Identifying the natural compound Catechin from tropical mangrove plants as a potential lead candidate against 3CLpro from SARS-CoV-2: An integrated in silico approach

Exploration of potential hit compounds targeting 1-deoxy-d-xylulose 5-phosphate reductoisomerase (IspC) from Acinetobacter baumannii: an in silico investigation

The emergence of carbapenem-resistant Acinetobacter baumannii, a highly concerning bacterial species designated as a Priority 1: Critical pathogen by the WHO, has become a formidable global threat. In this study, we utilised computational methods to explore the potent molecules capable of inhibiting the IspC enzyme, which plays a crucial role in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. Employing high-throughput virtual screening of small molecules from the Enamine library, we focused on the highly conserved substrate binding site of the DXR target protein, resulting in the identification of 1000 potential compounds. Among these compounds, we selected the top two candidates (Z2615855584 and Z2206320703) based on Lipinski's rule of Five and ADMET filters, along with FR900098, a known IspC inhibitor, and DXP, the substrate of IspC, for molecular dynamics (MD) simulations. The MD simulation trajectories revealed remarkable structural and thermodynamic stability, as well as strong binding affinity, for all the IspC-ligand complexes. Furthermore, binding free energy calculations based on MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) methodology demonstrated significant interactions between the selected ligand molecules and IspC. Taking into consideration all the aforementioned criteria, we suggest Z2206320703 as the potent lead candidate against IspC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03923-w.

Identification of active compounds of traditional chinese medicine derived from maxing shigan decoction for COVID-19 treatment: a meta-analysis and in silico study

BACKGROUND

Coronavirus 2019 (COVID-19) poses a serious threat to human health. In China, traditional Chinese medicine (TCM), mainly based on the Maxing Shigan decoction (MXSGD), is used in conjunction with western medicine to treat COVID-19.

RESEARCH DESIGN AND METHODS

We conducted a network meta-analysis to investigate whether MXSGD-related TCM combined with western medicine is more effective in treating COVID-19 compared to western medicine alone. Additionally, using network pharmacology, cross-docking, and molecular dynamics (MD) simulation to explore the potential active compounds and possible targets underlying the therapeutic effects of MXSGD-related TCM.

RESULTS

MXSGD-related TCM combined with western medicine was better for treating COVID-19 compared to western medicine alone. Network pharmacological analysis identified 43 shared ingredients in the MXSGD-related TCM prescriptions and 599 common target genes. Cross-docking of the 43 compounds with 154 proteins that matched these genes led to the identification of 60 proteins. Pathway profiling revealed that the active ingredients participated in multiple signaling pathways that contribute to their efficacy. Molecular docking and MD simulation demonstrated that MOL007214, the most promising molecule, could stably bind to the active site of SARS-CoV-2 3CLpro.

CONCLUSION

This study demonstrates the important role of MXSGD-related TCM in the treatment of COVID-19.

Structural and functional characterization of 1-deoxy-D-xylulose-5-phosphate synthase (DXS) from Acinetobacter baumannii: identification of promising lead molecules from virtual screening, molecular docking and molecular dynamics simulations

The advent of multi drug resistance and extensive-drug resistance among various pathogens has caused a rise in nosocomial infections, which in turn has led to rising hospital-acquired infection-related mortality rates. Amongst them, carbapenem-resistant Acinetobacter baumannii is one of the most notorious bacterial species, categorized as a Priority 1: Critical pathogen by the WHO. Therefore, the discovery and development of novel antibiotics, as well as the identification of potential inhibitors, have become the need of the hour. In this study, we have employed computational methods to explore and identify molecules capable of inhibiting enzymes essential in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. The high throughput virtual screening of small molecules (Enamine Advanced Collection (AC) library) against the highly conserved substrate-binding site of the DXS target protein provided us with a total of 1000 molecules. The top four potential candidate molecules, namely-Z3353989070, Z3353989049, Z2295848528, and Z1685501455, alongside fluoropyruvate (control), a known inhibitor of DXS, was chosen for a molecular dynamic simulation study. The molecular dynamic simulation trajectories suggested high structural and thermodynamical stability and strong binding affinity of all the DXS-ligand complexes. Moreover, the MM/PBSA-based binding free energy calculations also exhibited strong interactions of the selected ligand molecules with DXS. In conclusion, we have found that all four molecules displayed better results and stronger binding affinity than the control. In the end, based on all the above-mentioned criteria, we have proposed Z3353989049 to be the promising lead candidate against DXS from A. baumannii.Communicated by Ramaswamy H. Sarma.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.