Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.

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.

In silico Mutagenesis and Modeling of Decoy Peptides Targeting CIB1 to Obscure its Role in Triple-negative Breast Cancer Progression.

BACKGROUND: Cancer is recognized globally as the second-most dominating and leading cause of morbidities. Breast cancer is the most often diagnosed disease in women and one of the leading causes of cancer mortality. In women, 287,850, and in males, 2710 cases were reported in 2022. Approximately 10-20% of all new cases of breast cancer diagnosed in the United States in 2017 were triple-negative breast cancers (TNBCs), which lack the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). OBJECTIVE: This study aims to adopt different strategies for targeting calcium integrin-binding protein 1 by computer- aided drug design methods. Our results showed that the top four selected peptides interact with CIB1 more strongly than the reference peptide and restore normal cell function by engaging CIB1. Our binding affinity analyses explore an innovative approach to planning a new peptide to inhibit triple-negative breast cancer. METHODS: Molecular dynamic simulation of the CIB1-UNC10245092 interaction highlights the potential peptide inhibitors through in-silico mutagenesis and designs novel peptide inhibitors from the reference peptide (UNC10245092) through residue scan methodology. RESULTS: The top four designed peptides (based on binding free energy) were subjected to molecular dynamics simulations using AMBER to evaluate stability. CONCLUSION: Our results indicate that among the top five selected peptides, the mutant 2nd mutants have more potential to inhibit CIB1 than the reference peptide (UNC10245092) and have the potency to prevent or restore the tumor suppressor function of UNC10245092.

In Silico Identification of Lead Compounds for Pseudomonas Aeruginosa PqsA Enzyme: Computational Study to Block Biofilm Formation.

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.

Biocatalytic synthesis of chiral five-membered carbasugars intermediates utilizing CV2025 ω-transaminase from Chromobacterium violaceum.

(S)-4-(Hydroxymethyl)cyclopent-2-enone is a key intermediate in the synthesis of chiral five-membered carbasugars, which can be used to synthesize a large number of pharmacologically relevant carbocyclic nucleosides. Herein, CV2025 ω-transaminase from Chromobacterium violaceum was selected based on substrate similarity to convert ((1S,4R)-4-aminocyclopent-2-enyl)methanol to (S)-4-(hydroxymethyl)cyclopent-2-enone. The enzyme was successfully cloned, expressed in Escherichia coli, purified and characterized. We show that it has R configuration preference in contrast with the conventional S preference. The highest activity was obtained below 60 °C and at pH 7.5. Cations Ca2+ and K+ enhanced activity by 21% and 13%, respectively. The conversion rate reached 72.4% within 60 min at 50 °C, pH 7.5, using 0.5 mM pyridoxal-5'-phosphate, 0.6 µM CV2025, and 10 mM substrate. The present study provides a promising strategy for preparing five-membered carbasugars economically and efficiently.

Design of a novel multiple epitope-based vaccine: an immunoinformatics approach to combat monkeypox.

Monkeypox virus is an infectious agent that causes fever, Pneumonitis encephalitis, rash, lymphadenopathy and bacterial infection. The current outbreak of monkeypox has reawakened the global health concern. In the current situation of increasing viral infection, no vaccine or drug is available for monkeypox. Thus, there is an urgent need for viable vaccine development to prevent viral transmission by boosting human immunity. Herein, using immunoinformatics approaches, a multi-epitope vaccine was constructed for the Monkeypox virus. In this connection, B-Cell and T-cell epitopes were identified and joined with the help of adjutants and linkers. The vaccine construct was selected based on promising vaccine candidates and immunogenic potential. Further epitopes were selected based on antigenicity score, non-allergenicity and good immunological properties. Molecular docking reveals strong interactions between TLR-9 and the predicted vaccine construct. Finally, molecular dynamics simulations were performed to evaluate the stability and compactness of the constructed vaccine. The MD simulation results demonstrated the significant stability of the polypeptide vaccine construct. The predicted vaccine represented good stability, expression, immunostimulatory capabilities and significant solubility. Design vaccine was verified as efficient in different computer-based immune response investigations. Additionally, the constructed vaccine also represents a good population coverage in computer base analysis.Communicated by Ramaswamy H. Sarma.