RESUMEN

The synthesis of carbocyclic-ddA, a potent antiviral agent against hepatitis B, relies significantly on (1R,3R)-3-hydroxycyclopentanemethanol as a key intermediate. To effectively produce this intermediate, our study employed a chemoenzymatic approach. The selection of appropriate biocatalysts was based on substrate similarity, leading us to adopt the CrS enoate reductase derived from Thermus scotoductus SA-01. Additionally, we developed an enzymatic system for NADH regeneration, utilising formate dehydrogenase from Candida boidinii. This system facilitated the efficient catalysis of (S)-4-(hydroxymethyl)cyclopent-2-enone, resulting in the formation of (3R)-3-(hydroxymethyl) cyclopentanone. Furthermore, we successfully cloned, expressed, purified, and characterized the CrS enzyme in Escherichia coli. Optimal reaction conditions were determined, revealing that the highest activity occurred at 45 °C and pH 8.0. By employing 5 mM (S)-4-(hydroxymethyl)cyclopent-2-enone, 0.05 mM FMN, 0.2 mM NADH, 10 µM CrS, 40 µM formic acid dehydrogenase, and 40 mM sodium formate, complete conversion was achieved within 45 min at 35 °C and pH 7.0. Subsequently, (1R,3R)-3-hydroxycyclopentanemethanol was obtained through a simple three-step chemical conversion process. This study not only presents an effective method for synthesizing the crucial intermediate but also highlights the importance of biocatalysts and enzymatic systems in chemoenzymatic synthesis approaches.

Asunto(s)

Ciclopentanos , Escherichia coli , Ciclopentanos/metabolismo , Escherichia coli/metabolismo , Escherichia coli/genética , Candida/enzimología , Formiato Deshidrogenasas/metabolismo , Formiato Deshidrogenasas/genética , Antivirales/metabolismo , Antivirales/síntesis química , NAD/metabolismo , Biocatálisis , Oxidorreductasas/metabolismo , Clonación Molecular

RESUMEN

The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the body, including the brain and spine. Developing a single drug can take several decades, making drug discovery costly and time-consuming. Machine learning algorithms like support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF) and Gaussian naive base (GNB) are fast and effective and are commonly used in drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. For the training of the models, a dataset of 307 was downloaded from BindingDB. Among 307 compounds, 85 compounds were labeled as active, having an IC50 below 58 mM, while 222 compounds were labeled inactive against thymidylate kinase, with 87.2% accuracy. The developed models were subjected to an external ZINC dataset of 136,564 compounds. Furthermore, we performed the 100-ns dynamic simulation and post trajectories analysis of compounds having good interaction and score in molecular docking. As compared to the standard reference compound, the top three hits revealed greater stability and compactness. In conclusion, our predicted hits can inhibit thymidylate kinase overexpression to combat Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.

RESUMEN

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Pseudomonas aeruginosa infections are frequently associated with the development of biofilms, which give the bacteria additional drug resistance and increase their virulence. The goal of this study was to find strong compounds that block the Anthranilate-CoA ligase enzyme made by the pqsA gene. This would stop the P. aeruginosa quorum signaling system. This enzyme plays a crucial role in the pathogenicity of P. aeruginosa by producing autoinducers for cell-to-cell communication that lead to the production of biofilms. Pharmacophore-based virtual screening was carried out utilizing a library of commercially accessible enzyme inhibitors. The most promising hits obtained during virtual screening were put through molecular docking with the help of MOE. The virtual screening yielded 7/160 and 10/249 hits (ZINC and Chembridge). Finally, 2/7 ZINC hits and 2/10 ChemBridge hits were selected as potent lead compounds employing diverse scaffolds due to their high pqsA enzyme binding affinity. The results of the pharmacophore-based virtual screening were subsequently verified using a molecular dynamic simulation-based study (MDS). Using MDS and post-MDS, the stability of the complexes was evaluated. The most promising lead compounds exhibited a high binding affinity towards protein-binding pocket and interacted with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.