Structural and functional characterisation of Tst2, a novel TRPV1 inhibitory peptide from the Australian sea anemone Telmatactis stephensoni

Molecular Insights into the Low Complexity Secreted Venom of Calliactis polypus

Sea anemones are venomous animals that rely on their venom for prey capture, defense against predators, and intraspecific competition. Currently, comprehensive molecular and evolutionary analyses of the toxin repertoire for sea anemones are limited by a lack of proteomic data for most species. In this study, proteo-transcriptomic analysis was used to expand our knowledge of the proteinaceous components of sea anemone venom by determining the secreted venom proteome of Calliactis polypus. Electromechanical stimulation was used to obtain the secreted venom of C. polypus. We identified a low complexity proteome that was dominated by toxins with similarity to known neurotoxins, as well as six novel toxin candidates. The novel putative toxin candidates were found to be taxonomically restricted to species from the superfamily Metridioidea. Furthermore, the secreted venom of C. polypus had only three putative toxins in common with the venom of acontia from the same species and little similarity with the secreted venom of closely related species. Overall, this demonstrates that regionalized and lineage-specific variability in toxin abundance is common among sea anemone species. Moreover, the limited complexity of the toxin repertoire found in C. polypus supports the idea that peptide neurotoxins make up the dominant toxin arsenal found in the venom of sea anemones.

Effect of Crude Extract from the Sea Anemone Bunodeopsis globulifera on Voltage-Gated Ion Channels from Central and Peripheral Murine Nervous Systems

Sea anemones are an important source of bioactive compounds with potential pharmacological applications. Their toxins are produced and stored in organelles called nematocysts and act on specific targets, including voltage-gated ion channels. To date, sea anemone toxins have demonstrated effects on voltage-gated sodium and potassium channels, facilitating investigations into the structure and function of these proteins. In this study, we evaluated the effect of Bunodeopsis globulifera sea anemone crude extract, and of a low molecular weight fraction, on voltage-gated sodium and calcium channels within the murine nervous system. Notably, the crude extract led to a significant reduction in total sodium current, while also triggering calcium-dependent glutamate release. Furthermore, the low molecular weight fraction, in particular, enhanced total calcium currents and current density. These findings underscore the existence of sea anemone toxins with diverse mechanisms of action beyond those previously documented.

Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction

The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.