WIN55,212-2, a Dual Modulator of Cannabinoid Receptors and G Protein-Coupled Inward Rectifier Potassium Channels

The Sobering Sting: Oleoyl Serotonin Is a Novel Stephanoconus Snail Venom-Derived Antagonist of Cannabinoid Receptors That Counteracts Learning and Memory Deficits

Cannabinoid receptors (CB1 and CB2) are promising targets for a better understanding of neurological diseases. Nevertheless, only a few ligands of CB have reached clinical application so far. Venoms are considered as interesting sources of novel biologically active compounds. Here, we describe an endocannabinoid-like molecule, oleoyl serotonin (OS), present in the venom of Stephanoconus snails. Using electrophysiological assays, it was shown that OS inhibits CB1 and CB2. Structure-activity relationship studies using a chimeric CB1/2 revealed that the domain encompassing the transmembrane helix V (TMHV)- intracellular loop 3 (ICL3)-TMHVI of the CB2 is critical for the binding and function of OS. We concluded that OS binds to sites of the CB2 that are different from the binding sites of the non-selective CB agonist WIN55,212-2. Behavioral assays in mice showed that OS counteracted learning and memory deficits caused by WIN55,212-2. Furthermore, a selectivity screening of OS showed high selectivity for CB over various ion channels and receptors. Overall, OS may represent a new approach to the prevention and treatment of learning and memory cognition impairment in neurological diseases.

A GraphSAGE-based model with fingerprints only to predict drug-drug interactions

Drugs are an effective way to treat various diseases. Some diseases are so complicated that the effect of a single drug for such diseases is limited, which has led to the emergence of combination drug therapy. The use multiple drugs to treat these diseases can improve the drug efficacy, but it can also bring adverse effects. Thus, it is essential to determine drug-drug interactions (DDIs). Recently, deep learning algorithms have become popular to design DDI prediction models. However, most deep learning-based models need several types of drug properties, inducing the application problems for drugs without these properties. In this study, a new deep learning-based model was designed to predict DDIs. For wide applications, drugs were first represented by commonly used properties, referred to as fingerprint features. Then, these features were perfectly fused with the drug interaction network by a type of graph convolutional network method, GraphSAGE, yielding high-level drug features. The inner product was adopted to score the strength of drug pairs. The model was evaluated by 10-fold cross-validation, resulting in an AUROC of 0.9704 and AUPR of 0.9727. Such performance was better than the previous model which directly used drug fingerprint features and was competitive compared with some other previous models that used more drug properties. Furthermore, the ablation tests indicated the importance of the main parts of the model, and we analyzed the strengths and limitations of a model for drugs with different degrees in the network. This model identified some novel DDIs that may bring expected benefits, such as the combination of PEA and cannabinol that may produce better effects. DDIs that may cause unexpected side effects have also been discovered, such as the combined use of WIN 55,212-2 and cannabinol. These DDIs can provide novel insights for treating complex diseases or avoiding adverse drug events.

AsKC11, a Kunitz Peptide from Anemonia sulcata, Is a Novel Activator of G Protein-Coupled Inward-Rectifier Potassium Channels

(1) Background: G protein-coupled inward-rectifier potassium (GIRK) channels, especially neuronal GIRK1/2 channels, have been the focus of intense research interest for developing drugs against brain diseases. In this context, venom peptides that selectively activate GIRK channels can be seen as a new source for drug development. Here, we report on the identification and electrophysiological characterization of a novel activator of GIRK1/2 channels, AsKC11, found in the venom of the sea anemone Anemonia sulcata. (2) Methods: AsKC11 was purified from the sea anemone venom by reverse-phase chromatography and the sequence was identified by mass spectrometry. Using the two-electrode voltage-clamp technique, the activity of AsKC11 on GIRK1/2 channels was studied and its selectivity for other potassium channels was investigated. (3) Results: AsKC11, a Kunitz peptide found in the venom of A. sulcata, is the first peptide shown to directly activate neuronal GIRK1/2 channels independent from Gi/o protein activity, without affecting the inward-rectifier potassium channel (IRK1) and with only a minor effect on K_V1.6 channels. Thus, AsKC11 is a novel activator of GIRK channels resulting in larger K⁺ currents because of an increased chord conductance. (4) Conclusions: These discoveries provide new insights into a novel class of GIRK activators.