Shuheng Huang

As the initial antibody technology, the preparation of hybridoma cells has been widely used in discovering antibody drugs and is still in use. Various antibody drugs obtained through this technology have been approved for treating human diseases. However, the key to producing hybridoma cells is efficient cell fusion. High-voltage microsecond pulsed electric fields (μsHVPEFs) are currently one of the most common methods used for cell electrofusion. Nevertheless, the membrane potential induced by the external microsecond pulse is proportional to the diameter of the cell, making it difficult to fuse cells of different sizes. Although nanosecond pulsed electric fields (nsPEFs) can achieve the fusion of cells of different sizes, due to the limitation of pore size, deoxyribonucleic acid (DNA) cannot efficiently pass through the cell pores produced by nsPEFs. This directly causes the significant loss of the target gene and reduces the proportion of positive cells after fusion. To achieve an electric field environment independent of cell size and enable efficient cell fusion, we propose a combination of nanosecond pulsed electric fields and low-voltage microsecond pulsed electric fields (ns/μsLVPEFs) to balance the advantages and disadvantages of the two techniques. The results of fluorescence experiments and hybridoma culture experiments showed that after lymphocytes and myeloma cells were stimulated by a pulse (ns/μsLVPEF, μsHVPEF, and control), compared with μsHVPEF, applying ns/μsLVPEF at the same energy could increase the cell fusion efficiency by 1.5–3.0 times. Thus far, we have combined nanosecond and microsecond pulses and provided a practical solution that can significantly increase cell fusion efficiency. This efficient cell fusion method may contribute to the further development of hybridoma technology in electrofusion.

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.

The ATP-binding cassette (ABC) transporter P-glycoprotein (P-gp) is a physiologically essential membrane protein that protects many tissues against xenobiotic molecules, but limits the access of chemotherapeutics into tumor cells, thus contributing to multidrug resistance. The atomic-level mechanism of how substrates and inhibitors differentially affect the ATP hydrolysis by P-gp remains to be elucidated. In this work, atomistic molecular dynamics simulations in an explicit membrane/water environment were performed to explore the effects of substrate and inhibitor binding on the conformational dynamics of P-gp. Distinct differences in conformational changes that mainly occurred in the nucleotide-binding domains (NBDs) were observed from the substrate- and inhibitor-bound simulations. The binding of rhodamine-123 can increase the probability of the formation of an intermediate conformation, in which the NBDs were closer and better aligned, suggesting that substrate binding may prime the transporter for ATP hydrolysis. By contrast, the inhibitor QZ-Leu stabilized NBDs in a much more separated and misaligned conformation, which may result in the deficiency of ATP hydrolysis. The significant differences in conformational modulation of P-gp by substrate and inhibitor binding provided a molecular explanation of how these small molecules exert opposite effects on the ATPase activity. A further structural analysis suggested that the allosteric communication between transmembrane domains (TMDs) and NBDs was primarily mediated by two intracellular coupling helices. Our computational simulations provide not only valuable insights into the transport mechanism of P-gp substrates, but also for the molecular design of P-gp inhibitors.

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.