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Hwang, S. S.

Publications and source records attributed to Hwang, S. S..

2 recordsLinked to original sources

In silico modelling and characterization of Epstein Barr virus LMP1 protein

Latent membrane protein 1 (LMP1) plays a crucial role in Epstein-Barr virus (EBV)s ability to establish latency and is involved in the development and progression of EBV-associated cancers. Additionally, EBV-infected cells affect the immune responses, making it challenging for immune system to eliminate. Due to the aforementioned reasons, it is important to understand the structural features of LMP1 which is essential for the development of novel cancer therapies that target its signaling pathways. To date, there is no complete LMP1 protein structure therefore in our work, we modeled the full-length LMP1 containing the short cytoplasmic N-terminus, six transmembrane domains and a long-simulated C-terminus. Our model showed good stability and protein compactness evaluated through accelerated-Molecular Dynamics where the conformational ensemble exhibited compact folds, particularly in the transmembrane domains. Our results suggest that specific domains or motifs, predominantly in the C-terminus domain of LMP1 show promise as potential drug targets. As a whole, our work provides insights on key strucutral features of LMP1 that will allow the development of novel LMP1 therapies.

biochemistry↗

Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naive or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm [Formula] and a boosted K* algorithm [Formula] to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.

pharmacology and toxicology↗