Genetic Characterization of African Swine Fever Virus in Various Outbreaks in Central and Southern Vietnam During 2019-2021.

This study aimed to identify potential genetic diversity among African swine fever virus (ASFV) strains circulating in central and southern Vietnam. Thirty ASFV strains were collected from domestic pigs and convalescent pigs with ASFV-infected clinical signs from 19 different provinces of central and southern Vietnam during 2019-2021. A portion of the B646L (p72) gene and the entire E183L (p54), CP204L (p30), and B602L (CVR) genes were amplified, purified, and sequenced. Web-based BLAST and MEGA X software were used for sequence analysis. Analysis of the partial B646L (p72) gene, the full-length E183L (p54) and CP204L (p30) genes, and the central hypervariable region (CVR) of the B602L gene sequence showed that all 30 ASFV isolates belonged to genotype II and were 100% identical to the previously identified strains in Vietnam and China. Analysis of the p72, p54, and p30 regions did not indicate any change in the nucleotide and amino acid sequences among these strains in 3 years of research. No novel variant was found in the CVR within the B602L gene. Analysis of the CVR showed that these ASFV strains belong to subgroup XXXII. The results of this study revealed that these ASFVs shared high similarity with ASFV isolates detected previously in northern Vietnam and China. Taken together, the results of this study and a previous study in Vietnam showed high stability and no genetic diversity in the ASFV genome.

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries.

Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.