapses, using the cnidarian model, Nematostella vectensis. Delta-Nrxns are expressed mainly in neuronal cell clusters that exhibit both peptidergic and classical neurotransmitter signaling. Knockdown of δ-Nrxn reduces spontaneous peristalsis of N. vectensis polyps. Interestingly, gene knockdown and pharmacological studies suggest that δ-Nrxn is involved in glutamate- and glycine-mediated signaling rather than peptidergic signaling. Knockdown of the epithelial α-Nrxn reveals a major role in cell adhesion between ectodermal and endodermal epithelia. Overall, this study provides molecular, functional, and cellular insights into the pre-neural function of Nrxns, as well as key information for understanding how and why they were recruited to the synaptic machinery.