Centipedes are among the oldest venomous arthropods that use their venom to subdue the prey. The major components of centipede venom are a variety of low-molecular-weight peptide toxins that have evolved to target voltage-gated ion channels to interfere with the central system of prey and produce pain or paralysis for efficient hunting. Peptide toxins usually contain several intramolecular disulfide bonds, which confer chemical, thermal and biological stability. In addition, centipede peptides generally have novel structures and high potency and specificity and therefore hold great promise both as diagnostic tools and in the treatment of human disease. Here, we review the centipede peptide toxins with reported effects on ion channels, including Nav, Kv, Cav and the nonselective cation channel polymodal transient receptor potential vanilloid 1 (TRPV1).

Transient receptor potential vanilloid 1 (TRPV1), a non-selective cation channel, can be directly activated by oxidants through cysteine modification. However, the patterns of cysteine modification are unclear. Structural analysis showed that the free sulfhydryl groups of residue pairs C387 and C391 were potentially oxidized to form a disulfide bond, which is expected to be closely related to the redox sensing of TRPV1. To investigate if and how the redox states of C387 and C391 activate TRPV1, homology modeling and accelerated molecular dynamic simulations were performed. The simulation revealed the conformational transfer during the opening or closing of the channel. The formation of a disulfide bond between C387 and C391 leads to the motion of pre-S1, which further propagates conformational change to TRP, S6, and the pore helix from near to far. Residues D389, K426, E685–Q691, T642, and T671 contribute to the hydrogen bond transfer and play essential roles in the opening of the channel. The reduced TRPV1 was inactivated mainly by stabilizing the closed conformation. Our study elucidated the redox state of C387–C391 mediated long-range allostery of TRPV1, which provided new insights into the activation mechanism of TRPV1 and is crucial for making significant advances in the treatment of human diseases.

TRPV1 is a ligand-gated ion channel and plays an important role in detecting noxious heat and pain. A new TRPV1 antagonist RL-RhTx was discovered.

Peptide toxins generally have extreme pharmacological activities and provide a rich source for the discovery of drug leads. However, determining the optimal activity of a new peptide can be a long and expensive process. In this study, peptide toxins were retrieved from Uniprot; three positive-unlabeled (PU) learning schemes, adaptive basis classifier, two-step method, and PU bagging were adopted to develop models for predicting the biological function of new peptide toxins. All three schemes were embedded with 14 machine learning classifiers. The prediction results of the adaptive base classifier and the two-step method were highly consistent. The models with top comprehensive performances were further optimized by feature selection and hyperparameter tuning, and the models were validated by making predictions for 61 three-finger toxins or the external HemoPI dataset. Biological functions that can be identified by these models include cardiotoxicity, vasoactivity, lipid binding, hemolysis, neurotoxicity, postsynaptic neurotoxicity, hypotension, and cytolysis, with relatively weak predictions for hemostasis and presynaptic neurotoxicity. These models are discovery-prediction tools for active peptide toxins and are expected to accelerate the development of peptide toxins as drugs.

Focal adhesion kinase (FAK) is a kind of tyrosine kinase that modulates integrin and growth factor signaling pathways. FAK is a promising therapeutic target, involving in cancer cell migration, proliferation and survival. In order to design highly active FAK inhibitors, a three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics simulation were combined to study the interaction between pyrimidine derivatives and FAK. Both the comparative molecular field analysis (CoMFA) model (q² = 0.664, r² = 0.973) and comparative molecular similarity index analysis (CoMSIA) model (q² = 0.755, r² = 0.999) indicated that the constructed QSAR models are stable and reliable. Based on the model, several new compounds were designed and their inhibitory activities were validated by molecular docking and molecular dynamics simulation. The results show that the complex of FAK with new designed compounds is stable during the simulation, which supports the fact that the new compounds are potential FAK inhibitors.