Dong‐Qing Wei

The evolution of the SARS-CoV-2 new variants reported to be 70% more contagious than the earlier one is now spreading fast worldwide. There is an instant need to discover how the new variants interact with the host receptor (ACE2). Among the reported mutations in the Spike glycoprotein of the new variants, three are specific to the receptor-binding domain (RBD) and required insightful scrutiny for new therapeutic options. These structural evolutions in the RBD domain may impart a critical role to the unique pathogenicity of the SARS-CoV-2 new variants. Herein, using structural and biophysical approaches, we explored that the specific mutations in the UK (N501Y), South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y), and hypothetical (N501Y-E484K) variants alter the binding affinity, create new inter-protein contacts and changes the internal structural dynamics thereby increases the binding and eventually the infectivity. Our investigation highlighted that the South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y) variants are more lethal than the UK variant (N501Y). The behavior of the wild type and N501Y is comparable. Free energy calculations further confirmed that increased binding of the spike RBD to the ACE2 is mainly due to the electrostatic contribution. Further, we find that the unusual virulence of this virus is potentially the consequence of Darwinian selection-driven epistasis in protein evolution. The triple mutants (South African and Brazilian) may pose a serious threat to the efficacy of the already developed vaccine. Our analysis would help to understand the binding and structural dynamics of the new mutations in the RBD domain of the Spike protein and demand further investigation in in vitro and in vivo models to design potential therapeutics against the new variants.

Molecular-dynamics simulations are used to show that strongly interacting dipolar spheres can form a ferroelectric nematic phase. This is the first demonstration that dipolar forces alone can create an orientationally ordered liquid state. It is also the first time that the existence of a ferroelectric nematic phase has been established for a model fluid.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.