Background: Xenon (Xe) is a noble gas that has been used for the last several decades as an anesthetic during surgery. Its antagonistic effect on glutamate subtype of NMDA (N-methyl-d-aspartate) receptors resulted in evaluation of this gas for treatment of CNS pathologies, including psychoemotional disorders. The aim of this study was to assess the behavioral effects of acute inhalation of subanesthetic concentrations of Xe and to study the outcomes of Xe exposure in valproic acid (VPA)-induced rodent model of autism.Methods: We have conducted two series of experiments with a battery of behavioral tests aimed to evaluate locomotion, anxiety- and depression-like behavior, and social behavior in healthy, VPA-treated and Xe-exposed young rats.Results: We have shown that in healthy animals Xe exposure resulted in acute and delayed decrease of exploratory motivation, partial decrease in risk-taking and depressive-like behavior as well as improved sensorimotor integration during the negative geotaxis test. Acute inhalations of Xe in VPA-exposed animals led to improvement in social behavior, decrease in exploratory motivation, and normalization of behavior in forced-swim test.Conclusion: Behavioral modulatory effects of Xe are probably related to its generalized action on excitatory/inhibitory balance within the CNS. Our data suggest that subanesthetic short-term exposures to Xe have beneficial effect on several behavioral modalities and deserves further investigation.

Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel algorithm PeptoGrid that rescores poses predicted by AutoDock Vina according to frequency information of ligand atoms with particular properties appearing at different positions in the target protein’s ligand binding site. We explored the relevance of PeptoGrid ranking with a virtual screening of peptide libraries using angiotensin-converting enzyme and GABAB receptor as targets. A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads.