Structure prediction of eukaryotic elongation factor-2 kinase and identification of the binding mechanisms of its inhibitors: homology modeling, molecular docking, and molecular dynamics simulation

Computational drug repurposing study of the RNA binding domain of SARS-CoV-2 nucleocapsid protein with antiviral agents

The recent outbreak of coronavirus disease (COVID‐19) in China caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has led to worldwide human infections and deaths. The nucleocapsid (N) protein of coronaviruses (CoVs) is a multifunctional RNA binding protein necessary for viral RNA replication and transcription. Therefore, it is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. This study addresses the potential to repurpose antiviral compounds approved or in development for treating human CoV induced infections against SARS‐CoV‐2 N. For this purpose, we used the docking methodology to better understand the inhibitory mechanism of this protein with the existing 34 antiviral compounds. The results of this analysis indicate that rapamycin, saracatinib, camostat, trametinib, and nafamostat were the top hit compounds with binding energy (−11.87, −10.40, −9.85, −9.45, −9.35 kcal/mol, respectively). This analysis also showed that the most common residues that interact with the compounds are Phe66, Arg68, Gly69, Tyr123, Ile131, Trp132, Val133, and Ala134. Subsequently, protein‐ligand complex stability was examined with molecular dynamics simulations for these five compounds, which showed the best binding affinity. According to the results of this study, the interaction between these compounds and crucial residues of the target protein were maintained. These results suggest that these residues are potential drug targeting sites for the SARS‐CoV‐2 N protein. This study information will contribute to the development of novel compounds for further in vitro and in vivo studies of SARS‐CoV‐2, as well as possible new drug repurposing strategies to treat COVID‐19 disease.

Fluorescence chemosensing of meldonium using a cross-reactive sensor array

Differential sensing of meldonium was achieved using fluorescent chemosensors.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.