In silico modelling and virtual screening for identification of inhibitors for spore wall protein-5 in Nosema bombycis

Exploring caffeine as a disruptor of membrane integrity and genomic stability in Staphylococcus aureus: functional and in silico analysis

To explore the mechanistic underpinnings of caffeine as a potent antibacterial against Staphylococcus aureus ATCC 25923 via in vitro functional assays, whole-genome sequencing, and in silico docking studies. In vitro studies established that caffeine's minimum inhibitory concentration (MIC) against S. aureus ATCC 25923 is 0.01544 mmol/mL. Functional assays along with Scanning Electron Microscopy confirmed that caffeine at 0.030089 mmol/mL (2MIC) released nucleotide constituents (nucleotide leakage assay) and effluxed potassium ions (potassium efflux assay) thereby, further validating caffeine's role as a membrane-active antimicrobial agent. Whole genome sequencing of control versus caffeine treated samples revealed a significant drop in read mapping percentage from 99.96 to 23.68% and GC content from 30.69 to 6.93%. This massive reduction in the treated sample was a consequence of single nucleotide polymorphisms (SNPs, 50,303), along with insertions and deletions (InDels, 62). Several of these caffeine-induced mutations were found to be harbouring the coding regions of genes involved in processes such as cell membrane organization, bacterial virulence, and DNA repair processes. Thus, implying a caffeine-mediated genomic rearrangement and instability. In silico docking studies revealed a strong binding affinity of caffeine to key cell wall proteins ltaA (-6.9 kcal/mol) and ltaS (-6.5 kcal/mol) respectively. The dynamic simulation studies revealed caffeine's interaction with receptor ltaS remained stable, with low deviations and minimal fluctuations. Although caffeine has been widely investigated for its antibacterial properties, its specific mechanisms of action, notably its effects on the cell membrane and genomic integrity in S. aureus ATCC 25923, are little understood. This study thus offers a comprehensive functional genomic analysis of caffeine as an antibacterial against S. aureus.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.