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Molecular geometry and harmonic frequency calculations are essential in thermochemical computations, with density functional theory (DFT) being widely employed for vibrational frequency predictions due to its efficiency and accuracy. In this study, we assessed the precision of 28 Minnesota based functionals with three different basis sets using the VIBFREQ1295 dataset. Scaling factors are necessary for predicting fundamental frequencies, global scaling factors were fitted by using F38/10 and VIBFREQ1295 datasets. The superior performing functionals were then fitted based on vibrational frequency ranges to obtain frequency‐range‐specific scaling factors. We observed consistent outlier across various model chemistries in vibrational frequency predictions, with alternative scaling factors showing minimal impact on reducing outlier occurrences. Besides, large basis sets are not indispensably required for fundamental frequency predictions. M06‐L, revM06‐L, SOGGA11‐X, PW6B95‐D3(BJ), CF22D, and M06‐2X consistently exhibit excellent performance across the three basis sets. When using frequency‐range‐specific scaling factors, the root mean squard errors (RMSEs) and median absolute errors (MedAEs) of almost all the selected functionals were reduced. PW6B95‐D3(BJ), CF22D, and MN12‐SX exhibited the lowest RMSEs. Comparisons were also done for different data classifications; the dataset was classified by the elements of the molecules, vibrational frequency intervals, and vibrational modes. [ABSTRACT FROM AUTHOR]

The inhibition of Nav1.7 is a promising strategy for the development of analgesic treatments. Spider venom‐derived peptide toxins are recognized as significant sources of Nav1.7 inhibitors. However, their development has been impeded by limited selectivity. In this study, eight peptide toxins from three distinct spider venom Nav channel families demonstrated robust inhibition of hNav1.7, rKv4.2, and rKv4.3 (rKv4.2/4.3) currents, exhibiting a similar mode of action. The analysis of structure and function relationship revealed a significant overlap in the pharmacophore responsible for inhibiting hNav1.7 and rKv4.2 by HNTX‐III, although Lys25 seems to play a more pivotal role in the inhibition of rKv4.2/4.3. Pharmacophore‐guided rational design is employed for the development of an mGpTx1 analogue, mGpTx1‐SA, which retains its inhibition of hNav1.7 while significantly reducing its inhibition of rKv4.2/4.3 and eliminating cardiotoxicity. Moreover, mGpTx1‐SA demonstrates potent analgesic effects in both inflammatory and neuropathic pain models, accompanied by an improved in vivo safety profile. The results suggest that off‐target inhibition of rKv4.2/4.3 by specific spider peptide toxins targeting hNav1.7 may arise from a conserved binding motif. This insight promises to facilitate the design of hNav1.7‐specific analgesics, aimed at minimizing rKv4.2/4.3 inhibition and associated toxicity, thereby enhancing their suitability for therapeutic applications. [ABSTRACT FROM AUTHOR]

Voltage-gated sodium channels (NaVs) are activated by transiting the voltage sensor from the deactivated to the activated state. The crystal structures of several bacterial NaVs have captured the voltage sensor module (VSM) in an activated state, but structure of the deactivated voltage sensor remains elusive. In this study, we sought to identify peptide toxins stabilizing the deactivated VSM of bacterial NaVs. We screened fractions from several venoms and characterized a cystine knot toxin called JZTx-27 from the venom of tarantula Chilobrachys jingzhao as a high-affinity antagonist of the prokaryotic NaVs NsVBa (nonselective voltage-gated Bacillus alcalophilus) and NaChBac (bacterial sodium channel from Bacillus halodurans) (IC50 = 112 nM and 30 nM, respectively). JZTx-27 was more efficacious at weaker depolarizing voltages and significantly slowed the activation but accelerated the deactivation of NsVBa, whereas the local anesthetic drug lidocaine was shown to antagonize NsVBa without affecting channel gating. Mutation analysis confirmed that JZTx-27 bound to S3-4 linker of NsVBa, with F98 being the critical residue in determining toxin affinity. All electrophysiological data and in silico analysis suggested that JZTx-27 trapped VSM of NsVBa in one of the deactivated states. In mammalian NaVs, JZTx-27 preferably inhibited the inactivation of NaV1.5 by targeting the fourth transmembrane domain. To our knowledge, this is the first report of peptide antagonist for prokaryotic NaVs. More important, we proposed that JZTx-27 stabilized the NsVBa VSM in the deactivated state and may be used as a probe to determine the structure of the deactivated VSM of NaVs.-Tang, C., Zhou, X., Nguyen, P. T., Zhang, Y., Hu, Z., Zhang, C., Yarov-Yarovoy, V., DeCaen, P. G., Liang, S., Liu, Z. A novel tarantula toxin stabilizes the deactivated voltage sensor of bacterial sodium channel.

In the development and progression of cervical cancer, oxidative stress plays an important role within the cells. Among them, Solute Carrier Family 7 Member 11 (SLC7A11/xCT) is crucial for maintaining the synthesis of glutathione and the antioxidant system in cervical cancer cells. In various tumor cells, studies have shown that SLC7A11 inhibits ferroptosis, a form of cell death, by mediating cystine uptake and maintaining glutathione synthesis. Additionally, SLC7A11 is also involved in promoting tumor metastasis and immune evasion. Therefore, inhibiting the SLC7A11/xCT axis has become a potential therapeutic strategy for cervical cancer. In this study, through structure-based high-throughput virtual screening, a compound targeting the SLC7A11/xCT axis named compound 1 (PubChem CID: 3492258) was discovered. In vitro experiments using HeLa cervical cancer cells as the experimental cell model showed that compound 1 could reduce intracellular glutathione levels, increase glutamate and reactive oxygen species (ROS) levels, disrupt the oxidative balance within HeLa cells, and induce cell death. Furthermore, molecular dynamics simulation results showed that compound 1 has a stronger binding affinity with SLC7A11 compared to the positive control erastin. Overall, all the results mentioned above indicate the potential of compound 1 in targeting the SLC7A11/xCT axis and treating cervical cancer both in vitro and in silico. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common‐Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common‐Toxicity task but also enhances the stability of the model prediction. It shows that the graph‐based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs. Machine learning has shown great promise in predicting toxicity of small molecules. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network architecture, aiming to predict the toxicity of small molecules. ToxMPNN gives precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset, which contains 27 toxic endpoints in seven toxicity categories and a binary classification Common‐Toxicity task, and can be used as an effective tool to predict the toxicity of small molecules. [ABSTRACT FROM AUTHOR]