Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.

In Silico Discovery of Potential Azole-Containing mPGES-1 Inhibitors By Virtual Screening, Pharmacophore Modeling and Molecular Dynamics Simulations

Inhibition of microsomal prostaglandin E2 synthase-1 (mPGES-1) is promising for designing novel nonsteroidal anti-inflammatory drugs, as they lack side-effects associated with inhibition of cyclooxygenase enzymes. Azole compounds are nitrogen-containing heterocycles and have a wide use in medicine and are considered as promising compounds in medicinal chemistry. Various computer-aided drug design strategies are incorporated in this study. Structure-based virtual screening was performed employing various docking programs. Receiver Operator Characteristic (ROC) curves were used to evaluate the selectivity of each program. Furthermore, scoring power of Autodock4 and Autodock Vina was assessed by Pearson’s correlation coefficients. Pharmacophore models were generated and Güner-Henry score of the best model was calculated as 0.89. Binding modes of the final 10 azole compounds were analyzed and further investigation of the best binding (-8.38 kcal/mol) compound was performed using molecular dynamics simulation, revealing that furazan1224 (ZINC001142847306) occupied the binding site of the substrate, prostaglandin H2 (PGH2) and remained stable for 100 ns. Continuous hydrogen bonds with amino acids in the active site supported the stability of furazan1224 throughout the trajectory. Pharmacokinetic profile showed that furazan1224 lacks the risks of inhibiting cytochrome P450 3A4 enzyme and central nervous system-related side-effects.

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.

In Silico-Based Discovery of Natural Anthraquinones with Potential against Multidrug-Resistant E. coli

E. coli is a Gram-negative bacterium that causes different human infections. Additionally, it resists common antibiotics due to its outer protective membrane. Natural products have been proven to be efficient antibiotics. However, plant natural products are far less explored in this regard. Accordingly, over 16,000 structures covering almost all African medicinal plants in AfroDb in a structural-based virtual screening were used to find efficient anti-E. coli candidates. These drug-like structures were docked into the active sites of two important molecular targets (i.e., E. coli’s Ddl-B and Gyr-B). The top-scoring hits (i.e., got docking scores < −10 kcal/mol) produced in the initial virtual screening (0.15% of the database structures for Ddl-B and 0.17% of the database structures for Gyr-B in the database) were further refined using molecular dynamic simulation-based binding free energy (ΔG) calculation. Anthraquinones were found to prevail among the retrieved hits. Accordingly, readily available anthraquinone derivatives (10 hits) were selected, prepared, and tested in vitro against Ddl-B, Gyr-B, multidrug-resistant (MDR) E. coli, MRSA, and VRSA. A number of the tested derivatives demonstrated strong micromolar enzyme inhibition and antibacterial activity against E. coli, MRSA, and VRSA, with MIC values ranging from 2 to 64 µg/mL. Moreover, both E. coli’s Ddl-B and Gyr-B were inhibited by emodin and chrysophanol with IC50 values comparable to the reference inhibitors (IC50 = 216 ± 5.6, 236 ± 8.9 and 0.81 ± 0.3, 1.5 ± 0.5 µM for Ddl-B and Gyr-B, respectively). All of the active antibacterial anthraquinone hits showed low to moderate cellular cytotoxicity (CC50 > 50 µM) against human normal fibroblasts (WI-38). Furthermore, molecular dynamic simulation (MDS) experiments were carried out to reveal the binding modes of these inhibitors inside the active site of each enzyme. The findings presented in this study are regarded as a significant step toward developing novel antibacterial agents against MDR strains.

Combinatorial Virtual Screening Revealed a Novel Scaffold for TNKS Inhibition to Combat Colorectal Cancer

The abnormal Wnt signaling pathway leads to a high expression of β-catenin, which causes several types of cancer, particularly colorectal cancer (CRC). The inhibition of tankyrase (TNKS) activity can reduce cancer cell growth, invasion, and resistance to treatment by blocking the Wnt signaling pathway. A pharmacophore search and pharmacophore docking were performed to identify potential TNKS inhibitors in the training databases. The weighted MM/PBSA binding free energy of the docking model was calculated to rank the databases. The reranked results indicated that 26.98% of TNKS inhibitors that were present in the top 5% of compounds in the database and near an ideal value ranked 28.57%. The National Cancer Institute database was selected for formal virtual screening, and 11 potential TNKS inhibitors were identified. An enzyme-based experiment was performed to demonstrate that of the 11 potential TNKS inhibitors, NSC295092 and NSC319963 had the most potential. Finally, Wnt pathway analysis was performed through a cell-based assay, which indicated that NSC319963 is the most likely TNKS inhibitor (pIC50 = 5.59). The antiproliferation assay demonstrated that NSC319963 can decrease colorectal cancer cell growth; therefore, the proposed method successfully identified a novel TNKS inhibitor that can alleviate CRC.